Sample records for scientific computing problems
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Computing through Scientific Abstractions in SysBioPS
DOE Office of Scientific and Technical Information (OSTI.GOV)
Chin, George; Stephan, Eric G.; Gracio, Deborah K.
2004-10-13
Today, biologists and bioinformaticists have a tremendous amount of computational power at their disposal. With the availability of supercomputers, burgeoning scientific databases and digital libraries such as GenBank and PubMed, and pervasive computational environments such as the Grid, biologists have access to a wealth of computational capabilities and scientific data at hand. Yet, the rapid development of computational technologies has far exceeded the typical biologist’s ability to effectively apply the technology in their research. Computational sciences research and development efforts such as the Biology Workbench, BioSPICE (Biological Simulation Program for Intra-Cellular Evaluation), and BioCoRE (Biological Collaborative Research Environment) are importantmore » in connecting biologists and their scientific problems to computational infrastructures. On the Computational Cell Environment and Heuristic Entity-Relationship Building Environment projects at the Pacific Northwest National Laboratory, we are jointly developing a new breed of scientific problem solving environment called SysBioPSE that will allow biologists to access and apply computational resources in the scientific research context. In contrast to other computational science environments, SysBioPSE operates as an abstraction layer above a computational infrastructure. The goal of SysBioPSE is to allow biologists to apply computational resources in the context of the scientific problems they are addressing and the scientific perspectives from which they conduct their research. More specifically, SysBioPSE allows biologists to capture and represent scientific concepts and theories and experimental processes, and to link these views to scientific applications, data repositories, and computer systems.« less
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NASA Technical Reports Server (NTRS)
Denning, Peter J.; Tichy, Walter F.
1990-01-01
Highly parallel computing architectures are the only means to achieve the computation rates demanded by advanced scientific problems. A decade of research has demonstrated the feasibility of such machines and current research focuses on which architectures designated as multiple instruction multiple datastream (MIMD) and single instruction multiple datastream (SIMD) have produced the best results to date; neither shows a decisive advantage for most near-homogeneous scientific problems. For scientific problems with many dissimilar parts, more speculative architectures such as neural networks or data flow may be needed.
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Computational Science in Armenia (Invited Talk)
NASA Astrophysics Data System (ADS)
Marandjian, H.; Shoukourian, Yu.
This survey is devoted to the development of informatics and computer science in Armenia. The results in theoretical computer science (algebraic models, solutions to systems of general form recursive equations, the methods of coding theory, pattern recognition and image processing), constitute the theoretical basis for developing problem-solving-oriented environments. As examples can be mentioned: a synthesizer of optimized distributed recursive programs, software tools for cluster-oriented implementations of two-dimensional cellular automata, a grid-aware web interface with advanced service trading for linear algebra calculations. In the direction of solving scientific problems that require high-performance computing resources, examples of completed projects include the field of physics (parallel computing of complex quantum systems), astrophysics (Armenian virtual laboratory), biology (molecular dynamics study of human red blood cell membrane), meteorology (implementing and evaluating the Weather Research and Forecast Model for the territory of Armenia). The overview also notes that the Institute for Informatics and Automation Problems of the National Academy of Sciences of Armenia has established a scientific and educational infrastructure, uniting computing clusters of scientific and educational institutions of the country and provides the scientific community with access to local and international computational resources, that is a strong support for computational science in Armenia.
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ERIC Educational Resources Information Center
Armoni, Michal; Gal-Ezer, Judith
2005-01-01
When dealing with a complex problem, solving it by reduction to simpler problems, or problems for which the solution is already known, is a common method in mathematics and other scientific disciplines, as in computer science and, specifically, in the field of computability. However, when teaching computational models (as part of computability)…
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Joint the Center for Applied Scientific Computing
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gamblin, Todd; Bremer, Timo; Van Essen, Brian
The Center for Applied Scientific Computing serves as Livermore Lab’s window to the broader computer science, computational physics, applied mathematics, and data science research communities. In collaboration with academic, industrial, and other government laboratory partners, we conduct world-class scientific research and development on problems critical to national security. CASC applies the power of high-performance computing and the efficiency of modern computational methods to the realms of stockpile stewardship, cyber and energy security, and knowledge discovery for intelligence applications.
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An Ethnomethodological Perspective on How Middle School Students Addressed a Water Quality Problem
ERIC Educational Resources Information Center
Belland, Brian R.; Gu, Jiangyue; Kim, Nam Ju; Turner, David J.
2016-01-01
Science educators increasingly call for students to address authentic scientific problems in science class. One form of authentic science problem--socioscientific issue--requires that students engage in complex reasoning by considering both scientific and social implications of problems. Computer-based scaffolding can support this process by…
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Hypergraph-Based Combinatorial Optimization of Matrix-Vector Multiplication
ERIC Educational Resources Information Center
Wolf, Michael Maclean
2009-01-01
Combinatorial scientific computing plays an important enabling role in computational science, particularly in high performance scientific computing. In this thesis, we will describe our work on optimizing matrix-vector multiplication using combinatorial techniques. Our research has focused on two different problems in combinatorial scientific…
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ERIC Educational Resources Information Center
Gegner, Julie A.; Mackay, Donald H. J.; Mayer, Richard E.
2009-01-01
High school students can access original scientific research articles on the Internet, but may have trouble understanding them. To address this problem of online literacy, the authors developed a computer-based prototype for guiding students' comprehension of scientific articles. High school students were asked to read an original scientific…
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Toward Theory-Based Instruction in Scientific Problem Solving.
ERIC Educational Resources Information Center
Heller, Joan I.; And Others
Several empirical and theoretical analyses related to scientific problem-solving are reviewed, including: detailed studies of individuals at different levels of expertise, and computer models simulating some aspects of human information processing during problem solving. Analysis of these studies has revealed many facets about the nature of the…
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Whole earth modeling: developing and disseminating scientific software for computational geophysics.
NASA Astrophysics Data System (ADS)
Kellogg, L. H.
2016-12-01
Historically, a great deal of specialized scientific software for modeling and data analysis has been developed by individual researchers or small groups of scientists working on their own specific research problems. As the magnitude of available data and computer power has increased, so has the complexity of scientific problems addressed by computational methods, creating both a need to sustain existing scientific software, and expand its development to take advantage of new algorithms, new software approaches, and new computational hardware. To that end, communities like the Computational Infrastructure for Geodynamics (CIG) have been established to support the use of best practices in scientific computing for solid earth geophysics research and teaching. Working as a scientific community enables computational geophysicists to take advantage of technological developments, improve the accuracy and performance of software, build on prior software development, and collaborate more readily. The CIG community, and others, have adopted an open-source development model, in which code is developed and disseminated by the community in an open fashion, using version control and software repositories like Git. One emerging issue is how to adequately identify and credit the intellectual contributions involved in creating open source scientific software. The traditional method of disseminating scientific ideas, peer reviewed publication, was not designed for review or crediting scientific software, although emerging publication strategies such software journals are attempting to address the need. We are piloting an integrated approach in which authors are identified and credited as scientific software is developed and run. Successful software citation requires integration with the scholarly publication and indexing mechanisms as well, to assign credit, ensure discoverability, and provide provenance for software.
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I/O-Efficient Scientific Computation Using TPIE
NASA Technical Reports Server (NTRS)
Vengroff, Darren Erik; Vitter, Jeffrey Scott
1996-01-01
In recent years, input/output (I/O)-efficient algorithms for a wide variety of problems have appeared in the literature. However, systems specifically designed to assist programmers in implementing such algorithms have remained scarce. TPIE is a system designed to support I/O-efficient paradigms for problems from a variety of domains, including computational geometry, graph algorithms, and scientific computation. The TPIE interface frees programmers from having to deal not only with explicit read and write calls, but also the complex memory management that must be performed for I/O-efficient computation. In this paper we discuss applications of TPIE to problems in scientific computation. We discuss algorithmic issues underlying the design and implementation of the relevant components of TPIE and present performance results of programs written to solve a series of benchmark problems using our current TPIE prototype. Some of the benchmarks we present are based on the NAS parallel benchmarks while others are of our own creation. We demonstrate that the central processing unit (CPU) overhead required to manage I/O is small and that even with just a single disk, the I/O overhead of I/O-efficient computation ranges from negligible to the same order of magnitude as CPU time. We conjecture that if we use a number of disks in parallel this overhead can be all but eliminated.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sanders, W.M.; Campbell, C.L.; Pickerill, P.A.
1980-10-01
The Los Alamos Scientific Laboratory is funded by the US Department of Agriculture to apply scientific and computer technology to solve agricultural problems. This report summarizes work during the period October 1, 1978 through September 30, 1979 on the application of computer technology to four areas: (1) Texas brucellosis calfhood-vaccination studies, (2) brucellosis data-entry system in New Mexico, (3) Idaho adult vaccination data base, and (4) surveillance of slaughterplants in Texas.
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Predicting protein structures with a multiplayer online game.
Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit
2010-08-05
People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.
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Educational NASA Computational and Scientific Studies (enCOMPASS)
NASA Technical Reports Server (NTRS)
Memarsadeghi, Nargess
2013-01-01
Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.
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Commentary: Crowdsourcing, Foldit, and Scientific Discovery Games
ERIC Educational Resources Information Center
Parslow, Graham R.
2013-01-01
The web has created new possibilities for collaboration that fit under the terms crowdsourcing and human-based computation. Crowdsourcing applies when a task or problem is outsourced to an undefined public rather than a specific body. Human-based computation refers to ways that humans and computers can work together to solve problems. These two…
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An Overview of the Computational Physics and Methods Group at Los Alamos National Laboratory
DOE Office of Scientific and Technical Information (OSTI.GOV)
Baker, Randal Scott
CCS Division was formed to strengthen the visibility and impact of computer science and computational physics research on strategic directions for the Laboratory. Both computer science and computational science are now central to scientific discovery and innovation. They have become indispensable tools for all other scientific missions at the Laboratory. CCS Division forms a bridge between external partners and Laboratory programs, bringing new ideas and technologies to bear on today’s important problems and attracting high-quality technical staff members to the Laboratory. The Computational Physics and Methods Group CCS-2 conducts methods research and develops scientific software aimed at the latest andmore » emerging HPC systems.« less
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[Earth Science Technology Office's Computational Technologies Project
NASA Technical Reports Server (NTRS)
Fischer, James (Technical Monitor); Merkey, Phillip
2005-01-01
This grant supported the effort to characterize the problem domain of the Earth Science Technology Office's Computational Technologies Project, to engage the Beowulf Cluster Computing Community as well as the High Performance Computing Research Community so that we can predict the applicability of said technologies to the scientific community represented by the CT project and formulate long term strategies to provide the computational resources necessary to attain the anticipated scientific objectives of the CT project. Specifically, the goal of the evaluation effort is to use the information gathered over the course of the Round-3 investigations to quantify the trends in scientific expectations, the algorithmic requirements and capabilities of high-performance computers to satisfy this anticipated need.
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NASA Technical Reports Server (NTRS)
Oliger, Joseph
1993-01-01
The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing, Advanced Methods for Scientific Computing, High Performance Networks and Technology, and Learning Systems. Parallel compiler techniques, adaptive numerical methods for flows in complicated geometries, and optimization were identified as important problems to investigate for ARC's involvement in the Computational Grand Challenges of the next decade.
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Some Thoughts Regarding Practical Quantum Computing
NASA Astrophysics Data System (ADS)
Ghoshal, Debabrata; Gomez, Richard; Lanzagorta, Marco; Uhlmann, Jeffrey
2006-03-01
Quantum computing has become an important area of research in computer science because of its potential to provide more efficient algorithmic solutions to certain problems than are possible with classical computing. The ability of performing parallel operations over an exponentially large computational space has proved to be the main advantage of the quantum computing model. In this regard, we are particularly interested in the potential applications of quantum computers to enhance real software systems of interest to the defense, industrial, scientific and financial communities. However, while much has been written in popular and scientific literature about the benefits of the quantum computational model, several of the problems associated to the practical implementation of real-life complex software systems in quantum computers are often ignored. In this presentation we will argue that practical quantum computation is not as straightforward as commonly advertised, even if the technological problems associated to the manufacturing and engineering of large-scale quantum registers were solved overnight. We will discuss some of the frequently overlooked difficulties that plague quantum computing in the areas of memories, I/O, addressing schemes, compilers, oracles, approximate information copying, logical debugging, error correction and fault-tolerant computing protocols.
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Scaffolding Argumentation about Water Quality: A Mixed-Method Study in a Rural Middle School
ERIC Educational Resources Information Center
Belland, Brian R.; Gu, Jiangyue; Armbrust, Sara; Cook, Brant
2015-01-01
A common way for students to develop scientific argumentation abilities is through argumentation about socioscientific issues, defined as scientific problems with social, ethical, and moral aspects. Computer-based scaffolding can support students in this process. In this mixed method study, we examined the use and impact of computer based…
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A toolbox and record for scientific models
NASA Technical Reports Server (NTRS)
Ellman, Thomas
1994-01-01
Computational science presents a host of challenges for the field of knowledge-based software design. Scientific computation models are difficult to construct. Models constructed by one scientist are easily misapplied by other scientists to problems for which they are not well-suited. Finally, models constructed by one scientist are difficult for others to modify or extend to handle new types of problems. Construction of scientific models actually involves much more than the mechanics of building a single computational model. In the course of developing a model, a scientist will often test a candidate model against experimental data or against a priori expectations. Test results often lead to revisions of the model and a consequent need for additional testing. During a single model development session, a scientist typically examines a whole series of alternative models, each using different simplifying assumptions or modeling techniques. A useful scientific software design tool must support these aspects of the model development process as well. In particular, it should propose and carry out tests of candidate models. It should analyze test results and identify models and parts of models that must be changed. It should determine what types of changes can potentially cure a given negative test result. It should organize candidate models, test data, and test results into a coherent record of the development process. Finally, it should exploit the development record for two purposes: (1) automatically determining the applicability of a scientific model to a given problem; (2) supporting revision of a scientific model to handle a new type of problem. Existing knowledge-based software design tools must be extended in order to provide these facilities.
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The Petascale Data Storage Institute
DOE Office of Scientific and Technical Information (OSTI.GOV)
Gibson, Garth; Long, Darrell; Honeyman, Peter
2013-07-01
Petascale computing infrastructures for scientific discovery make petascale demands on information storage capacity, performance, concurrency, reliability, availability, and manageability.The Petascale Data Storage Institute focuses on the data storage problems found in petascale scientific computing environments, with special attention to community issues such as interoperability, community buy-in, and shared tools.The Petascale Data Storage Institute is a collaboration between researchers at Carnegie Mellon University, National Energy Research Scientific Computing Center, Pacific Northwest National Laboratory, Oak Ridge National Laboratory, Sandia National Laboratory, Los Alamos National Laboratory, University of Michigan, and the University of California at Santa Cruz.
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ERIC Educational Resources Information Center
Stredney, Donald Larry
An overview of computer animation and the techniques involved in its creation is provided in the introduction to this masters thesis, which focuses on the problems encountered by students in learning the forms and functions of complex anatomical structures and ways in which computer animation can address these problems. The objectives for,…
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SciCADE 95: International conference on scientific computation and differential equations
DOE Office of Scientific and Technical Information (OSTI.GOV)
NONE
1995-12-31
This report consists of abstracts from the conference. Topics include algorithms, computer codes, and numerical solutions for differential equations. Linear and nonlinear as well as boundary-value and initial-value problems are covered. Various applications of these problems are also included.
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Parallel processing for scientific computations
NASA Technical Reports Server (NTRS)
Alkhatib, Hasan S.
1991-01-01
The main contribution of the effort in the last two years is the introduction of the MOPPS system. After doing extensive literature search, we introduced the system which is described next. MOPPS employs a new solution to the problem of managing programs which solve scientific and engineering applications on a distributed processing environment. Autonomous computers cooperate efficiently in solving large scientific problems with this solution. MOPPS has the advantage of not assuming the presence of any particular network topology or configuration, computer architecture, or operating system. It imposes little overhead on network and processor resources while efficiently managing programs concurrently. The core of MOPPS is an intelligent program manager that builds a knowledge base of the execution performance of the parallel programs it is managing under various conditions. The manager applies this knowledge to improve the performance of future runs. The program manager learns from experience.
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[Earth and Space Sciences Project Services for NASA HPCC
NASA Technical Reports Server (NTRS)
Merkey, Phillip
2002-01-01
This grant supported the effort to characterize the problem domain of the Earth Science Technology Office's Computational Technologies Project, to engage the Beowulf Cluster Computing Community as well as the High Performance Computing Research Community so that we can predict the applicability of said technologies to the scientific community represented by the CT project and formulate long term strategies to provide the computational resources necessary to attain the anticipated scientific objectives of the CT project. Specifically, the goal of the evaluation effort is to use the information gathered over the course of the Round-3 investigations to quantify the trends in scientific expectations, the algorithmic requirements and capabilities of high-performance computers to satisfy this anticipated need.
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Benchmark problems and solutions
NASA Technical Reports Server (NTRS)
Tam, Christopher K. W.
1995-01-01
The scientific committee, after careful consideration, adopted six categories of benchmark problems for the workshop. These problems do not cover all the important computational issues relevant to Computational Aeroacoustics (CAA). The deciding factor to limit the number of categories to six was the amount of effort needed to solve these problems. For reference purpose, the benchmark problems are provided here. They are followed by the exact or approximate analytical solutions. At present, an exact solution for the Category 6 problem is not available.
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ERIC Educational Resources Information Center
Kunsting, Josef; Wirth, Joachim; Paas, Fred
2011-01-01
Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the "effects of goal specificity" (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on "strategy use" and "instructional efficiency". Our empirical findings close an important research gap,…
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Computational Science: A Research Methodology for the 21st Century
NASA Astrophysics Data System (ADS)
Orbach, Raymond L.
2004-03-01
Computational simulation - a means of scientific discovery that employs computer systems to simulate a physical system according to laws derived from theory and experiment - has attained peer status with theory and experiment. Important advances in basic science are accomplished by a new "sociology" for ultrascale scientific computing capability (USSCC), a fusion of sustained advances in scientific models, mathematical algorithms, computer architecture, and scientific software engineering. Expansion of current capabilities by factors of 100 - 1000 open up new vistas for scientific discovery: long term climatic variability and change, macroscopic material design from correlated behavior at the nanoscale, design and optimization of magnetic confinement fusion reactors, strong interactions on a computational lattice through quantum chromodynamics, and stellar explosions and element production. The "virtual prototype," made possible by this expansion, can markedly reduce time-to-market for industrial applications such as jet engines and safer, more fuel efficient cleaner cars. In order to develop USSCC, the National Energy Research Scientific Computing Center (NERSC) announced the competition "Innovative and Novel Computational Impact on Theory and Experiment" (INCITE), with no requirement for current DOE sponsorship. Fifty nine proposals for grand challenge scientific problems were submitted for a small number of awards. The successful grants, and their preliminary progress, will be described.
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ERIC Educational Resources Information Center
Robinson, William R.
2000-01-01
Describes a review of research that addresses the effectiveness of simulations in promoting scientific discovery learning and the problems that learners may encounter when using discovery learning. (WRM)
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Sign use and cognition in automated scientific discovery: are computers only special kinds of signs?
NASA Astrophysics Data System (ADS)
Giza, Piotr
2018-04-01
James Fetzer criticizes the computational paradigm, prevailing in cognitive science by questioning, what he takes to be, its most elementary ingredient: that cognition is computation across representations. He argues that if cognition is taken to be a purposive, meaningful, algorithmic problem solving activity, then computers are incapable of cognition. Instead, they appear to be signs of a special kind, that can facilitate computation. He proposes the conception of minds as semiotic systems as an alternative paradigm for understanding mental phenomena, one that seems to overcome the difficulties of computationalism. Now, I argue, that with computer systems dealing with scientific discovery, the matter is not so simple as that. The alleged superiority of humans using signs to stand for something other over computers being merely "physical symbol systems" or "automatic formal systems" is only easy to establish in everyday life, but becomes far from obvious when scientific discovery is at stake. In science, as opposed to everyday life, the meaning of symbols is, apart from very low-level experimental investigations, defined implicitly by the way the symbols are used in explanatory theories or experimental laws relevant to the field, and in consequence, human and machine discoverers are much more on a par. Moreover, the great practical success of the genetic programming method and recent attempts to apply it to automatic generation of cognitive theories seem to show, that computer systems are capable of very efficient problem solving activity in science, which is neither purposive nor meaningful, nor algorithmic. This, I think, undermines Fetzer's argument that computer systems are incapable of cognition because computation across representations is bound to be a purposive, meaningful, algorithmic problem solving activity.
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JPRS Report, Science & Technology, USSR: Computers
1987-07-15
Algebras and Multilevel Program Planning (G. Ye.. Tseytlin; PROGRAMMIROVANIYE, No 3, May-Jun 86) 36 Linguistic Facilities for Programming...scientific production associations which, jointly with the USSR Academy of Sciences, will solve basic and applied problems in the informatics industry...especially the establishment of complex , interdisciplinary problems and directions), the change in the style of the scientific thought of the epoch, and
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Computational Modeling and Mathematics Applied to the Physical Sciences.
ERIC Educational Resources Information Center
National Academy of Sciences - National Research Council, Washington, DC.
One aim of this report is to show and emphasize that in the computational approaches to most of today's pressing and challenging scientific and technological problems, the mathematical aspects cannot and should not be considered in isolation. Following an introductory chapter, chapter 2 discusses a number of typical problems leading to…
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Elucidating Reaction Mechanisms on Quantum Computers
NASA Astrophysics Data System (ADS)
Wiebe, Nathan; Reiher, Markus; Svore, Krysta; Wecker, Dave; Troyer, Matthias
We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical-computer simulations for such problems, to significantly increase their accuracy and enable hitherto intractable simulations. Detailed resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. This demonstrates that quantum computers will realistically be able to tackle important problems in chemistry that are both scientifically and economically significant.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Fermilab
2017-09-01
Scientists, engineers and programmers at Fermilab are tackling today’s most challenging computational problems. Their solutions, motivated by the needs of worldwide research in particle physics and accelerators, help America stay at the forefront of innovation.
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DOE Office of Scientific and Technical Information (OSTI.GOV)
Sanders, W.M.; Campbell, C.L.; Lester, J.V.
1979-09-01
The Los Alamos Scientific Laboratory is funded by the US Department of Agriculture to apply scientific and computer technology to solve agricultural problems. This report summarizes work during the period October 1, 1977, through September 30, 1978, on the application of computer technology to three areas: (1) surveillance of slaughterplants in Texas; (2) a pilot study of the New Mexico Brucellosis Eradication Program; and (3) the Market Cattle Identification program in Texas.
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1977-05-10
apply this method of forecast- ing in the solution of all major scientific-technical problems of the na- tional economy. Citing the slow...the future, however, computers will "mature" and learn to recognize patterns in what amounts to a much more complex language—the language of visual...images. Photoelectronic tracking devices or "eyes" will allow the computer to take in information in a much more complex form and to perform opera
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Effects of Students' Prior Knowledge on Scientific Reasoning in Density.
ERIC Educational Resources Information Center
Yang, Il-Ho; Kwon, Yong-Ju; Kim, Young-Shin; Jang, Myoung-Duk; Jeong, Jin-Woo; Park, Kuk-Tae
2002-01-01
Investigates the effects of students' prior knowledge on the scientific reasoning processes of performing the task of controlling variables with computer simulation and identifies a number of problems that students encounter in scientific discovery. Involves (n=27) 5th grade students and (n=33) 7th grade students. Indicates that students' prior…
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Scientific and Technological Progress: Problems for the West.
ERIC Educational Resources Information Center
de Rose, Francois
1978-01-01
Discusses the impact of science and technology on major social problems confronting the Western world. Topics include pollution and ecology, military impact, computer science, and the benefits of science and technology. (Author/MA)
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NASA Astrophysics Data System (ADS)
Lisker, Joseph S.
1999-01-01
A new conception of the scientific problem of information exchange in the system plant-man-environment is developed. The laser-optical methods and the system are described which allow computer automated investigation of bio-objects without damaging their vital function. The results of investigation of optical-physiological features of plants and seeds are presented. The effects of chlorophyll well and IR beg are discovered for plants and also the effects os water pumping and protein transformations are shown for seeds. The perspectives of the use of the optical methods and equipment suggested to solve scientific problems of agriculture are discussed.
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Changing from computing grid to knowledge grid in life-science grid.
Talukdar, Veera; Konar, Amit; Datta, Ayan; Choudhury, Anamika Roy
2009-09-01
Grid computing has a great potential to become a standard cyber infrastructure for life sciences that often require high-performance computing and large data handling, which exceeds the computing capacity of a single institution. Grid computer applies the resources of many computers in a network to a single problem at the same time. It is useful to scientific problems that require a great number of computer processing cycles or access to a large amount of data.As biologists,we are constantly discovering millions of genes and genome features, which are assembled in a library and distributed on computers around the world.This means that new, innovative methods must be developed that exploit the re-sources available for extensive calculations - for example grid computing.This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing a "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. By extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.
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Integrating Numerical Computation into the Modeling Instruction Curriculum
ERIC Educational Resources Information Center
Caballero, Marcos D.; Burk, John B.; Aiken, John M.; Thoms, Brian D.; Douglas, Scott S.; Scanlon, Erin M.; Schatz, Michael F.
2014-01-01
Numerical computation (the use of a computer to solve, simulate, or visualize a physical problem) has fundamentally changed the way scientific research is done. Systems that are too difficult to solve in closed form are probed using computation. Experiments that are impossible to perform in the laboratory are studied numerically. Consequently, in…
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A Pipeline Software Architecture for NMR Spectrum Data Translation
Ellis, Heidi J.C.; Weatherby, Gerard; Nowling, Ronald J.; Vyas, Jay; Fenwick, Matthew; Gryk, Michael R.
2012-01-01
The problem of formatting data so that it conforms to the required input for scientific data processing tools pervades scientific computing. The CONNecticut Joint University Research Group (CONNJUR) has developed a data translation tool based on a pipeline architecture that partially solves this problem. The CONNJUR Spectrum Translator supports data format translation for experiments that use Nuclear Magnetic Resonance to determine the structure of large protein molecules. PMID:24634607
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ERIC Educational Resources Information Center
New Orleans Public Schools, LA.
Secondary school teachers incorporating the use of a computer in algebra, trigonometry, advanced mathematics, chemistry, or physics classes are the individuals for whom this book is intended. The content included in it is designed to aid the learning of programing techniques and basic scientific or mathematical principles, and to offer some…
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Self-Directed Cooperative Planetary Rovers
NASA Technical Reports Server (NTRS)
Zilberstein, Shlomo; Morris, Robert (Technical Monitor)
2003-01-01
The project is concerned with the development of decision-theoretic techniques to optimize the scientific return of planetary rovers. Planetary rovers are small unmanned vehicles equipped with cameras and a variety of sensors used for scientific experiments. They must operate under tight constraints over such resources as operation time, power, storage capacity, and communication bandwidth. Moreover, the limited computational resources of the rover limit the complexity of on-line planning and scheduling. We have developed a comprehensive solution to this problem that involves high-level tools to describe a mission; a compiler that maps a mission description and additional probabilistic models of the components of the rover into a Markov decision problem; and algorithms for solving the rover control problem that are sensitive to the limited computational resources and high-level of uncertainty in this domain.
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Accelerating scientific discovery : 2007 annual report.
DOE Office of Scientific and Technical Information (OSTI.GOV)
Beckman, P.; Dave, P.; Drugan, C.
2008-11-14
As a gateway for scientific discovery, the Argonne Leadership Computing Facility (ALCF) works hand in hand with the world's best computational scientists to advance research in a diverse span of scientific domains, ranging from chemistry, applied mathematics, and materials science to engineering physics and life sciences. Sponsored by the U.S. Department of Energy's (DOE) Office of Science, researchers are using the IBM Blue Gene/L supercomputer at the ALCF to study and explore key scientific problems that underlie important challenges facing our society. For instance, a research team at the University of California-San Diego/ SDSC is studying the molecular basis ofmore » Parkinson's disease. The researchers plan to use the knowledge they gain to discover new drugs to treat the disease and to identify risk factors for other diseases that are equally prevalent. Likewise, scientists from Pratt & Whitney are using the Blue Gene to understand the complex processes within aircraft engines. Expanding our understanding of jet engine combustors is the secret to improved fuel efficiency and reduced emissions. Lessons learned from the scientific simulations of jet engine combustors have already led Pratt & Whitney to newer designs with unprecedented reductions in emissions, noise, and cost of ownership. ALCF staff members provide in-depth expertise and assistance to those using the Blue Gene/L and optimizing user applications. Both the Catalyst and Applications Performance Engineering and Data Analytics (APEDA) teams support the users projects. In addition to working with scientists running experiments on the Blue Gene/L, we have become a nexus for the broader global community. In partnership with the Mathematics and Computer Science Division at Argonne National Laboratory, we have created an environment where the world's most challenging computational science problems can be addressed. Our expertise in high-end scientific computing enables us to provide guidance for applications that are transitioning to petascale as well as to produce software that facilitates their development, such as the MPICH library, which provides a portable and efficient implementation of the MPI standard--the prevalent programming model for large-scale scientific applications--and the PETSc toolkit that provides a programming paradigm that eases the development of many scientific applications on high-end computers.« less
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A cross-disciplinary introduction to quantum annealing-based algorithms
NASA Astrophysics Data System (ADS)
Venegas-Andraca, Salvador E.; Cruz-Santos, William; McGeoch, Catherine; Lanzagorta, Marco
2018-04-01
A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.
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NASA Astrophysics Data System (ADS)
Kashansky, Vladislav V.; Kaftannikov, Igor L.
2018-02-01
Modern numerical modeling experiments and data analytics problems in various fields of science and technology reveal a wide variety of serious requirements for distributed computing systems. Many scientific computing projects sometimes exceed the available resource pool limits, requiring extra scalability and sustainability. In this paper we share the experience and findings of our own on combining the power of SLURM, BOINC and GlusterFS as software system for scientific computing. Especially, we suggest a complete architecture and highlight important aspects of systems integration.
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Computer Output Microfilm and Library Catalogs.
ERIC Educational Resources Information Center
Meyer, Richard W.
Early computers dealt with mathematical and scientific problems requiring very little input and not much output, therefore high speed printing devices were not required. Today with increased variety of use, high speed printing is necessary and Computer Output Microfilm (COM) devices have been created to meet this need. This indirect process can…
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Examination of the Computational Thinking Skills of Students
ERIC Educational Resources Information Center
Korucu, Agah Tugrul; Gencturk, Abdullah Tarik; Gundogdu, Mustafa Mucahit
2017-01-01
Computational thinking is generally considered as a kind of analytical way of thinking. According to Wings (2008) it shares with mathematical thinking, engineering thinking and scientific thinking in the general ways in which we may use for solving a problem, designing and evaluating complex systems or understanding computability and intelligence…
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Evaluation of Cache-based Superscalar and Cacheless Vector Architectures for Scientific Computations
NASA Technical Reports Server (NTRS)
Oliker, Leonid; Carter, Jonathan; Shalf, John; Skinner, David; Ethier, Stephane; Biswas, Rupak; Djomehri, Jahed; VanderWijngaart, Rob
2003-01-01
The growing gap between sustained and peak performance for scientific applications has become a well-known problem in high performance computing. The recent development of parallel vector systems offers the potential to bridge this gap for a significant number of computational science codes and deliver a substantial increase in computing capabilities. This paper examines the intranode performance of the NEC SX6 vector processor and the cache-based IBM Power3/4 superscalar architectures across a number of key scientific computing areas. First, we present the performance of a microbenchmark suite that examines a full spectrum of low-level machine characteristics. Next, we study the behavior of the NAS Parallel Benchmarks using some simple optimizations. Finally, we evaluate the perfor- mance of several numerical codes from key scientific computing domains. Overall results demonstrate that the SX6 achieves high performance on a large fraction of our application suite and in many cases significantly outperforms the RISC-based architectures. However, certain classes of applications are not easily amenable to vectorization and would likely require extensive reengineering of both algorithm and implementation to utilize the SX6 effectively.
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5 CFR 551.210 - Computer employees.
Code of Federal Regulations, 2010 CFR
2010-01-01
... 5 Administrative Personnel 1 2010-01-01 2010-01-01 false Computer employees. 551.210 Section 551.210 Administrative Personnel OFFICE OF PERSONNEL MANAGEMENT CIVIL SERVICE REGULATIONS PAY... solve complex business, scientific or engineering problems of the organization or the organization's...
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Exploring quantum computing application to satellite data assimilation
NASA Astrophysics Data System (ADS)
Cheung, S.; Zhang, S. Q.
2015-12-01
This is an exploring work on potential application of quantum computing to a scientific data optimization problem. On classical computational platforms, the physical domain of a satellite data assimilation problem is represented by a discrete variable transform, and classical minimization algorithms are employed to find optimal solution of the analysis cost function. The computation becomes intensive and time-consuming when the problem involves large number of variables and data. The new quantum computer opens a very different approach both in conceptual programming and in hardware architecture for solving optimization problem. In order to explore if we can utilize the quantum computing machine architecture, we formulate a satellite data assimilation experimental case in the form of quadratic programming optimization problem. We find a transformation of the problem to map it into Quadratic Unconstrained Binary Optimization (QUBO) framework. Binary Wavelet Transform (BWT) will be applied to the data assimilation variables for its invertible decomposition and all calculations in BWT are performed by Boolean operations. The transformed problem will be experimented as to solve for a solution of QUBO instances defined on Chimera graphs of the quantum computer.
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NASA Astrophysics Data System (ADS)
Añel, Juan A.
2017-03-01
Nowadays, the majority of the scientific community is not aware of the risks and problems associated with an inadequate use of computer systems for research, mostly for reproducibility of scientific results. Such reproducibility can be compromised by the lack of clear standards and insufficient methodological description of the computational details involved in an experiment. In addition, the inappropriate application or ignorance of copyright laws can have undesirable effects on access to aspects of great importance of the design of experiments and therefore to the interpretation of results.
Plain Language SummaryThis article highlights several important issues to ensure the scientific reproducibility of results within the current scientific framework, going beyond simple documentation. Several specific examples are discussed in the field of hydrological modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/EJ1143751.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/EJ1143751.pdf"><span>High School Students' Written Argumentation Qualities with Problem-Based Computer-Aided Material (PBCAM) Designed about Human Endocrine System</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Vekli, Gülsah Sezen; Çimer, Atilla</p> <p>2017-01-01</p> <p>This study investigated development of students' scientific argumentation levels in the applications made with Problem-Based Computer-Aided Material (PBCAM) designed about Human Endocrine System. The case study method was used: The study group was formed of 43 students in the 11th grade of the science high school in Rize. Human Endocrine System…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED300203.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED300203.pdf"><span>Computer Lab Modules as Problem Solving Tools. Final Report.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Ignatz, Mila E.; Ignatz, Milton</p> <p></p> <p>There are many problems involved in upgrading scientific literacy in high schools: poorly qualified teachers, the lack of good instructional materials, and economic and academic disadvantages all contribute to the problem. This document describes a project designed to increase the opportunities available to the high school science student to…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Notebook+AND+Computers.&pg=6&id=EJ444308','ERIC'); return false;" href="https://eric.ed.gov/?q=Notebook+AND+Computers.&pg=6&id=EJ444308"><span>What We've Learned about Assessing Hands-On Science.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Shavelson, Richard J.; Baxter, Gail P.</p> <p>1992-01-01</p> <p>A recent study compared hands-on scientific inquiry assessment to assessments involving lab notebooks, computer simulations, short-answer paper-and-pencil problems, and multiple-choice questions. Creating high quality performance assessments is a costly, time-consuming process requiring considerable scientific and technological know-how. Improved…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA132359','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA132359"><span>Four Frames Suffice. A Provisionary Model of Vision and Space,</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1982-09-01</p> <p>0 * / Justifi ati AvailabilitY Codes 1. Introduction This paper is an attempt to specify’ a computationally and scientifically plausible model of how...abstract neural compuiting unit and a variety of construtions built of these units and their properties. All of this is part of the connectionist...chosen are inlerided to elucidate the nia’or scientific problems in intermediate level vision and would not be the best choice or a practical computer</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21467227','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21467227"><span>The emergence of spatial cyberinfrastructure.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wright, Dawn J; Wang, Shaowen</p> <p>2011-04-05</p> <p>Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3078415','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3078415"><span>The emergence of spatial cyberinfrastructure</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Wright, Dawn J.; Wang, Shaowen</p> <p>2011-01-01</p> <p>Cyberinfrastructure integrates advanced computer, information, and communication technologies to empower computation-based and data-driven scientific practice and improve the synthesis and analysis of scientific data in a collaborative and shared fashion. As such, it now represents a paradigm shift in scientific research that has facilitated easy access to computational utilities and streamlined collaboration across distance and disciplines, thereby enabling scientific breakthroughs to be reached more quickly and efficiently. Spatial cyberinfrastructure seeks to resolve longstanding complex problems of handling and analyzing massive and heterogeneous spatial datasets as well as the necessity and benefits of sharing spatial data flexibly and securely. This article provides an overview and potential future directions of spatial cyberinfrastructure. The remaining four articles of the special feature are introduced and situated in the context of providing empirical examples of how spatial cyberinfrastructure is extending and enhancing scientific practice for improved synthesis and analysis of both physical and social science data. The primary focus of the articles is spatial analyses using distributed and high-performance computing, sensor networks, and other advanced information technology capabilities to transform massive spatial datasets into insights and knowledge. PMID:21467227</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_1");'>1</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li class="active"><span>3</span></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_3 --> <div id="page_4" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="61"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940009459','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940009459"><span>USRA/RIACS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oliger, Joseph</p> <p>1992-01-01</p> <p>The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under a cooperative agreement with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing; Advanced Methods for Scientific Computing; Learning Systems; High Performance Networks and Technology; Graphics, Visualization, and Virtual Environments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA481620','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA481620"><span>Reconfigurable Computing for Computational Science: A New Focus in High Performance Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2006-11-01</p> <p>in the past decade. Researchers are regularly employing the power of large computing systems and parallel processing to tackle larger and more...complex problems in all of the physical sciences. For the past decade or so, most of this growth in computing power has been “free” with increased...the scientific computing community as a means to continued growth in computing capability. This paper offers a glimpse of the hardware and</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1365487','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1365487"><span>Quantum Testbeds Stakeholder Workshop (QTSW) Report meeting purpose and agenda.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hebner, Gregory A.</p> <p></p> <p>Quantum computing (QC) is a promising early-stage technology with the potential to provide scientific computing capabilities far beyond what is possible with even an Exascale computer in specific problems of relevance to the Office of Science. These include (but are not limited to) materials modeling, molecular dynamics, and quantum chromodynamics. However, commercial QC systems are not yet available and the technical maturity of current QC hardware, software, algorithms, and systems integration is woefully incomplete. Thus, there is a significant opportunity for DOE to define the technology building blocks, and solve the system integration issues to enable a revolutionary tool. Oncemore » realized, QC will have world changing impact on economic competitiveness, the scientific enterprise, and citizen well-being. Prior to this workshop, DOE / Office of Advanced Scientific Computing Research (ASCR) hosted a workshop in 2015 to explore QC scientific applications. The goal of that workshop was to assess the viability of QC technologies to meet the computational requirements in support of DOE’s science and energy mission and to identify the potential impact of these technologies.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20050180255','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20050180255"><span>A Parallel Processing Algorithm for Remote Sensing Classification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gualtieri, J. Anthony</p> <p>2005-01-01</p> <p>A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN23B3732H','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN23B3732H"><span>Cultural and Technological Issues and Solutions for Geodynamics Software Citation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Heien, E. M.; Hwang, L.; Fish, A. E.; Smith, M.; Dumit, J.; Kellogg, L. H.</p> <p>2014-12-01</p> <p>Computational software and custom-written codes play a key role in scientific research and teaching, providing tools to perform data analysis and forward modeling through numerical computation. However, development of these codes is often hampered by the fact that there is no well-defined way for the authors to receive credit or professional recognition for their work through the standard methods of scientific publication and subsequent citation of the work. This in turn may discourage researchers from publishing their codes or making them easier for other scientists to use. We investigate the issues involved in citing software in a scientific context, and introduce features that should be components of a citation infrastructure, particularly oriented towards the codes and scientific culture in the area of geodynamics research. The codes used in geodynamics are primarily specialized numerical modeling codes for continuum mechanics problems; they may be developed by individual researchers, teams of researchers, geophysicists in collaboration with computational scientists and applied mathematicians, or by coordinated community efforts such as the Computational Infrastructure for Geodynamics. Some but not all geodynamics codes are open-source. These characteristics are common to many areas of geophysical software development and use. We provide background on the problem of software citation and discuss some of the barriers preventing adoption of such citations, including social/cultural barriers, insufficient technological support infrastructure, and an overall lack of agreement about what a software citation should consist of. We suggest solutions in an initial effort to create a system to support citation of software and promotion of scientific software development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4014878','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4014878"><span>Open-Phylo: a customizable crowd-computing platform for multiple sequence alignment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2013-01-01</p> <p>Citizen science games such as Galaxy Zoo, Foldit, and Phylo aim to harness the intelligence and processing power generated by crowds of online gamers to solve scientific problems. However, the selection of the data to be analyzed through these games is under the exclusive control of the game designers, and so are the results produced by gamers. Here, we introduce Open-Phylo, a freely accessible crowd-computing platform that enables any scientist to enter our system and use crowds of gamers to assist computer programs in solving one of the most fundamental problems in genomics: the multiple sequence alignment problem. PMID:24148814</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=rate+AND+interest&pg=4&id=EJ861681','ERIC'); return false;" href="https://eric.ed.gov/?q=rate+AND+interest&pg=4&id=EJ861681"><span>A Study of Attrition and the Use of Student Learning Communities in the Computer Science Introductory Programming Sequence</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Howles, Trudy</p> <p>2009-01-01</p> <p>Student attrition and low graduation rates are critical problems in computer science education. Disappointing graduation rates and declining student interest have caught the attention of business leaders, researchers and universities. With weak graduation rates and little interest in scientific computing, many are concerned about the USA's ability…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017GGG....18..769W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017GGG....18..769W"><span>TerraFERMA: The Transparent Finite Element Rapid Model Assembler for multiphysics problems in Earth sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, Cian R.; Spiegelman, Marc; van Keken, Peter E.</p> <p>2017-02-01</p> <p>We introduce and describe a new software infrastructure TerraFERMA, the Transparent Finite Element Rapid Model Assembler, for the rapid and reproducible description and solution of coupled multiphysics problems. The design of TerraFERMA is driven by two computational needs in Earth sciences. The first is the need for increased flexibility in both problem description and solution strategies for coupled problems where small changes in model assumptions can lead to dramatic changes in physical behavior. The second is the need for software and models that are more transparent so that results can be verified, reproduced, and modified in a manner such that the best ideas in computation and Earth science can be more easily shared and reused. TerraFERMA leverages three advanced open-source libraries for scientific computation that provide high-level problem description (FEniCS), composable solvers for coupled multiphysics problems (PETSc), and an options handling system (SPuD) that allows the hierarchical management of all model options. TerraFERMA integrates these libraries into an interface that organizes the scientific and computational choices required in a model into a single options file from which a custom compiled application is generated and run. Because all models share the same infrastructure, models become more reusable and reproducible, while still permitting the individual researcher considerable latitude in model construction. TerraFERMA solves partial differential equations using the finite element method. It is particularly well suited for nonlinear problems with complex coupling between components. TerraFERMA is open-source and available at http://terraferma.github.io, which includes links to documentation and example input files.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1339043-stable-marriages-greedy-matchings','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1339043-stable-marriages-greedy-matchings"><span>On Stable Marriages and Greedy Matchings</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Manne, Fredrik; Naim, Md; Lerring, Hakon</p> <p>2016-12-11</p> <p>Research on stable marriage problems has a long and mathematically rigorous history, while that of exploiting greedy matchings in combinatorial scientific computing is a younger and less developed research field. In this paper we consider the relationships between these two areas. In particular we show that several problems related to computing greedy matchings can be formulated as stable marriage problems and as a consequence several recently proposed algorithms for computing greedy matchings are in fact special cases of well known algorithms for the stable marriage problem. However, in terms of implementations and practical scalable solutions on modern hardware, the greedymore » matching community has made considerable progress. We show that due to the strong relationship between these two fields many of these results are also applicable for solving stable marriage problems.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930018035','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930018035"><span>USRA/RIACS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oliger, Joseph</p> <p>1992-01-01</p> <p>The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under a cooperative agreement with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: (1) parallel computing; (2) advanced methods for scientific computing; (3) learning systems; (4) high performance networks and technology; and (5) graphics, visualization, and virtual environments. In the past year, parallel compiler techniques and adaptive numerical methods for flows in complicated geometries were identified as important problems to investigate for ARC's involvement in the Computational Grand Challenges of the next decade. We concluded a summer student visitors program during this six months. We had six visiting graduate students that worked on projects over the summer and presented seminars on their work at the conclusion of their visits. RIACS technical reports are usually preprints of manuscripts that have been submitted to research journals or conference proceedings. A list of these reports for the period July 1, 1992 through December 31, 1992 is provided.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890016257','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890016257"><span>Speeding up parallel processing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Denning, Peter J.</p> <p>1988-01-01</p> <p>In 1967 Amdahl expressed doubts about the ultimate utility of multiprocessors. The formulation, now called Amdahl's law, became part of the computing folklore and has inspired much skepticism about the ability of the current generation of massively parallel processors to efficiently deliver all their computing power to programs. The widely publicized recent results of a group at Sandia National Laboratory, which showed speedup on a 1024 node hypercube of over 500 for three fixed size problems and over 1000 for three scalable problems, have convincingly challenged this bit of folklore and have given new impetus to parallel scientific computing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AIPC.1629..424G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AIPC.1629..424G"><span>Mathematical modeling of heat transfer problems in the permafrost</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gornov, V. F.; Stepanov, S. P.; Vasilyeva, M. V.; Vasilyev, V. I.</p> <p>2014-11-01</p> <p>In this work we present results of numerical simulation of three-dimensional temperature fields in soils for various applied problems: the railway line in the conditions of permafrost for different geometries, the horizontal tunnel underground storage and greenhouses of various designs in the Far North. Mathematical model of the process is described by a nonstationary heat equation with phase transitions of pore water. The numerical realization of the problem is based on the finite element method using a library of scientific computing FEniCS. For numerical calculations we use high-performance computing systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=problem+AND+artificial+AND+intelligence&pg=3&id=EJ283190','ERIC'); return false;" href="https://eric.ed.gov/?q=problem+AND+artificial+AND+intelligence&pg=3&id=EJ283190"><span>Computer Intelligence: Unlimited and Untapped.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Staples, Betsy</p> <p>1983-01-01</p> <p>Herbert Simon (Nobel prize-winning economist/professor) expresses his views on human and artificial intelligence, problem solving, inventing concepts, and the future. Includes comments on expert systems, state of the art in artificial intelligence, robotics, and "Bacon," a computer program that finds scientific laws hidden in raw data.…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000PhDT.......192C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000PhDT.......192C"><span>The effects of computer-simulated experiments on high school biology students' problem-solving skills and achievement</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Carmack, Gay Lynn Dickinson</p> <p>2000-10-01</p> <p>This two-part quasi-experimental repeated measures study examined whether computer simulated experiments have an effect on the problem solving skills of high school biology students in a school-within-a-school magnet program. Specifically, the study identified episodes in a simulation sequence where problem solving skills improved. In the Fall academic semester, experimental group students (n = 30) were exposed to two simulations: CaseIt! and EVOLVE!. Control group students participated in an internet research project and a paper Hardy-Weinberg activity. In the Spring academic semester, experimental group students were exposed to three simulations: Genetics Construction Kit, CaseIt! and EVOLVE! . Spring control group students participated in a Drosophila lab, an internet research project, and Advanced Placement lab 8. Results indicate that the Fall and Spring experimental groups experienced significant gains in scientific problem solving after the second simulation in the sequence. These gains were independent of the simulation sequence or the amount of time spent on the simulations. These gains were significantly greater than control group scores in the Fall. The Spring control group significantly outscored all other study groups on both pretest measures. Even so, the Spring experimental group problem solving performance caught up to the Spring control group performance after the third simulation. There were no significant differences between control and experimental groups on content achievement. Results indicate that CSE is as effective as traditional laboratories in promoting scientific problem solving and that CSE is a useful tool for improving students' scientific problem solving skills. Moreover, retention of problem solving skills is enhanced by utilizing more than one simulation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=medicinal&id=EJ832500','ERIC'); return false;" href="https://eric.ed.gov/?q=medicinal&id=EJ832500"><span>Conformational Analysis of Drug Molecules: A Practical Exercise in the Medicinal Chemistry Course</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Yuriev, Elizabeth; Chalmers, David; Capuano, Ben</p> <p>2009-01-01</p> <p>Medicinal chemistry is a specialized, scientific discipline. Computational chemistry and structure-based drug design constitute important themes in the education of medicinal chemists. This problem-based task is associated with structure-based drug design lectures. It requires students to use computational techniques to investigate conformational…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950010045','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950010045"><span>Equation solvers for distributed-memory computers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Storaasli, Olaf O.</p> <p>1994-01-01</p> <p>A large number of scientific and engineering problems require the rapid solution of large systems of simultaneous equations. The performance of parallel computers in this area now dwarfs traditional vector computers by nearly an order of magnitude. This talk describes the major issues involved in parallel equation solvers with particular emphasis on the Intel Paragon, IBM SP-1 and SP-2 processors.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPhCS.633a2130E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPhCS.633a2130E"><span>Human-computer interfaces applied to numerical solution of the Plateau problem</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Elias Fabris, Antonio; Soares Bandeira, Ivana; Ramos Batista, Valério</p> <p>2015-09-01</p> <p>In this work we present a code in Matlab to solve the Problem of Plateau numerically, and the code will include human-computer interface. The Problem of Plateau has applications in areas of knowledge like, for instance, Computer Graphics. The solution method will be the same one of the Surface Evolver, but the difference will be a complete graphical interface with the user. This will enable us to implement other kinds of interface like ocular mouse, voice, touch, etc. To date, Evolver does not include any graphical interface, which restricts its use by the scientific community. Specially, its use is practically impossible for most of the Physically Challenged People.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19870007345','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19870007345"><span>Applications of artificial intelligence to scientific research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Prince, Mary Ellen</p> <p>1986-01-01</p> <p>Artificial intelligence (AI) is a growing field which is just beginning to make an impact on disciplines other than computer science. While a number of military and commercial applications were undertaken in recent years, few attempts were made to apply AI techniques to basic scientific research. There is no inherent reason for the discrepancy. The characteristics of the problem, rather than its domain, determines whether or not it is suitable for an AI approach. Expert system, intelligent tutoring systems, and learning programs are examples of theoretical topics which can be applied to certain areas of scientific research. Further research and experimentation should eventurally make it possible for computers to act as intelligent assistants to scientists.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880012303','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880012303"><span>Parallel algorithms for mapping pipelined and parallel computations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nicol, David M.</p> <p>1988-01-01</p> <p>Many computational problems in image processing, signal processing, and scientific computing are naturally structured for either pipelined or parallel computation. When mapping such problems onto a parallel architecture it is often necessary to aggregate an obvious problem decomposition. Even in this context the general mapping problem is known to be computationally intractable, but recent advances have been made in identifying classes of problems and architectures for which optimal solutions can be found in polynomial time. Among these, the mapping of pipelined or parallel computations onto linear array, shared memory, and host-satellite systems figures prominently. This paper extends that work first by showing how to improve existing serial mapping algorithms. These improvements have significantly lower time and space complexities: in one case a published O(nm sup 3) time algorithm for mapping m modules onto n processors is reduced to an O(nm log m) time complexity, and its space requirements reduced from O(nm sup 2) to O(m). Run time complexity is further reduced with parallel mapping algorithms based on these improvements, which run on the architecture for which they create the mappings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920013195','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920013195"><span>FAST: A multi-processed environment for visualization of computational fluid dynamics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bancroft, Gordon V.; Merritt, Fergus J.; Plessel, Todd C.; Kelaita, Paul G.; Mccabe, R. Kevin</p> <p>1991-01-01</p> <p>Three-dimensional, unsteady, multi-zoned fluid dynamics simulations over full scale aircraft are typical of the problems being investigated at NASA Ames' Numerical Aerodynamic Simulation (NAS) facility on CRAY2 and CRAY-YMP supercomputers. With multiple processor workstations available in the 10-30 Mflop range, we feel that these new developments in scientific computing warrant a new approach to the design and implementation of analysis tools. These larger, more complex problems create a need for new visualization techniques not possible with the existing software or systems available as of this writing. The visualization techniques will change as the supercomputing environment, and hence the scientific methods employed, evolves even further. The Flow Analysis Software Toolkit (FAST), an implementation of a software system for fluid mechanics analysis, is discussed.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_2");'>2</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li class="active"><span>4</span></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_4 --> <div id="page_5" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="81"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19860060773&hterms=linear+programming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dlinear%2Bprogramming','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19860060773&hterms=linear+programming&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dlinear%2Bprogramming"><span>Communications oriented programming of parallel iterative solutions of sparse linear systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Patrick, M. L.; Pratt, T. W.</p> <p>1986-01-01</p> <p>Parallel algorithms are developed for a class of scientific computational problems by partitioning the problems into smaller problems which may be solved concurrently. The effectiveness of the resulting parallel solutions is determined by the amount and frequency of communication and synchronization and the extent to which communication can be overlapped with computation. Three different parallel algorithms for solving the same class of problems are presented, and their effectiveness is analyzed from this point of view. The algorithms are programmed using a new programming environment. Run-time statistics and experience obtained from the execution of these programs assist in measuring the effectiveness of these algorithms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EGUGA..1817382P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EGUGA..1817382P"><span>Challenges and opportunities of cloud computing for atmospheric sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pérez Montes, Diego A.; Añel, Juan A.; Pena, Tomás F.; Wallom, David C. H.</p> <p>2016-04-01</p> <p>Cloud computing is an emerging technological solution widely used in many fields. Initially developed as a flexible way of managing peak demand it has began to make its way in scientific research. One of the greatest advantages of cloud computing for scientific research is independence of having access to a large cyberinfrastructure to fund or perform a research project. Cloud computing can avoid maintenance expenses for large supercomputers and has the potential to 'democratize' the access to high-performance computing, giving flexibility to funding bodies for allocating budgets for the computational costs associated with a project. Two of the most challenging problems in atmospheric sciences are computational cost and uncertainty in meteorological forecasting and climate projections. Both problems are closely related. Usually uncertainty can be reduced with the availability of computational resources to better reproduce a phenomenon or to perform a larger number of experiments. Here we expose results of the application of cloud computing resources for climate modeling using cloud computing infrastructures of three major vendors and two climate models. We show how the cloud infrastructure compares in performance to traditional supercomputers and how it provides the capability to complete experiments in shorter periods of time. The monetary cost associated is also analyzed. Finally we discuss the future potential of this technology for meteorological and climatological applications, both from the point of view of operational use and research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009msty.book..393C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009msty.book..393C"><span>Build It: Will They Come?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Corrie, Brian; Zimmerman, Todd</p> <p></p> <p>Scientific research is fundamentally collaborative in nature, and many of today's complex scientific problems require domain expertise in a wide range of disciplines. In order to create research groups that can effectively explore such problems, research collaborations are often formed that involve colleagues at many institutions, sometimes spanning a country and often spanning the world. An increasingly common manifestation of such a collaboration is the collaboratory (Bos et al., 2007), a “…center without walls in which the nation's researchers can perform research without regard to geographical location — interacting with colleagues, accessing instrumentation, sharing data and computational resources, and accessing information from digital libraries.” In order to bring groups together on such a scale, a wide range of components need to be available to researchers, including distributed computer systems, remote instrumentation, data storage, collaboration tools, and the financial and human resources to operate and run such a system (National Research Council, 1993). Media Spaces, as both a technology and a social facilitator, have the potential to meet many of these needs. In this chapter, we focus on the use of scientific media spaces (SMS) as a tool for supporting collaboration in scientific research. In particular, we discuss the design, deployment, and use of a set of SMS environments deployed by WestGrid and one of its collaborating organizations, the Centre for Interdisciplinary Research in the Mathematical and Computational Sciences (IRMACS) over a 5-year period.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JEI....26a3014P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JEI....26a3014P"><span>Laying the foundation to use Raspberry Pi 3 V2 camera module imagery for scientific and engineering purposes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pagnutti, Mary; Ryan, Robert E.; Cazenavette, George; Gold, Maxwell; Harlan, Ryan; Leggett, Edward; Pagnutti, James</p> <p>2017-01-01</p> <p>A comprehensive radiometric characterization of raw-data format imagery acquired with the Raspberry Pi 3 and V2.1 camera module is presented. The Raspberry Pi is a high-performance single-board computer designed to educate and solve real-world problems. This small computer supports a camera module that uses a Sony IMX219 8 megapixel CMOS sensor. This paper shows that scientific and engineering-grade imagery can be produced with the Raspberry Pi 3 and its V2.1 camera module. Raw imagery is shown to be linear with exposure and gain (ISO), which is essential for scientific and engineering applications. Dark frame, noise, and exposure stability assessments along with flat fielding results, spectral response measurements, and absolute radiometric calibration results are described. This low-cost imaging sensor, when calibrated to produce scientific quality data, can be used in computer vision, biophotonics, remote sensing, astronomy, high dynamic range imaging, and security applications, to name a few.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2578010','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2578010"><span>The Workstation Approach to Laboratory Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Crosby, P.A.; Malachowski, G.C.; Hall, B.R.; Stevens, V.; Gunn, B.J.; Hudson, S.; Schlosser, D.</p> <p>1985-01-01</p> <p>There is a need for a Laboratory Workstation which specifically addresses the problems associated with computing in the scientific laboratory. A workstation based on the IBM PC architecture and including a front end data acquisition system which communicates with a host computer via a high speed communications link; a new graphics display controller with hardware window management and window scrolling; and an integrated software package is described.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1255238','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1255238"><span>PETSc Users Manual Revision 3.7</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Balay, Satish; Abhyankar, S.; Adams, M.</p> <p></p> <p>This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1409218','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1409218"><span>PETSc Users Manual Revision 3.8</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Balay, S.; Abhyankar, S.; Adams, M.</p> <p></p> <p>This manual describes the use of PETSc for the numerical solution of partial differential equations and related problems on high-performance computers. The Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures and routines that provide the building blocks for the implementation of large-scale application codes on parallel (and serial) computers. PETSc uses the MPI standard for all message-passing communication.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED196710.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED196710.pdf"><span>A Research Study of Computer-Based Tutoring of Mathematical and Scientific Knowledge. Final Technical Report.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Goldstein, Ira</p> <p></p> <p>Computer coaching of students as an aid in problem-solving instruction is discussed. This report describes an advanced form of computer-assisted instruction that must not only present the material to be taught, but also analyze the student's responses. The program must decide whether to intervene and how much to say to a pupil based on its…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/CFR-2010-title29-vol3/pdf/CFR-2010-title29-vol3-sec541-402.pdf','CFR'); return false;" href="https://www.gpo.gov/fdsys/pkg/CFR-2010-title29-vol3/pdf/CFR-2010-title29-vol3-sec541-402.pdf"><span>29 CFR 541.402 - Executive and administrative computer employees.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collectionCfr.action?selectedYearFrom=2010&page.go=Go">Code of Federal Regulations, 2010 CFR</a></p> <p></p> <p>2010-07-01</p> <p>... planning, scheduling, and coordinating activities required to develop systems to solve complex business, scientific or engineering problems of the employer or the employer's customers. Similarly, a senior or lead...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT........30V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT........30V"><span>A Systematic Approach for Obtaining Performance on Matrix-Like Operations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Veras, Richard Michael</p> <p></p> <p>Scientific Computation provides a critical role in the scientific process because it allows us ask complex queries and test predictions that would otherwise be unfeasible to perform experimentally. Because of its power, Scientific Computing has helped drive advances in many fields ranging from Engineering and Physics to Biology and Sociology to Economics and Drug Development and even to Machine Learning and Artificial Intelligence. Common among these domains is the desire for timely computational results, thus a considerable amount of human expert effort is spent towards obtaining performance for these scientific codes. However, this is no easy task because each of these domains present their own unique set of challenges to software developers, such as domain specific operations, structurally complex data and ever-growing datasets. Compounding these problems are the myriads of constantly changing, complex and unique hardware platforms that an expert must target. Unfortunately, an expert is typically forced to reproduce their effort across multiple problem domains and hardware platforms. In this thesis, we demonstrate the automatic generation of expert level high-performance scientific codes for Dense Linear Algebra (DLA), Structured Mesh (Stencil), Sparse Linear Algebra and Graph Analytic. In particular, this thesis seeks to address the issue of obtaining performance on many complex platforms for a certain class of matrix-like operations that span across many scientific, engineering and social fields. We do this by automating a method used for obtaining high performance in DLA and extending it to structured, sparse and scale-free domains. We argue that it is through the use of the underlying structure found in the data from these domains that enables this process. Thus, obtaining performance for most operations does not occur in isolation of the data being operated on, but instead depends significantly on the structure of the data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004AGUFMSF31B..04G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004AGUFMSF31B..04G"><span>Computational Infrastructure for Geodynamics (CIG)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gurnis, M.; Kellogg, L. H.; Bloxham, J.; Hager, B. H.; Spiegelman, M.; Willett, S.; Wysession, M. E.; Aivazis, M.</p> <p>2004-12-01</p> <p>Solid earth geophysicists have a long tradition of writing scientific software to address a wide range of problems. In particular, computer simulations came into wide use in geophysics during the decade after the plate tectonic revolution. Solution schemes and numerical algorithms that developed in other areas of science, most notably engineering, fluid mechanics, and physics, were adapted with considerable success to geophysics. This software has largely been the product of individual efforts and although this approach has proven successful, its strength for solving problems of interest is now starting to show its limitations as we try to share codes and algorithms or when we want to recombine codes in novel ways to produce new science. With funding from the NSF, the US community has embarked on a Computational Infrastructure for Geodynamics (CIG) that will develop, support, and disseminate community-accessible software for the greater geodynamics community from model developers to end-users. The software is being developed for problems involving mantle and core dynamics, crustal and earthquake dynamics, magma migration, seismology, and other related topics. With a high level of community participation, CIG is leveraging state-of-the-art scientific computing into a suite of open-source tools and codes. The infrastructure that we are now starting to develop will consist of: (a) a coordinated effort to develop reusable, well-documented and open-source geodynamics software; (b) the basic building blocks - an infrastructure layer - of software by which state-of-the-art modeling codes can be quickly assembled; (c) extension of existing software frameworks to interlink multiple codes and data through a superstructure layer; (d) strategic partnerships with the larger world of computational science and geoinformatics; and (e) specialized training and workshops for both the geodynamics and broader Earth science communities. The CIG initiative has already started to leverage and develop long-term strategic partnerships with open source development efforts within the larger thrusts of scientific computing and geoinformatics. These strategic partnerships are essential as the frontier has moved into multi-scale and multi-physics problems in which many investigators now want to use simulation software for data interpretation, data assimilation, and hypothesis testing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=technical+AND+report+AND+computer&pg=5&id=EJ980434','ERIC'); return false;" href="https://eric.ed.gov/?q=technical+AND+report+AND+computer&pg=5&id=EJ980434"><span>Using Scenarios to Design Complex Technology-Enhanced Learning Environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>de Jong, Ton; Weinberger, Armin; Girault, Isabelle; Kluge, Anders; Lazonder, Ard W.; Pedaste, Margus; Ludvigsen, Sten; Ney, Muriel; Wasson, Barbara; Wichmann, Astrid; Geraedts, Caspar; Giemza, Adam; Hovardas, Tasos; Julien, Rachel; van Joolingen, Wouter R.; Lejeune, Anne; Manoli, Constantinos C.; Matteman, Yuri; Sarapuu, Tago; Verkade, Alex; Vold, Vibeke; Zacharia, Zacharias C.</p> <p>2012-01-01</p> <p>Science Created by You (SCY) learning environments are computer-based environments in which students learn about science topics in the context of addressing a socio-scientific problem. Along their way to a solution for this problem students produce many types of intermediate products or learning objects. SCY learning environments center the entire…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25125798','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25125798"><span>Testing Scientific Software: A Systematic Literature Review.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kanewala, Upulee; Bieman, James M</p> <p>2014-10-01</p> <p>Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/7133918','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/7133918"><span>Extending compile-time reverse mode and exploiting partial separability in ADIFOR</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bischof, C.H.; El-Khadiri, M.</p> <p>1992-10-01</p> <p>The numerical methods employed in the solution of many scientific computing problems require the computation of the gradient of a function f: R[sup n] [yields] R. ADIFOR is a source translator that, given a collection of subroutines to compute f, generates Fortran 77 code for computing the derivative of this function. Using the so-called torsion problem from the MINPACK-2 test collection as an example, this paper explores two issues in automatic differentiation: the efficient computation of derivatives for partial separable functions and the use of the compile-time reverse mode for the generation of derivatives. We show that orders of magnitudesmore » of improvement are possible when exploiting partial separability and maximizing use of the reverse mode.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014InPhT..66..160F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014InPhT..66..160F"><span>Application of infrared thermography in computer aided diagnosis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Faust, Oliver; Rajendra Acharya, U.; Ng, E. Y. K.; Hong, Tan Jen; Yu, Wenwei</p> <p>2014-09-01</p> <p>The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060022636','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060022636"><span>Information Power Grid Posters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Vaziri, Arsi</p> <p>2003-01-01</p> <p>This document is a summary of the accomplishments of the Information Power Grid (IPG). Grids are an emerging technology that provide seamless and uniform access to the geographically dispersed, computational, data storage, networking, instruments, and software resources needed for solving large-scale scientific and engineering problems. The goal of the NASA IPG is to use NASA's remotely located computing and data system resources to build distributed systems that can address problems that are too large or complex for a single site. The accomplishments outlined in this poster presentation are: access to distributed data, IPG heterogeneous computing, integration of large-scale computing node into distributed environment, remote access to high data rate instruments,and exploratory grid environment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008asvo.proc..139I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008asvo.proc..139I"><span>Adaptation of XMM-Newton SAS to GRID and VO architectures via web</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ibarra, A.; de La Calle, I.; Gabriel, C.; Salgado, J.; Osuna, P.</p> <p>2008-10-01</p> <p>The XMM-Newton Scientific Analysis Software (SAS) is a robust software that has allowed users to produce good scientific results since the beginning of the mission. This has been possible given the SAS capability to evolve with the advent of new technologies and adapt to the needs of the scientific community. The prototype of the Remote Interface for Science Analysis (RISA) presented here, is one such example, which provides remote analysis of XMM-Newton data with access to all the existing SAS functionality, while making use of GRID computing technology. This new technology has recently emerged within the astrophysical community to tackle the ever lasting problem of computer power for the reduction of large amounts of data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19780025843&hterms=record+scientific&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Drecord%2Bscientific','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19780025843&hterms=record+scientific&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D10%26Ntt%3Drecord%2Bscientific"><span>A data management system for engineering and scientific computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Elliot, L.; Kunii, H. S.; Browne, J. C.</p> <p>1978-01-01</p> <p>Data elements and relationship definition capabilities for this data management system are explicitly tailored to the needs of engineering and scientific computing. System design was based upon studies of data management problems currently being handled through explicit programming. The system-defined data element types include real scalar numbers, vectors, arrays and special classes of arrays such as sparse arrays and triangular arrays. The data model is hierarchical (tree structured). Multiple views of data are provided at two levels. Subschemas provide multiple structural views of the total data base and multiple mappings for individual record types are supported through the use of a REDEFINES capability. The data definition language and the data manipulation language are designed as extensions to FORTRAN. Examples of the coding of real problems taken from existing practice in the data definition language and the data manipulation language are given.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/674852','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/674852"><span>High performance computing and communications: Advancing the frontiers of information technology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>NONE</p> <p>1997-12-31</p> <p>This report, which supplements the President`s Fiscal Year 1997 Budget, describes the interagency High Performance Computing and Communications (HPCC) Program. The HPCC Program will celebrate its fifth anniversary in October 1996 with an impressive array of accomplishments to its credit. Over its five-year history, the HPCC Program has focused on developing high performance computing and communications technologies that can be applied to computation-intensive applications. Major highlights for FY 1996: (1) High performance computing systems enable practical solutions to complex problems with accuracies not possible five years ago; (2) HPCC-funded research in very large scale networking techniques has been instrumental inmore » the evolution of the Internet, which continues exponential growth in size, speed, and availability of information; (3) The combination of hardware capability measured in gigaflop/s, networking technology measured in gigabit/s, and new computational science techniques for modeling phenomena has demonstrated that very large scale accurate scientific calculations can be executed across heterogeneous parallel processing systems located thousands of miles apart; (4) Federal investments in HPCC software R and D support researchers who pioneered the development of parallel languages and compilers, high performance mathematical, engineering, and scientific libraries, and software tools--technologies that allow scientists to use powerful parallel systems to focus on Federal agency mission applications; and (5) HPCC support for virtual environments has enabled the development of immersive technologies, where researchers can explore and manipulate multi-dimensional scientific and engineering problems. Educational programs fostered by the HPCC Program have brought into classrooms new science and engineering curricula designed to teach computational science. This document contains a small sample of the significant HPCC Program accomplishments in FY 1996.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/932408','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/932408"><span>Final Report for "Implimentation and Evaluation of Multigrid Linear Solvers into Extended Magnetohydrodynamic Codes for Petascale Computing"</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Srinath Vadlamani; Scott Kruger; Travis Austin</p> <p></p> <p>Extended magnetohydrodynamic (MHD) codes are used to model the large, slow-growing instabilities that are projected to limit the performance of International Thermonuclear Experimental Reactor (ITER). The multiscale nature of the extended MHD equations requires an implicit approach. The current linear solvers needed for the implicit algorithm scale poorly because the resultant matrices are so ill-conditioned. A new solver is needed, especially one that scales to the petascale. The most successful scalable parallel processor solvers to date are multigrid solvers. Applying multigrid techniques to a set of equations whose fundamental modes are dispersive waves is a promising solution to CEMM problems.more » For the Phase 1, we implemented multigrid preconditioners from the HYPRE project of the Center for Applied Scientific Computing at LLNL via PETSc of the DOE SciDAC TOPS for the real matrix systems of the extended MHD code NIMROD which is a one of the primary modeling codes of the OFES-funded Center for Extended Magnetohydrodynamic Modeling (CEMM) SciDAC. We implemented the multigrid solvers on the fusion test problem that allows for real matrix systems with success, and in the process learned about the details of NIMROD data structures and the difficulties of inverting NIMROD operators. The further success of this project will allow for efficient usage of future petascale computers at the National Leadership Facilities: Oak Ridge National Laboratory, Argonne National Laboratory, and National Energy Research Scientific Computing Center. The project will be a collaborative effort between computational plasma physicists and applied mathematicians at Tech-X Corporation, applied mathematicians Front Range Scientific Computations, Inc. (who are collaborators on the HYPRE project), and other computational plasma physicists involved with the CEMM project.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_3");'>3</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li class="active"><span>5</span></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_5 --> <div id="page_6" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="101"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860023560','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860023560"><span>First 3 years of operation of RIACS (Research Institute for Advanced Computer Science) (1983-1985)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Denning, P. J.</p> <p>1986-01-01</p> <p>The focus of the Research Institute for Advanced Computer Science (RIACS) is to explore matches between advanced computing architectures and the processes of scientific research. An architecture evaluation of the MIT static dataflow machine, specification of a graphical language for expressing distributed computations, and specification of an expert system for aiding in grid generation for two-dimensional flow problems was initiated. Research projects for 1984 and 1985 are summarized.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Electricity&id=EJ1168897','ERIC'); return false;" href="https://eric.ed.gov/?q=Electricity&id=EJ1168897"><span>The Development and Evaluation of a Computer-Simulated Science Inquiry Environment Using Gamified Elements</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Tsai, Fu-Hsing</p> <p>2018-01-01</p> <p>This study developed a computer-simulated science inquiry environment, called the Science Detective Squad, to engage students in investigating an electricity problem that may happen in daily life. The environment combined the simulation of scientific instruments and a virtual environment, including gamified elements, such as points and a story for…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040084409','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040084409"><span>Unstructured Adaptive (UA) NAS Parallel Benchmark. Version 1.0</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Feng, Huiyu; VanderWijngaart, Rob; Biswas, Rupak; Mavriplis, Catherine</p> <p>2004-01-01</p> <p>We present a complete specification of a new benchmark for measuring the performance of modern computer systems when solving scientific problems featuring irregular, dynamic memory accesses. It complements the existing NAS Parallel Benchmark suite. The benchmark involves the solution of a stylized heat transfer problem in a cubic domain, discretized on an adaptively refined, unstructured mesh.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/28649288','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/28649288"><span>A Hybrid Human-Computer Approach to the Extraction of Scientific Facts from the Literature.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Tchoua, Roselyne B; Chard, Kyle; Audus, Debra; Qin, Jian; de Pablo, Juan; Foster, Ian</p> <p>2016-01-01</p> <p>A wealth of valuable data is locked within the millions of research articles published each year. Reading and extracting pertinent information from those articles has become an unmanageable task for scientists. This problem hinders scientific progress by making it hard to build on results buried in literature. Moreover, these data are loosely structured, encoded in manuscripts of various formats, embedded in different content types, and are, in general, not machine accessible. We present a hybrid human-computer solution for semi-automatically extracting scientific facts from literature. This solution combines an automated discovery, download, and extraction phase with a semi-expert crowd assembled from students to extract specific scientific facts. To evaluate our approach we apply it to a challenging molecular engineering scenario, extraction of a polymer property: the Flory-Huggins interaction parameter. We demonstrate useful contributions to a comprehensive database of polymer properties.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1167393','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1167393"><span>Combinatorial Algorithms to Enable Computational Science and Engineering: Work from the CSCAPES Institute</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Boman, Erik G.; Catalyurek, Umit V.; Chevalier, Cedric</p> <p>2015-01-16</p> <p>This final progress report summarizes the work accomplished at the Combinatorial Scientific Computing and Petascale Simulations Institute. We developed Zoltan, a parallel mesh partitioning library that made use of accurate hypergraph models to provide load balancing in mesh-based computations. We developed several graph coloring algorithms for computing Jacobian and Hessian matrices and organized them into a software package called ColPack. We developed parallel algorithms for graph coloring and graph matching problems, and also designed multi-scale graph algorithms. Three PhD students graduated, six more are continuing their PhD studies, and four postdoctoral scholars were advised. Six of these students and Fellowsmore » have joined DOE Labs (Sandia, Berkeley), as staff scientists or as postdoctoral scientists. We also organized the SIAM Workshop on Combinatorial Scientific Computing (CSC) in 2007, 2009, and 2011 to continue to foster the CSC community.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PPNL...14..993B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PPNL...14..993B"><span>Impact of configuration management system of computer center on support of scientific projects throughout their lifecycle</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogdanov, A. V.; Iuzhanin, N. V.; Zolotarev, V. I.; Ezhakova, T. R.</p> <p>2017-12-01</p> <p>In this article the problem of scientific projects support throughout their lifecycle in the computer center is considered in every aspect of support. Configuration Management system plays a connecting role in processes related to the provision and support of services of a computer center. In view of strong integration of IT infrastructure components with the use of virtualization, control of infrastructure becomes even more critical to the support of research projects, which means higher requirements for the Configuration Management system. For every aspect of research projects support, the influence of the Configuration Management system is being reviewed and development of the corresponding elements of the system is being described in the present paper.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013EUCAS...5...93P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013EUCAS...5...93P"><span>Effects of shock on hypersonic boundary layer stability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pinna, F.; Rambaud, P.</p> <p>2013-06-01</p> <p>The design of hypersonic vehicles requires the estimate of the laminar to turbulent transition location for an accurate sizing of the thermal protection system. Linear stability theory is a fast scientific way to study the problem. Recent improvements in computational capabilities allow computing the flow around a full vehicle instead of using only simplified boundary layer equations. In this paper, the effect of the shock is studied on a mean flow provided by steady Computational Fluid Dynamics (CFD) computations and simplified boundary layer calculations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/10114303','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/10114303"><span>Extending compile-time reverse mode and exploiting partial separability in ADIFOR. ADIFOR Working Note No. 7</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bischof, C.H.; El-Khadiri, M.</p> <p>1992-10-01</p> <p>The numerical methods employed in the solution of many scientific computing problems require the computation of the gradient of a function f: R{sup n} {yields} R. ADIFOR is a source translator that, given a collection of subroutines to compute f, generates Fortran 77 code for computing the derivative of this function. Using the so-called torsion problem from the MINPACK-2 test collection as an example, this paper explores two issues in automatic differentiation: the efficient computation of derivatives for partial separable functions and the use of the compile-time reverse mode for the generation of derivatives. We show that orders of magnitudesmore » of improvement are possible when exploiting partial separability and maximizing use of the reverse mode.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20150008748&hterms=cloud+computing&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcloud%2Bcomputing','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20150008748&hterms=cloud+computing&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D50%26Ntt%3Dcloud%2Bcomputing"><span>Evaluating the Efficacy of the Cloud for Cluster Computation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Knight, David; Shams, Khawaja; Chang, George; Soderstrom, Tom</p> <p>2012-01-01</p> <p>Computing requirements vary by industry, and it follows that NASA and other research organizations have computing demands that fall outside the mainstream. While cloud computing made rapid inroads for tasks such as powering web applications, performance issues on highly distributed tasks hindered early adoption for scientific computation. One venture to address this problem is Nebula, NASA's homegrown cloud project tasked with delivering science-quality cloud computing resources. However, another industry development is Amazon's high-performance computing (HPC) instances on Elastic Cloud Compute (EC2) that promises improved performance for cluster computation. This paper presents results from a series of benchmarks run on Amazon EC2 and discusses the efficacy of current commercial cloud technology for running scientific applications across a cluster. In particular, a 240-core cluster of cloud instances achieved 2 TFLOPS on High-Performance Linpack (HPL) at 70% of theoretical computational performance. The cluster's local network also demonstrated sub-100 ?s inter-process latency with sustained inter-node throughput in excess of 8 Gbps. Beyond HPL, a real-world Hadoop image processing task from NASA's Lunar Mapping and Modeling Project (LMMP) was run on a 29 instance cluster to process lunar and Martian surface images with sizes on the order of tens of gigapixels. These results demonstrate that while not a rival of dedicated supercomputing clusters, commercial cloud technology is now a feasible option for moderately demanding scientific workloads.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMGP53B1124N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMGP53B1124N"><span>A Parallel Numerical Micromagnetic Code Using FEniCS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nagy, L.; Williams, W.; Mitchell, L.</p> <p>2013-12-01</p> <p>Many problems in the geosciences depend on understanding the ability of magnetic minerals to provide stable paleomagnetic recordings. Numerical micromagnetic modelling allows us to calculate the domain structures found in naturally occurring magnetic materials. However the computational cost rises exceedingly quickly with respect to the size and complexity of the geometries that we wish to model. This problem is compounded by the fact that the modern processor design no longer focuses on the speed at which calculations are performed, but rather on the number of computational units amongst which we may distribute our calculations. Consequently to better exploit modern computational resources our micromagnetic simulations must "go parallel". We present a parallel and scalable micromagnetics code written using FEniCS. FEniCS is a multinational collaboration involving several institutions (University of Cambridge, University of Chicago, The Simula Research Laboratory, etc.) that aims to provide a set of tools for writing scientific software; in particular software that employs the finite element method. The advantages of this approach are the leveraging of pre-existing projects from the world of scientific computing (PETSc, Trilinos, Metis/Parmetis, etc.) and exposing these so that researchers may pose problems in a manner closer to the mathematical language of their domain. Our code provides a scriptable interface (in Python) that allows users to not only run micromagnetic models in parallel, but also to perform pre/post processing of data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4128280','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4128280"><span>Testing Scientific Software: A Systematic Literature Review</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kanewala, Upulee; Bieman, James M.</p> <p>2014-01-01</p> <p>Context Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. Objective This study aims to identify specific challenges, proposed solutions, and unsolved problems faced when testing scientific software. Method We conducted a systematic literature survey to identify and analyze relevant literature. We identified 62 studies that provided relevant information about testing scientific software. Results We found that challenges faced when testing scientific software fall into two main categories: (1) testing challenges that occur due to characteristics of scientific software such as oracle problems and (2) testing challenges that occur due to cultural differences between scientists and the software engineering community such as viewing the code and the model that it implements as inseparable entities. In addition, we identified methods to potentially overcome these challenges and their limitations. Finally we describe unsolved challenges and how software engineering researchers and practitioners can help to overcome them. Conclusions Scientific software presents special challenges for testing. Specifically, cultural differences between scientist developers and software engineers, along with the characteristics of the scientific software make testing more difficult. Existing techniques such as code clone detection can help to improve the testing process. Software engineers should consider special challenges posed by scientific software such as oracle problems when developing testing techniques. PMID:25125798</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040084608','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040084608"><span>Job Scheduling in a Heterogeneous Grid Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shan, Hong-Zhang; Smith, Warren; Oliker, Leonid; Biswas, Rupak</p> <p>2004-01-01</p> <p>Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19870017124','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19870017124"><span>Animated computer graphics models of space and earth sciences data generated via the massively parallel processor</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Treinish, Lloyd A.; Gough, Michael L.; Wildenhain, W. David</p> <p>1987-01-01</p> <p>The capability was developed of rapidly producing visual representations of large, complex, multi-dimensional space and earth sciences data sets via the implementation of computer graphics modeling techniques on the Massively Parallel Processor (MPP) by employing techniques recently developed for typically non-scientific applications. Such capabilities can provide a new and valuable tool for the understanding of complex scientific data, and a new application of parallel computing via the MPP. A prototype system with such capabilities was developed and integrated into the National Space Science Data Center's (NSSDC) Pilot Climate Data System (PCDS) data-independent environment for computer graphics data display to provide easy access to users. While developing these capabilities, several problems had to be solved independently of the actual use of the MPP, all of which are outlined.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017RScEd.tmp..235M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017RScEd.tmp..235M"><span>Model-Based Knowing: How Do Students Ground Their Understanding About Climate Systems in Agent-Based Computer Models?</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Markauskaite, Lina; Kelly, Nick; Jacobson, Michael J.</p> <p>2017-12-01</p> <p>This paper gives a grounded cognition account of model-based learning of complex scientific knowledge related to socio-scientific issues, such as climate change. It draws on the results from a study of high school students learning about the carbon cycle through computational agent-based models and investigates two questions: First, how do students ground their understanding about the phenomenon when they learn and solve problems with computer models? Second, what are common sources of mistakes in students' reasoning with computer models? Results show that students ground their understanding in computer models in five ways: direct observation, straight abstraction, generalisation, conceptualisation, and extension. Students also incorporate into their reasoning their knowledge and experiences that extend beyond phenomena represented in the models, such as attitudes about unsustainable carbon emission rates, human agency, external events, and the nature of computational models. The most common difficulties of the students relate to seeing the modelled scientific phenomenon and connecting results from the observations with other experiences and understandings about the phenomenon in the outside world. An important contribution of this study is the constructed coding scheme for establishing different ways of grounding, which helps to understand some challenges that students encounter when they learn about complex phenomena with agent-based computer models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD0628405','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD0628405"><span>SDC DOCUMENTS APPLICABLE TO STATE AND LOCAL GOVERNMENT PROBLEMS.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p></p> <p>Public administration , Urban and regional planning, The administration of justice, Bio-medical systems, Educational systems, Computer program systems, The development and management of computer-based systems, Information retrieval, Simulation. AD numbers are provided for those documents which can be obtained from the Defense Documentation Center or the Department of Commerce’s Clearinghouse for Federal Scientific and Technical Information.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=electromagnetic&pg=6&id=EJ1150209','ERIC'); return false;" href="https://eric.ed.gov/?q=electromagnetic&pg=6&id=EJ1150209"><span>Design and Use Online Platforms to Learn Mathematics and the Use of Them in Simulations of Problems in Applied Sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Méndez-Fragoso, Ricardo; Villavicencio-Torres, Mirna; Martínez-Moreno, Josué</p> <p>2017-01-01</p> <p>In this contribution, we show the practical use of the computer to visualise simple computational simulations to show phenomena that occur in everyday life, or require an abstract understanding for being unintuitive phenomena. The relationship of the mathematics to different scientific disciplines motivates us to devise different treatments to…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/18261888','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18261888"><span>The changing features of the body-mind problem.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Agassi, Joseph</p> <p>2007-01-01</p> <p>The body-mind problem invites scientific study, since mental events are repeated and repeatable and invite testable explanations. They seemed troublesome because of the classical theory of substance that failed to solve its own central problems. These are soluble with the aid of the theory of the laws of nature, particularly in its emergentist version [Bunge, M., 1980. The Body-mind Problem, Pergamon, Oxford] that invites refutable explanations [Popper, K.R., 1959. The Logic of Scientific Discovery, Hutchinson, London]. The view of mental properties as emergent is a modification of the two chief classical views, materialism and dualism. As this view invites testable explanations of events of the inner world, it is better than the quasi-behaviorist view of self-awareness as computer-style self-monitoring [Minsky, M., Laske, O., 1992. A conversation with Marvin Minsky. AI Magazine 13 (3), 31-45].</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3019603','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3019603"><span>Application of Metamorphic Testing to Supervised Classifiers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Xie, Xiaoyuan; Ho, Joshua; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh</p> <p>2010-01-01</p> <p>Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no “test oracle” to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called “metamorphic testing”, which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well. PMID:21243103</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1997APS..CPC...S07P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1997APS..CPC...S07P"><span>An Object-Oriented Network-Centric Software Architecture for Physical Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palmer, Richard</p> <p>1997-08-01</p> <p>Recent developments in object-oriented computer languages and infrastructure such as the Internet, Web browsers, and the like provide an opportunity to define a more productive computational environment for scientific programming that is based more closely on the underlying mathematics describing physics than traditional programming languages such as FORTRAN or C++. In this talk I describe an object-oriented software architecture for representing physical problems that includes classes for such common mathematical objects as geometry, boundary conditions, partial differential and integral equations, discretization and numerical solution methods, etc. In practice, a scientific program written using this architecture looks remarkably like the mathematics used to understand the problem, is typically an order of magnitude smaller than traditional FORTRAN or C++ codes, and hence easier to understand, debug, describe, etc. All objects in this architecture are ``network-enabled,'' which means that components of a software solution to a physical problem can be transparently loaded from anywhere on the Internet or other global network. The architecture is expressed as an ``API,'' or application programmers interface specification, with reference embeddings in Java, Python, and C++. A C++ class library for an early version of this API has been implemented for machines ranging from PC's to the IBM SP2, meaning that phidentical codes run on all architectures.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17775454','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17775454"><span>Exact solution of large asymmetric traveling salesman problems.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Miller, D L; Pekny, J F</p> <p>1991-02-15</p> <p>The traveling salesman problem is one of a class of difficult problems in combinatorial optimization that is representative of a large number of important scientific and engineering problems. A survey is given of recent applications and methods for solving large problems. In addition, an algorithm for the exact solution of the asymmetric traveling salesman problem is presented along with computational results for several classes of problems. The results show that the algorithm performs remarkably well for some classes of problems, determining an optimal solution even for problems with large numbers of cities, yet for other classes, even small problems thwart determination of a provably optimal solution.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_4");'>4</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li class="active"><span>6</span></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_6 --> <div id="page_7" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="121"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JAGI....6....1B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JAGI....6....1B"><span>Editorial: Computational Creativity, Concept Invention, and General Intelligence</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Besold, Tarek R.; Kühnberger, Kai-Uwe; Veale, Tony</p> <p>2015-12-01</p> <p>Over the last decade, computational creativity as a field of scientific investigation and computational systems engineering has seen growing popularity. Still, the levels of development between projects aiming at systems for artistic production or performance and endeavours addressing creative problem-solving or models of creative cognitive capacities is diverging. While the former have already seen several great successes, the latter still remain in their infancy. This volume collects reports on work trying to close the accrued gap.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1798b0117O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1798b0117O"><span>Automation of multi-agent control for complex dynamic systems in heterogeneous computational network</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Oparin, Gennady; Feoktistov, Alexander; Bogdanova, Vera; Sidorov, Ivan</p> <p>2017-01-01</p> <p>The rapid progress of high-performance computing entails new challenges related to solving large scientific problems for various subject domains in a heterogeneous distributed computing environment (e.g., a network, Grid system, or Cloud infrastructure). The specialists in the field of parallel and distributed computing give the special attention to a scalability of applications for problem solving. An effective management of the scalable application in the heterogeneous distributed computing environment is still a non-trivial issue. Control systems that operate in networks, especially relate to this issue. We propose a new approach to the multi-agent management for the scalable applications in the heterogeneous computational network. The fundamentals of our approach are the integrated use of conceptual programming, simulation modeling, network monitoring, multi-agent management, and service-oriented programming. We developed a special framework for an automation of the problem solving. Advantages of the proposed approach are demonstrated on the parametric synthesis example of the static linear regulator for complex dynamic systems. Benefits of the scalable application for solving this problem include automation of the multi-agent control for the systems in a parallel mode with various degrees of its detailed elaboration.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018IJBC...2850055N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018IJBC...2850055N"><span>Inconsistencies in Numerical Simulations of Dynamical Systems Using Interval Arithmetic</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nepomuceno, Erivelton G.; Peixoto, Márcia L. C.; Martins, Samir A. M.; Rodrigues, Heitor M.; Perc, Matjaž</p> <p></p> <p>Over the past few decades, interval arithmetic has been attracting widespread interest from the scientific community. With the expansion of computing power, scientific computing is encountering a noteworthy shift from floating-point arithmetic toward increased use of interval arithmetic. Notwithstanding the significant reliability of interval arithmetic, this paper presents a theoretical inconsistency in a simulation of dynamical systems using a well-known implementation of arithmetic interval. We have observed that two natural interval extensions present an empty intersection during a finite time range, which is contrary to the fundamental theorem of interval analysis. We have proposed a procedure to at least partially overcome this problem, based on the union of the two generated pseudo-orbits. This paper also shows a successful case of interval arithmetic application in the reduction of interval width size on the simulation of discrete map. The implications of our findings on the reliability of scientific computing using interval arithmetic have been properly addressed using two numerical examples.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4934704','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4934704"><span>Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Abdulhamid, Shafi’i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid</p> <p>2016-01-01</p> <p>Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. PMID:27384239</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27384239','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27384239"><span>Secure Scientific Applications Scheduling Technique for Cloud Computing Environment Using Global League Championship Algorithm.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Abdulhamid, Shafi'i Muhammad; Abd Latiff, Muhammad Shafie; Abdul-Salaam, Gaddafi; Hussain Madni, Syed Hamid</p> <p>2016-01-01</p> <p>Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. The experiment is carried out using CloudSim simulator. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900051805&hterms=feeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dfeeling','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900051805&hterms=feeling&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D70%26Ntt%3Dfeeling"><span>Scientific work environments in the next decade</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Gomez, Julian E.</p> <p>1989-01-01</p> <p>The applications of contemporary computer graphics to scientific visualization is described, with emphasis on the nonintuitive problems. A radically different approach is proposed which centers on the idea of the scientist being in the simulation display space rather than observing it on a screen. Interaction is performed with nonstandard input devices to preserve the feeling of being immersed in the three-dimensional display space. Construction of such a system could begin now with currently available technology.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JOSS....2..357P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JOSS....2..357P"><span>schwimmbad: A uniform interface to parallel processing pools in Python</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Price-Whelan, Adrian M.; Foreman-Mackey, Daniel</p> <p>2017-09-01</p> <p>Many scientific and computing problems require doing some calculation on all elements of some data set. If the calculations can be executed in parallel (i.e. without any communication between calculations), these problems are said to be perfectly parallel. On computers with multiple processing cores, these tasks can be distributed and executed in parallel to greatly improve performance. A common paradigm for handling these distributed computing problems is to use a processing "pool": the "tasks" (the data) are passed in bulk to the pool, and the pool handles distributing the tasks to a number of worker processes when available. schwimmbad provides a uniform interface to parallel processing pools and enables switching easily between local development (e.g., serial processing or with multiprocessing) and deployment on a cluster or supercomputer (via, e.g., MPI or JobLib).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=one-group+AND+pretest+AND+posttest&pg=5&id=EJ1140594','ERIC'); return false;" href="https://eric.ed.gov/?q=one-group+AND+pretest+AND+posttest&pg=5&id=EJ1140594"><span>"What's the Weather Like Today?": A Computer Game to Develop Algorithmic Thinking and Problem Solving Skills of Primary School Pupils</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Gürbüz, Hasan; Evlioglu, Bengisu; Erol, Çigdem Selçukcan; Gülseçen, Hulusi; Gülseçen, Sevinç</p> <p>2017-01-01</p> <p>Computer-based games as developments in information technology seem to grow and spread rapidly. Using of these games by children and teenagers have increased. The presence of more beneficial and educational games in contrast to the violent and harmful games is remarkable. Many scientific studies have indicated that the useful (functional) games…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA516485','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA516485"><span>Computational Electromagnetics Application to Small Geometric Anomalies and Associated Ucertainty Evaluation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2010-02-28</p> <p>implemented a fast method to enable the statistical characterization of electromagnetic interference and compatibility (EMI/EMC) phenomena on electrically...higher accuracy is needed, e.g., to compute higher moment statistics . To address this problem, we have developed adaptive stochastic collocation methods ...SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES) AF OFFICE OF SCIENTIFIC RESEARCH 875 N. RANDOLPH ST. ROOM 3112 ARLINGTON VA 22203 UA</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19929848','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19929848"><span>The synergistic use of computation, chemistry and biology to discover novel peptide-based drugs: the time is right.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Audie, J; Boyd, C</p> <p>2010-01-01</p> <p>The case for peptide-based drugs is compelling. Due to their chemical, physical and conformational diversity, and relatively unproblematic toxicity and immunogenicity, peptides represent excellent starting material for drug discovery. Nature has solved many physiological and pharmacological problems through the use of peptides, polypeptides and proteins. If nature could solve such a diversity of challenging biological problems through the use of peptides, it seems reasonable to infer that human ingenuity will prove even more successful. And this, indeed, appears to be the case, as a number of scientific and methodological advances are making peptides and peptide-based compounds ever more promising pharmacological agents. Chief among these advances are powerful chemical and biological screening technologies for lead identification and optimization, methods for enhancing peptide in vivo stability, bioavailability and cell-permeability, and new delivery technologies. Other advances include the development and experimental validation of robust computational methods for peptide lead identification and optimization. Finally, scientific analysis, biology and chemistry indicate the prospect of designing relatively small peptides to therapeutically modulate so-called 'undruggable' protein-protein interactions. Taken together a clear picture is emerging: through the synergistic use of the scientific imagination and the computational, chemical and biological methods that are currently available, effective peptide therapeutics for novel targets can be designed that surpass even the proven peptidic designs of nature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910009297','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910009297"><span>Distributed memory compiler design for sparse problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wu, Janet; Saltz, Joel; Berryman, Harry; Hiranandani, Seema</p> <p>1991-01-01</p> <p>A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004breg.book.....D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004breg.book.....D"><span>Beyond Reason: Eight Great Problems That Reveal the Limits of Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Dewdney, A. K.</p> <p>2004-04-01</p> <p>A mind-bending excursion to the limits of science and mathematics Are some scientific problems insoluble? In Beyond Reason, internationally acclaimed math and science author A. K. Dewdney answers this question by examining eight insurmountable mathematical and scientific roadblocks that have stumped thinkers across the centuries, from ancient mathematical conundrums such as "squaring the circle," first attempted by the Pythagoreans, to G?del's vexing theorem, from perpetual motion to the upredictable behavior of chaotic systems such as the weather. A. K. Dewdney, PhD (Ontario, Canada), was the author of Scientific American's "Computer Recreations" column for eight years. He has written several critically acclaimed popular math and science books, including A Mathematical Mystery Tour (0-471-40734-8); Yes, We Have No Neutrons (0-471-29586-8); and 200% of Nothing (0-471-14574-2).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22160586','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22160586"><span>Computational chemistry and cheminformatics: an essay on the future.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Glen, Robert Charles</p> <p>2012-01-01</p> <p>Computers have changed the way we do science. Surrounded by a sea of data and with phenomenal computing capacity, the methodology and approach to scientific problems is evolving into a partnership between experiment, theory and data analysis. Given the pace of change of the last twenty-five years, it seems folly to speculate on the future, but along with unpredictable leaps of progress there will be a continuous evolution of capability, which points to opportunities and improvements that will certainly appear as our discipline matures.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920019932','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920019932"><span>Building the Scientific Modeling Assistant: An interactive environment for specialized software design</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Keller, Richard M.</p> <p>1991-01-01</p> <p>The construction of scientific software models is an integral part of doing science, both within NASA and within the scientific community at large. Typically, model-building is a time-intensive and painstaking process, involving the design of very large, complex computer programs. Despite the considerable expenditure of resources involved, completed scientific models cannot easily be distributed and shared with the larger scientific community due to the low-level, idiosyncratic nature of the implemented code. To address this problem, we have initiated a research project aimed at constructing a software tool called the Scientific Modeling Assistant. This tool provides automated assistance to the scientist in developing, using, and sharing software models. We describe the Scientific Modeling Assistant, and also touch on some human-machine interaction issues relevant to building a successful tool of this type.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMNG23A..08B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMNG23A..08B"><span>a Novel Discrete Optimal Transport Method for Bayesian Inverse Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bui-Thanh, T.; Myers, A.; Wang, K.; Thiery, A.</p> <p>2017-12-01</p> <p>We present the Augmented Ensemble Transform (AET) method for generating approximate samples from a high-dimensional posterior distribution as a solution to Bayesian inverse problems. Solving large-scale inverse problems is critical for some of the most relevant and impactful scientific endeavors of our time. Therefore, constructing novel methods for solving the Bayesian inverse problem in more computationally efficient ways can have a profound impact on the science community. This research derives the novel AET method for exploring a posterior by solving a sequence of linear programming problems, resulting in a series of transport maps which map prior samples to posterior samples, allowing for the computation of moments of the posterior. We show both theoretical and numerical results, indicating this method can offer superior computational efficiency when compared to other SMC methods. Most of this efficiency is derived from matrix scaling methods to solve the linear programming problem and derivative-free optimization for particle movement. We use this method to determine inter-well connectivity in a reservoir and the associated uncertainty related to certain parameters. The attached file shows the difference between the true parameter and the AET parameter in an example 3D reservoir problem. The error is within the Morozov discrepancy allowance with lower computational cost than other particle methods.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/554168','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/554168"><span>Adventures in supercomputing: Scientific exploration in an era of change</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Gentry, E.; Helland, B.; Summers, B.</p> <p>1997-11-01</p> <p>Students deserve the opportunity to explore the world of science surrounding them. Therefore it is important that scientific exploration and investigation be a part of each student`s educational career. The Department of Energy`s Adventures in Superconducting (AiS) takes students beyond mere scientific literacy to a rich embodiment of scientific exploration. AiS provides today`s science and math students with a greater opportunity to investigate science problems, propose solutions, explore different methods of solving the problem, organize their work into a technical paper, and present their results. Students learn at different rates in different ways. Science classes with students having varying learningmore » styles and levels of achievement have always been a challenge for teachers. The AiS {open_quotes}hands-on, minds-on{close_quotes} project-based method of teaching science meets the challenge of this diversity heads on! AiS uses the development of student chosen projects as the means of achieving a lifelong enthusiasm for scientific proficiency. One goal of AiS is to emulate the research that takes place in the everyday environment of scientists. Students work in teams and often collaborate with students nationwide. With the help of mentors from the academic and scientific community, students pose a problem in science, investigate possible solutions, design a mathematical and computational model for the problem, exercise the model to achieve results, and evaluate the implications of the results. The students then have the opportunity to present the project to their peers, teachers, and scientists. Using this inquiry-based technique, students learn more than science skills, they learn to reason and think -- going well beyond the National Science Education Standard. The teacher becomes a resource person actively working together with the students in their quest for scientific knowledge.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2625686','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2625686"><span>Guidelines for Preparation of a Scientific Paper</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kosiba, Margaret M.</p> <p>1988-01-01</p> <p>Even the experienced scientific writer may have difficulty transferring research results to clear, concise, publishable words. To assist the beginning scientific writer, guidelines are proposed that will provide direction for determining a topic, developing protocols, collecting data, using computers for analysis and word processing, incorporating copyediting notations, consulting scientific writing manuals, and developing sound writing habits. Guidelines for writing each section of a research paper are described to help the writer prepare the title page, introduction, materials and methods, results, and discussion sections of the paper, as well as the acknowledgments and references. Procedures for writing the first draft and subsequent revisions include a checklist of structural and stylistic problems and common errors in English usage. PMID:3339646</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16..253Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16..253Z"><span>Special data base of Informational - Computational System 'INM RAS - Black Sea' for solving inverse and data assimilation problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zakharova, Natalia; Piskovatsky, Nicolay; Gusev, Anatoly</p> <p>2014-05-01</p> <p>Development of Informational-Computational Systems (ICS) for data assimilation procedures is one of multidisciplinary problems. To study and solve these problems one needs to apply modern results from different disciplines and recent developments in: mathematical modeling; theory of adjoint equations and optimal control; inverse problems; numerical methods theory; numerical algebra and scientific computing. The above problems are studied in the Institute of Numerical Mathematics of the Russian Academy of Science (INM RAS) in ICS for personal computers. In this work the results on the Special data base development for ICS "INM RAS - Black Sea" are presented. In the presentation the input information for ICS is discussed, some special data processing procedures are described. In this work the results of forecast using ICS "INM RAS - Black Sea" with operational observation data assimilation are presented. This study was supported by the Russian Foundation for Basic Research (project No 13-01-00753) and by Presidium Program of Russian Academy of Sciences (project P-23 "Black sea as an imitational ocean model"). References 1. V.I. Agoshkov, M.V. Assovskii, S.A. Lebedev, Numerical simulation of Black Sea hydrothermodynamics taking into account tide-forming forces. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 5-31. 2. E.I. Parmuzin, V.I. Agoshkov, Numerical solution of the variational assimilation problem for sea surface temperature in the model of the Black Sea dynamics. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 69-94. 3. V.B. Zalesny, N.A. Diansky, V.V. Fomin, S.N. Moshonkin, S.G. Demyshev, Numerical model of the circulation of Black Sea and Sea of Azov. Russ. J. Numer. Anal. Math. Modelling (2012) 27, No.1, pp. 95-111. 4. Agoshkov V.I.,Assovsky M.B., Giniatulin S. V., Zakharova N.B., Kuimov G.V., Parmuzin E.I., Fomin V.V. Informational Computational system of variational assimilation of observation data "INM RAS - Black sea"// Ecological safety of coastal and shelf zones and complex use of shelf resources: Collection of scientific works. Issue 26, Volume 2. - National Academy of Sciences of Ukraine, Marine Hydrophysical Institute, Sebastopol, 2012. Pages 352-360. (In russian)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/18460474','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/18460474"><span>Computational intelligence approaches for pattern discovery in biological systems.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fogel, Gary B</p> <p>2008-07-01</p> <p>Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1049967','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1049967"><span>Scout: high-performance heterogeneous computing made simple</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Jablin, James; Mc Cormick, Patrick; Herlihy, Maurice</p> <p>2011-01-26</p> <p>Researchers must often write their own simulation and analysis software. During this process they simultaneously confront both computational and scientific problems. Current strategies for aiding the generation of performance-oriented programs do not abstract the software development from the science. Furthermore, the problem is becoming increasingly complex and pressing with the continued development of many-core and heterogeneous (CPU-GPU) architectures. To acbieve high performance, scientists must expertly navigate both software and hardware. Co-design between computer scientists and research scientists can alleviate but not solve this problem. The science community requires better tools for developing, optimizing, and future-proofing codes, allowing scientists to focusmore » on their research while still achieving high computational performance. Scout is a parallel programming language and extensible compiler framework targeting heterogeneous architectures. It provides the abstraction required to buffer scientists from the constantly-shifting details of hardware while still realizing higb-performance by encapsulating software and hardware optimization within a compiler framework.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_5");'>5</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li class="active"><span>7</span></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_7 --> <div id="page_8" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="141"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/6267300-machine-intelligence-applications-securities-production','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/6267300-machine-intelligence-applications-securities-production"><span>Machine intelligence applications to securities production</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Johnson, C.K.</p> <p>1987-01-01</p> <p>The production of security documents provides a cache of interesting problems ranging across a broad spectrum. Some of the problems do not have rigorous scientific solutions available at this time and provide opportunities for less structured approaches such as AI. AI methods can be used in conjunction with traditional scientific and computational methods. The most productive applications of AI occur when this marriage of methods can be carried out without motivation to prove that one method is better than the other. Fields such as ink chemistry and technology, and machine inspection of graphic arts printing offer interesting challenges which willmore » continue to intrigue current and future generations of researchers into the 21st century.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007JPhCS..78a2048M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007JPhCS..78a2048M"><span>Harnessing the power of emerging petascale platforms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mellor-Crummey, John</p> <p>2007-07-01</p> <p>As part of the US Department of Energy's Scientific Discovery through Advanced Computing (SciDAC-2) program, science teams are tackling problems that require computational simulation and modeling at the petascale. A grand challenge for computer science is to develop software technology that makes it easier to harness the power of these systems to aid scientific discovery. As part of its activities, the SciDAC-2 Center for Scalable Application Development Software (CScADS) is building open source software tools to support efficient scientific computing on the emerging leadership-class platforms. In this paper, we describe two tools for performance analysis and tuning that are being developed as part of CScADS: a tool for analyzing scalability and performance, and a tool for optimizing loop nests for better node performance. We motivate these tools by showing how they apply to S3D, a turbulent combustion code under development at Sandia National Laboratory. For S3D, our node performance analysis tool helped uncover several performance bottlenecks. Using our loop nest optimization tool, we transformed S3D's most costly loop nest to reduce execution time by a factor of 2.94 for a processor working on a 503 domain.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/993399','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/993399"><span>2009 ALCF annual report.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Beckman, P.; Martin, D.; Drugan, C.</p> <p>2010-11-23</p> <p>This year the Argonne Leadership Computing Facility (ALCF) delivered nearly 900 million core hours of science. The research conducted at their leadership class facility touched our lives in both minute and massive ways - whether it was studying the catalytic properties of gold nanoparticles, predicting protein structures, or unearthing the secrets of exploding stars. The authors remained true to their vision to act as the forefront computational center in extending science frontiers by solving pressing problems for our nation. Our success in this endeavor was due mainly to the Department of Energy's (DOE) INCITE (Innovative and Novel Computational Impact onmore » Theory and Experiment) program. The program awards significant amounts of computing time to computationally intensive, unclassified research projects that can make high-impact scientific advances. This year, DOE allocated 400 million hours of time to 28 research projects at the ALCF. Scientists from around the world conducted the research, representing such esteemed institutions as the Princeton Plasma Physics Laboratory, National Institute of Standards and Technology, and European Center for Research and Advanced Training in Scientific Computation. Argonne also provided Director's Discretionary allocations for research challenges, addressing such issues as reducing aerodynamic noise, critical for next-generation 'green' energy systems. Intrepid - the ALCF's 557-teraflops IBM Blue/Gene P supercomputer - enabled astounding scientific solutions and discoveries. Intrepid went into full production five months ahead of schedule. As a result, the ALCF nearly doubled the days of production computing available to the DOE Office of Science, INCITE awardees, and Argonne projects. One of the fastest supercomputers in the world for open science, the energy-efficient system uses about one-third as much electricity as a machine of comparable size built with more conventional parts. In October 2009, President Barack Obama recognized the excellence of the entire Blue Gene series by awarding it to the National Medal of Technology and Innovation. Other noteworthy achievements included the ALCF's collaboration with the National Energy Research Scientific Computing Center (NERSC) to examine cloud computing as a potential new computing paradigm for scientists. Named Magellan, the DOE-funded initiative will explore which science application programming models work well within the cloud, as well as evaluate the challenges that come with this new paradigm. The ALCF obtained approval for its next-generation machine, a 10-petaflops system to be delivered in 2012. This system will allow us to resolve ever more pressing problems, even more expeditiously through breakthrough science in the years to come.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013ASPC..475..235L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013ASPC..475..235L"><span>Cross-Identification of Astronomical Catalogs on Multiple GPUs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, M. A.; Budavári, T.</p> <p>2013-10-01</p> <p>One of the most fundamental problems in observational astronomy is the cross-identification of sources. Observations are made in different wavelengths, at different times, and from different locations and instruments, resulting in a large set of independent observations. The scientific outcome is often limited by our ability to quickly perform meaningful associations between detections. The matching, however, is difficult scientifically, statistically, as well as computationally. The former two require detailed physical modeling and advanced probabilistic concepts; the latter is due to the large volumes of data and the problem's combinatorial nature. In order to tackle the computational challenge and to prepare for future surveys, whose measurements will be exponentially increasing in size past the scale of feasible CPU-based solutions, we developed a new implementation which addresses the issue by performing the associations on multiple Graphics Processing Units (GPUs). Our implementation utilizes up to 6 GPUs in combination with the Thrust library to achieve an over 40x speed up verses the previous best implementation running on a multi-CPU SQL Server.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20160006556&hterms=cognition&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcognition','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20160006556&hterms=cognition&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dcognition"><span>Citizen Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Memarsadeghi, Nargess</p> <p>2015-01-01</p> <p>Scientists and engineers constantly face new challenges, despite myriad advances in computing. More sets of data are collected today from earth and sky than there is time or resources available to carefully analyze them. Some problems either don't have fast algorithms to solve them or have solutions that must be found among millions of options, a situation akin to finding a needle in a haystack. But all hope is not lost: advances in technology and the Internet have empowered the general public to participate in the scientific process via individual computational resources and brain cognition, which isn't matched by any machine. Citizen scientists are volunteers who perform scientific work by making observations, collecting and disseminating data, making measurements, and analyzing or interpreting data without necessarily having any scientific training. In so doing, individuals from all over the world can contribute to science in ways that wouldn't have been otherwise possible.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880005251','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880005251"><span>Optimal pre-scheduling of problem remappings</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Nicol, David M.; Saltz, Joel H.</p> <p>1987-01-01</p> <p>A large class of scientific computational problems can be characterized as a sequence of steps where a significant amount of computation occurs each step, but the work performed at each step is not necessarily identical. Two good examples of this type of computation are: (1) regridding methods which change the problem discretization during the course of the computation, and (2) methods for solving sparse triangular systems of linear equations. Recent work has investigated a means of mapping such computations onto parallel processors; the method defines a family of static mappings with differing degrees of importance placed on the conflicting goals of good load balance and low communication/synchronization overhead. The performance tradeoffs are controllable by adjusting the parameters of the mapping method. To achieve good performance it may be necessary to dynamically change these parameters at run-time, but such changes can impose additional costs. If the computation's behavior can be determined prior to its execution, it can be possible to construct an optimal parameter schedule using a low-order-polynomial-time dynamic programming algorithm. Since the latter can be expensive, the performance is studied of the effect of a linear-time scheduling heuristic on one of the model problems, and it is shown to be effective and nearly optimal.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19900004116','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19900004116"><span>Analysis of the flight dynamics of the Solar Maximum Mission (SMM) off-sun scientific pointing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pitone, D. S.; Klein, J. R.</p> <p>1989-01-01</p> <p>Algorithms are presented which were created and implemented by the Goddard Space Flight Center's (GSFC's) Solar Maximum Mission (SMM) attitude operations team to support large-angle spacecraft pointing at scientific objectives. The mission objective of the post-repair SMM satellite was to study solar phenomena. However, because the scientific instruments, such as the Coronagraph/Polarimeter (CP) and the Hard X ray Burst Spectrometer (HXRBS), were able to view objects other than the Sun, attitude operations support for attitude pointing at large angles from the nominal solar-pointing attitudes was required. Subsequently, attitude support for SMM was provided for scientific objectives such as Comet Halley, Supernova 1987A, Cygnus X-1, and the Crab Nebula. In addition, the analysis was extended to include the reverse problem, computing the right ascension and declination of a body given the off-Sun angles. This analysis led to the computation of the orbits of seven new solar comets seen in the field-of-view (FOV) of the CP. The activities necessary to meet these large-angle attitude-pointing sequences, such as slew sequence planning, viewing-period prediction, and tracking-bias computation are described. Analysis is presented for the computation of maneuvers and pointing parameters relative to the SMM-unique, Sun-centered reference frame. Finally, science data and independent attitude solutions are used to evaluate the large-angle pointing performance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900059710&hterms=SMM&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DSMM','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900059710&hterms=SMM&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3DSMM"><span>Analysis of the flight dynamics of the Solar Maximum Mission (SMM) off-sun scientific pointing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Pitone, D. S.; Klein, J. R.; Twambly, B. J.</p> <p>1990-01-01</p> <p>Algorithms are presented which were created and implemented by the Goddard Space Flight Center's (GSFC's) Solar Maximum Mission (SMM) attitude operations team to support large-angle spacecraft pointing at scientific objectives. The mission objective of the post-repair SMM satellite was to study solar phenomena. However, because the scientific instruments, such as the Coronagraph/Polarimeter (CP) and the Hard X-ray Burst Spectrometer (HXRBS), were able to view objects other than the Sun, attitude operations support for attitude pointing at large angles from the nominal solar-pointing attitudes was required. Subsequently, attitude support for SMM was provided for scientific objectives such as Comet Halley, Supernova 1987A, Cygnus X-1, and the Crab Nebula. In addition, the analysis was extended to include the reverse problem, computing the right ascension and declination of a body given the off-Sun angles. This analysis led to the computation of the orbits of seven new solar comets seen in the field-of-view (FOV) of the CP. The activities necessary to meet these large-angle attitude-pointing sequences, such as slew sequence planning, viewing-period prediction, and tracking-bias computation are described. Analysis is presented for the computation of maneuvers and pointing parameters relative to the SMM-unique, Sun-centered reference frame. Finally, science data and independent attitude solutions are used to evaluate the larg-angle pointing performance.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110014533','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110014533"><span>Engineering and Scientific Applications: Using MatLab(Registered Trademark) for Data Processing and Visualization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sen, Syamal K.; Shaykhian, Gholam Ali</p> <p>2011-01-01</p> <p>MatLab(TradeMark)(MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many countries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its real strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbox. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using symbolic operations. MatLab in its interpreter programming language form (command interface) is similar with well known programming languages such as C/C++, support data structures and cell arrays to define classes in object oriented programming. As such, MatLab is equipped with most of the essential constructs of a higher programming language. MatLab is packaged with an editor and debugging functionality useful to perform analysis of large MatLab programs and find errors. We believe there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and analysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applications. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientific problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabular format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1069280','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1069280"><span>A Programming Framework for Scientific Applications on CPU-GPU Systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Owens, John</p> <p>2013-03-24</p> <p>At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiplemore » parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSEdT..25..127W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSEdT..25..127W"><span>Defining Computational Thinking for Mathematics and Science Classrooms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri</p> <p>2016-02-01</p> <p>Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940006574','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940006574"><span>The 1987 RIACS annual report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1987-01-01</p> <p>The Research Institute for Advanced Computer Science (RIACS) was established at the NASA Ames Research Center in June of 1983. RIACS is privately operated by the Universities Space Research Association (USRA), a consortium of 64 universities with graduate programs in the aerospace sciences, under several Cooperative Agreements with NASA. RIACS's goal is to provide preeminent leadership in basic and applied computer science research as partners in support of NASA's goals and missions. In pursuit of this goal, RIACS contributes to several of the grand challenges in science and engineering facing NASA: flying an airplane inside a computer; determining the chemical properties of materials under hostile conditions in the atmospheres of earth and the planets; sending intelligent machines on unmanned space missions; creating a one-world network that makes all scientific resources, including those in space, accessible to all the world's scientists; providing intelligent computational support to all stages of the process of scientific investigation from problem formulation to results dissemination; and developing accurate global models for climatic behavior throughout the world. In working with these challenges, we seek novel architectures, and novel ways to use them, that exploit the potential of parallel and distributed computation and make possible new functions that are beyond the current reach of computing machines. The investigation includes pattern computers as well as the more familiar numeric and symbolic computers, and it includes networked systems of resources distributed around the world. We believe that successful computer science research is interdisciplinary: it is driven by (and drives) important problems in other disciplines. We believe that research should be guided by a clear long-term vision with planned milestones. And we believe that our environment must foster and exploit innovation. Our activities and accomplishments for the calendar year 1987 and our plans for 1988 are reported.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN43C1538N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN43C1538N"><span>Low Latency Workflow Scheduling and an Application of Hyperspectral Brightness Temperatures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Nguyen, P. T.; Chapman, D. R.; Halem, M.</p> <p>2012-12-01</p> <p>New system analytics for Big Data computing holds the promise of major scientific breakthroughs and discoveries from the exploration and mining of the massive data sets becoming available to the science community. However, such data intensive scientific applications face severe challenges in accessing, managing and analyzing petabytes of data. While the Hadoop MapReduce environment has been successfully applied to data intensive problems arising in business, there are still many scientific problem domains where limitations in the functionality of MapReduce systems prevent its wide adoption by those communities. This is mainly because MapReduce does not readily support the unique science discipline needs such as special science data formats, graphic and computational data analysis tools, maintaining high degrees of computational accuracies, and interfacing with application's existing components across heterogeneous computing processors. We address some of these limitations by exploiting the MapReduce programming model for satellite data intensive scientific problems and address scalability, reliability, scheduling, and data management issues when dealing with climate data records and their complex observational challenges. In addition, we will present techniques to support the unique Earth science discipline needs such as dealing with special science data formats (HDF and NetCDF). We have developed a Hadoop task scheduling algorithm that improves latency by 2x for a scientific workflow including the gridding of the EOS AIRS hyperspectral Brightness Temperatures (BT). This workflow processing algorithm has been tested at the Multicore Computing Center private Hadoop based Intel Nehalem cluster, as well as in a virtual mode under the Open Source Eucalyptus cloud. The 55TB AIRS hyperspectral L1b Brightness Temperature record has been gridded at the resolution of 0.5x1.0 degrees, and we have computed a 0.9 annual anti-correlation to the El Nino Southern oscillation in the Nino 4 region, as well as a 1.9 Kelvin decadal Arctic warming in the 4u and 12u spectral regions. Additionally, we will present the frequency of extreme global warming events by the use of a normalized maximum BT in a grid cell relative to its local standard deviation. A low-latency Hadoop scheduling environment maintains data integrity and fault tolerance in a MapReduce data intensive Cloud environment while improving the "time to solution" metric by 35% when compared to a more traditional parallel processing system for the same dataset. Our next step will be to improve the usability of our Hadoop task scheduling system, to enable rapid prototyping of data intensive experiments by means of processing "kernels". We will report on the performance and experience of implementing these experiments on the NEX testbed, and propose the use of a graphical directed acyclic graph (DAG) interface to help us develop on-demand scientific experiments. Our workflow system works within Hadoop infrastructure as a replacement for the FIFO or FairScheduler, thus the use of Apache "Pig" latin or other Apache tools may also be worth investigating on the NEX system to improve the usability of our workflow scheduling infrastructure for rapid experimentation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009JPhCS.180a1003O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009JPhCS.180a1003O"><span>The scaling issue: scientific opportunities</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Orbach, Raymond L.</p> <p>2009-07-01</p> <p>A brief history of the Leadership Computing Facility (LCF) initiative is presented, along with the importance of SciDAC to the initiative. The initiative led to the initiation of the Innovative and Novel Computational Impact on Theory and Experiment program (INCITE), open to all researchers in the US and abroad, and based solely on scientific merit through peer review, awarding sizeable allocations (typically millions of processor-hours per project). The development of the nation's LCFs has enabled available INCITE processor-hours to double roughly every eight months since its inception in 2004. The 'top ten' LCF accomplishments in 2009 illustrate the breadth of the scientific program, while the 75 million processor hours allocated to American business since 2006 highlight INCITE contributions to US competitiveness. The extrapolation of INCITE processor hours into the future brings new possibilities for many 'classic' scaling problems. Complex systems and atomic displacements to cracks are but two examples. However, even with increasing computational speeds, the development of theory, numerical representations, algorithms, and efficient implementation are required for substantial success, exhibiting the crucial role that SciDAC will play.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018OEng....8....7V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018OEng....8....7V"><span>Templet Web: the use of volunteer computing approach in PaaS-style cloud</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vostokin, Sergei; Artamonov, Yuriy; Tsarev, Daniil</p> <p>2018-03-01</p> <p>This article presents the Templet Web cloud service. The service is designed for high-performance scientific computing automation. The use of high-performance technology is specifically required by new fields of computational science such as data mining, artificial intelligence, machine learning, and others. Cloud technologies provide a significant cost reduction for high-performance scientific applications. The main objectives to achieve this cost reduction in the Templet Web service design are: (a) the implementation of "on-demand" access; (b) source code deployment management; (c) high-performance computing programs development automation. The distinctive feature of the service is the approach mainly used in the field of volunteer computing, when a person who has access to a computer system delegates his access rights to the requesting user. We developed an access procedure, algorithms, and software for utilization of free computational resources of the academic cluster system in line with the methods of volunteer computing. The Templet Web service has been in operation for five years. It has been successfully used for conducting laboratory workshops and solving research problems, some of which are considered in this article. The article also provides an overview of research directions related to service development.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930003950','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930003950"><span>Mass storage system experiences and future needs at the National Center for Atmospheric Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Olear, Bernard T.</p> <p>1992-01-01</p> <p>This presentation is designed to relate some of the experiences of the Scientific Computing Division at NCAR dealing with the 'data problem'. A brief history and a development of some basic Mass Storage System (MSS) principles are given. An attempt is made to show how these principles apply to the integration of various components into NCAR's MSS. There is discussion of future MSS needs for future computing environments.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E%26ES...94a2193L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E%26ES...94a2193L"><span>Energy Consumption Management of Virtual Cloud Computing Platform</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Li, Lin</p> <p>2017-11-01</p> <p>For energy consumption management research on virtual cloud computing platforms, energy consumption management of virtual computers and cloud computing platform should be understood deeper. Only in this way can problems faced by energy consumption management be solved. In solving problems, the key to solutions points to data centers with high energy consumption, so people are in great need to use a new scientific technique. Virtualization technology and cloud computing have become powerful tools in people’s real life, work and production because they have strong strength and many advantages. Virtualization technology and cloud computing now is in a rapid developing trend. It has very high resource utilization rate. In this way, the presence of virtualization and cloud computing technologies is very necessary in the constantly developing information age. This paper has summarized, explained and further analyzed energy consumption management questions of the virtual cloud computing platform. It eventually gives people a clearer understanding of energy consumption management of virtual cloud computing platform and brings more help to various aspects of people’s live, work and son on.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/7275575-division-annual-review-operating-plan','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/7275575-division-annual-review-operating-plan"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Not Available</p> <p></p> <p>The Computing and Communications (C) Division is responsible for the Laboratory's Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called Grand Challenge'' problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/10161295-division-annual-review-operating-plan','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/10161295-division-annual-review-operating-plan"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Not Available</p> <p></p> <p>The Computing and Communications (C) Division is responsible for the Laboratory`s Integrated Computing Network (ICN) as well as Laboratory-wide communications. Our computing network, used by 8,000 people distributed throughout the nation, constitutes one of the most powerful scientific computing facilities in the world. In addition to the stable production environment of the ICN, we have taken a leadership role in high-performance computing and have established the Advanced Computing Laboratory (ACL), the site of research on experimental, massively parallel computers; high-speed communication networks; distributed computing; and a broad variety of advanced applications. The computational resources available in the ACL are ofmore » the type needed to solve problems critical to national needs, the so-called ``Grand Challenge`` problems. The purpose of this publication is to inform our clients of our strategic and operating plans in these important areas. We review major accomplishments since late 1990 and describe our strategic planning goals and specific projects that will guide our operations over the next few years. Our mission statement, planning considerations, and management policies and practices are also included.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011IJMES..42..283S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011IJMES..42..283S"><span>Science modelling in pre-calculus: how to make mathematics problems contextually meaningful</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sokolowski, Andrzej; Yalvac, Bugrahan; Loving, Cathleen</p> <p>2011-04-01</p> <p>'Use of mathematical representations to model and interpret physical phenomena and solve problems is one of the major teaching objectives in high school math curriculum' (National Council of Teachers of Mathematics (NCTM), Principles and Standards for School Mathematics, NCTM, Reston, VA, 2000). Commonly used pre-calculus textbooks provide a wide range of application problems. However, these problems focus students' attention on evaluating or solving pre-arranged formulas for given values. The role of scientific content is reduced to provide a background for these problems instead of being sources of data gathering for inducing mathematical tools. Students are neither required to construct mathematical models based on the contexts nor are they asked to validate or discuss the limitations of applied formulas. Using these contexts, the instructor may think that he/she is teaching problem solving, where in reality he/she is teaching algorithms of the mathematical operations (G. Kulm (ed.), New directions for mathematics assessment, in Assessing Higher Order Thinking in Mathematics, Erlbaum, Hillsdale, NJ, 1994, pp. 221-240). Without a thorough representation of the physical phenomena and the mathematical modelling processes undertaken, problem solving unintentionally appears as simple algorithmic operations. In this article, we deconstruct the representations of mathematics problems from selected pre-calculus textbooks and explicate their limitations. We argue that the structure and content of those problems limits students' coherent understanding of mathematical modelling, and this could result in weak student problem-solving skills. Simultaneously, we explore the ways to enhance representations of those mathematical problems, which we have characterized as lacking a meaningful physical context and limiting coherent student understanding. In light of our discussion, we recommend an alternative to strengthen the process of teaching mathematical modelling - utilization of computer-based science simulations. Although there are several exceptional computer-based science simulations designed for mathematics classes (see, e.g. Kinetic Book (http://www.kineticbooks.com/) or Gizmos (http://www.explorelearning.com/)), we concentrate mainly on the PhET Interactive Simulations developed at the University of Colorado at Boulder (http://phet.colorado.edu/) in generating our argument that computer simulations more accurately represent the contextual characteristics of scientific phenomena than their textual descriptions.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_6");'>6</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li class="active"><span>8</span></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_8 --> <div id="page_9" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="161"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005APS..MAR.J6001T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005APS..MAR.J6001T"><span>Scientific Discovery through Advanced Computing in Plasma Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, William</p> <p>2005-03-01</p> <p>Advanced computing is generally recognized to be an increasingly vital tool for accelerating progress in scientific research during the 21st Century. For example, the Department of Energy's ``Scientific Discovery through Advanced Computing'' (SciDAC) Program was motivated in large measure by the fact that formidable scientific challenges in its research portfolio could best be addressed by utilizing the combination of the rapid advances in super-computing technology together with the emergence of effective new algorithms and computational methodologies. The imperative is to translate such progress into corresponding increases in the performance of the scientific codes used to model complex physical systems such as those encountered in high temperature plasma research. If properly validated against experimental measurements and analytic benchmarks, these codes can provide reliable predictive capability for the behavior of a broad range of complex natural and engineered systems. This talk reviews recent progress and future directions for advanced simulations with some illustrative examples taken from the plasma science applications area. Significant recent progress has been made in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics, giving increasingly good agreement between experimental observations and computational modeling. This was made possible by the combination of access to powerful new computational resources together with innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning a huge range in time and space scales. In particular, the plasma science community has made excellent progress in developing advanced codes for which computer run-time and problem size scale well with the number of processors on massively parallel machines (MPP's). A good example is the effective usage of the full power of multi-teraflop (multi-trillion floating point computations per second) MPP's to produce three-dimensional, general geometry, nonlinear particle simulations which have accelerated progress in understanding the nature of plasma turbulence in magnetically-confined high temperature plasmas. These calculations, which typically utilized billions of particles for thousands of time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In general, results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. The associated scientific excitement should serve to stimulate improved cross-cutting collaborations with other fields and also to help attract bright young talent to the computational science area.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA086708','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA086708"><span>Models, Data, and War: a Critique of the Foundation for Defense Analyses.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1980-03-12</p> <p>scientific formulation 6 An "objective" solution 8 Analysis of a squishy problem 9 A judgmental formulation 9 A potential for distortion 11 A subjective...inextricably tied to those judgments. Different analysts, with apparently identical knowledge of a real world problem, may develop plausible formulations ...configured is a concrete theoretical statement." 2/ The formulation of a computer model--conceiving a mathematical representation of the real world</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1436','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1436"><span>Partitioning Rectangular and Structurally Nonsymmetric Sparse Matrices for Parallel Processing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>B. Hendrickson; T.G. Kolda</p> <p>1998-09-01</p> <p>A common operation in scientific computing is the multiplication of a sparse, rectangular or structurally nonsymmetric matrix and a vector. In many applications the matrix- transpose-vector product is also required. This paper addresses the efficient parallelization of these operations. We show that the problem can be expressed in terms of partitioning bipartite graphs. We then introduce several algorithms for this partitioning problem and compare their performance on a set of test matrices.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/15013787','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/15013787"><span>Simulation Data as Data Streams</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Abdulla, G; Arrighi, W; Critchlow, T</p> <p>2003-11-18</p> <p>Computational or scientific simulations are increasingly being applied to solve a variety of scientific problems. Domains such as astrophysics, engineering, chemistry, biology, and environmental studies are benefiting from this important capability. Simulations, however, produce enormous amounts of data that need to be analyzed and understood. In this overview paper, we describe scientific simulation data, its characteristics, and the way scientists generate and use the data. We then compare and contrast simulation data to data streams. Finally, we describe our approach to analyzing simulation data, present the AQSim (Ad-hoc Queries for Simulation data) system, and discuss some of the challenges thatmore » result from handling this kind of data.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2809324','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2809324"><span>The philosophy of scientific experimentation: a review</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p></p> <p>2009-01-01</p> <p>Practicing and studying automated experimentation may benefit from philosophical reflection on experimental science in general. This paper reviews the relevant literature and discusses central issues in the philosophy of scientific experimentation. The first two sections present brief accounts of the rise of experimental science and of its philosophical study. The next sections discuss three central issues of scientific experimentation: the scientific and philosophical significance of intervention and production, the relationship between experimental science and technology, and the interactions between experimental and theoretical work. The concluding section identifies three issues for further research: the role of computing and, more specifically, automating, in experimental research, the nature of experimentation in the social and human sciences, and the significance of normative, including ethical, problems in experimental science. PMID:20098589</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/964104','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/964104"><span>Parallel Computation of the Regional Ocean Modeling System (ROMS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Wang, P; Song, Y T; Chao, Y</p> <p>2005-04-05</p> <p>The Regional Ocean Modeling System (ROMS) is a regional ocean general circulation modeling system solving the free surface, hydrostatic, primitive equations over varying topography. It is free software distributed world-wide for studying both complex coastal ocean problems and the basin-to-global scale ocean circulation. The original ROMS code could only be run on shared-memory systems. With the increasing need to simulate larger model domains with finer resolutions and on a variety of computer platforms, there is a need in the ocean-modeling community to have a ROMS code that can be run on any parallel computer ranging from 10 to hundreds ofmore » processors. Recently, we have explored parallelization for ROMS using the MPI programming model. In this paper, an efficient parallelization strategy for such a large-scale scientific software package, based on an existing shared-memory computing model, is presented. In addition, scientific applications and data-performance issues on a couple of SGI systems, including Columbia, the world's third-fastest supercomputer, are discussed.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19860003843','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19860003843"><span>Discovery of the Kalman filter as a practical tool for aerospace and industry</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Mcgee, L. A.; Schmidt, S. F.</p> <p>1985-01-01</p> <p>The sequence of events which led the researchers at Ames Research Center to the early discovery of the Kalman filter shortly after its introduction into the literature is recounted. The scientific breakthroughs and reformulations that were necessary to transform Kalman's work into a useful tool for a specific aerospace application are described. The resulting extended Kalman filter, as it is now known, is often still referred to simply as the Kalman filter. As the filter's use gained in popularity in the scientific community, the problems of implementation on small spaceborne and airborne computers led to a square-root formulation of the filter to overcome numerical difficulties associated with computer word length. The work that led to this new formulation is also discussed, including the first airborne computer implementation and flight test. Since then the applications of the extended and square-root formulations of the Kalman filter have grown rapidly throughout the aerospace industry.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/877703','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/877703"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>None, None</p> <p></p> <p>The Second SIAM Conference on Computational Science and Engineering was held in San Diego from February 10-12, 2003. Total conference attendance was 553. This is a 23% increase in attendance over the first conference. The focus of this conference was to draw attention to the tremendous range of major computational efforts on large problems in science and engineering, to promote the interdisciplinary culture required to meet these large-scale challenges, and to encourage the training of the next generation of computational scientists. Computational Science & Engineering (CS&E) is now widely accepted, along with theory and experiment, as a crucial third modemore » of scientific investigation and engineering design. Aerospace, automotive, biological, chemical, semiconductor, and other industrial sectors now rely on simulation for technical decision support. For federal agencies also, CS&E has become an essential support for decisions on resources, transportation, and defense. CS&E is, by nature, interdisciplinary. It grows out of physical applications and it depends on computer architecture, but at its heart are powerful numerical algorithms and sophisticated computer science techniques. From an applied mathematics perspective, much of CS&E has involved analysis, but the future surely includes optimization and design, especially in the presence of uncertainty. Another mathematical frontier is the assimilation of very large data sets through such techniques as adaptive multi-resolution, automated feature search, and low-dimensional parameterization. The themes of the 2003 conference included, but were not limited to: Advanced Discretization Methods; Computational Biology and Bioinformatics; Computational Chemistry and Chemical Engineering; Computational Earth and Atmospheric Sciences; Computational Electromagnetics; Computational Fluid Dynamics; Computational Medicine and Bioengineering; Computational Physics and Astrophysics; Computational Solid Mechanics and Materials; CS&E Education; Meshing and Adaptivity; Multiscale and Multiphysics Problems; Numerical Algorithms for CS&E; Discrete and Combinatorial Algorithms for CS&E; Inverse Problems; Optimal Design, Optimal Control, and Inverse Problems; Parallel and Distributed Computing; Problem-Solving Environments; Software and Wddleware Systems; Uncertainty Estimation and Sensitivity Analysis; and Visualization and Computer Graphics.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=computational+AND+technical+AND+report&pg=6&id=ED031114','ERIC'); return false;" href="https://eric.ed.gov/?q=computational+AND+technical+AND+report&pg=6&id=ED031114"><span>An Optimum Strategy for Learning to Read Foreign Scientific and Technical Literature.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Alford, M.H.T.</p> <p></p> <p>A report on a series of computer programs being written to make frequency counts designed to indicate the learning problems to be encountered in any approach to a target literature discusses how these programs can be used to aid in foreign language vocabulary learning. (Author/AF)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21585552','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21585552"><span>CFD and ventilation research.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Li, Y; Nielsen, P V</p> <p>2011-12-01</p> <p>There has been a rapid growth of scientific literature on the application of computational fluid dynamics (CFD) in the research of ventilation and indoor air science. With a 1000-10,000 times increase in computer hardware capability in the past 20 years, CFD has become an integral part of scientific research and engineering development of complex air distribution and ventilation systems in buildings. This review discusses the major and specific challenges of CFD in terms of turbulence modelling, numerical approximation, and boundary conditions relevant to building ventilation. We emphasize the growing need for CFD verification and validation, suggest ongoing needs for analytical and experimental methods to support the numerical solutions, and discuss the growing capacity of CFD in opening up new research areas. We suggest that CFD has not become a replacement for experiment and theoretical analysis in ventilation research, rather it has become an increasingly important partner. We believe that an effective scientific approach for ventilation studies is still to combine experiments, theory, and CFD. We argue that CFD verification and validation are becoming more crucial than ever as more complex ventilation problems are solved. It is anticipated that ventilation problems at the city scale will be tackled by CFD in the next 10 years. © 2011 John Wiley & Sons A/S.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017MS%26E..192a2016P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017MS%26E..192a2016P"><span>Remote control system for high-perfomance computer simulation of crystal growth by the PFC method</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pavlyuk, Evgeny; Starodumov, Ilya; Osipov, Sergei</p> <p>2017-04-01</p> <p>Modeling of crystallization process by the phase field crystal method (PFC) - one of the important directions of modern computational materials science. In this paper, the practical side of the computer simulation of the crystallization process by the PFC method is investigated. To solve problems using this method, it is necessary to use high-performance computing clusters, data storage systems and other often expensive complex computer systems. Access to such resources is often limited, unstable and accompanied by various administrative problems. In addition, the variety of software and settings of different computing clusters sometimes does not allow researchers to use unified program code. There is a need to adapt the program code for each configuration of the computer complex. The practical experience of the authors has shown that the creation of a special control system for computing with the possibility of remote use can greatly simplify the implementation of simulations and increase the performance of scientific research. In current paper we show the principal idea of such a system and justify its efficiency.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JPhCS.657a1001V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JPhCS.657a1001V"><span>FOREWORD: 5th International Workshop on New Computational Methods for Inverse Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vourc'h, Eric; Rodet, Thomas</p> <p>2015-11-01</p> <p>This volume of Journal of Physics: Conference Series is dedicated to the scientific research presented during the 5th International Workshop on New Computational Methods for Inverse Problems, NCMIP 2015 (http://complement.farman.ens-cachan.fr/NCMIP_2015.html). This workshop took place at Ecole Normale Supérieure de Cachan, on May 29, 2015. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of ValueTools Conference, in May 2011, and secondly at the initiative of Institut Farman, in May 2012, May 2013 and May 2014. The New Computational Methods for Inverse Problems (NCMIP) workshop focused on recent advances in the resolution of inverse problems. Indeed, inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, Kernel methods, learning methods, convex optimization, free discontinuity problems, metamodels, proper orthogonal decomposition, reduced models for the inversion, non-linear inverse scattering, image reconstruction and restoration, and applications (bio-medical imaging, non-destructive evaluation...). NCMIP 2015 was a one-day workshop held in May 2015 which attracted around 70 attendees. Each of the submitted papers has been reviewed by two reviewers. There have been 15 accepted papers. In addition, three international speakers were invited to present a longer talk. The workshop was supported by Institut Farman (ENS Cachan, CNRS) and endorsed by the following French research networks: GDR ISIS, GDR MIA, GDR MOA and GDR Ondes. The program committee acknowledges the following research laboratories: CMLA, LMT, LURPA and SATIE.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/111091-st-catherine-sea-turtle-conservation-program-modeling-effective-science-education-through-conservation-research-partnerships','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/111091-st-catherine-sea-turtle-conservation-program-modeling-effective-science-education-through-conservation-research-partnerships"><span>The St. Catherine`s sea turtle conservation program: Modeling effective science education through conservation and research partnerships</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>March, N.B.; Bishop, G.</p> <p>1994-12-31</p> <p>Georgia school teachers served eight to ten day internships as research colleagues on St. Catherine`s island, Georgia. Interns monitored daily nesting activity, evaluated possible nests, validated egg chambers, screened the nests, and monitored each nest daily and assessed hatching success by excavation upon emergence of hatchlings. The real-world, hands-on holistic field experience immersed school teachers in the problems of executing a natural history conservation project integrating scientific content and methodology, mathematical analysis, and computer documentation. Outcomes included increased scientific inquiry, reduced science anxiety, heightened self-confidence, and enhanced credibility with students and colleagues. This educational model is applicable to many areasmore » and problems.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/3760366','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/3760366"><span>Cardiology office computer use: primer, pointers, pitfalls.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Shepard, R B; Blum, R I</p> <p>1986-10-01</p> <p>An office computer is a utility, like an automobile, with benefits and costs that are both direct and hidden and potential for disaster. For the cardiologist or cardiovascular surgeon, the increasing power and decreasing costs of computer hardware and the availability of software make use of an office computer system an increasingly attractive possibility. Management of office business functions is common; handling and scientific analysis of practice medical information are less common. The cardiologist can also access national medical information systems for literature searches and for interactive further education. Selection and testing of programs and the entire computer system before purchase of computer hardware will reduce the chances of disappointment or serious problems. Personnel pretraining and planning for office information flow and medical information security are necessary. Some cardiologists design their own office systems, buy hardware and software as needed, write programs for themselves and carry out the implementation themselves. For most cardiologists, the better course will be to take advantage of the professional experience of expert advisors. This article provides a starting point from which the practicing cardiologist can approach considering, specifying or implementing an office computer system for business functions and for scientific analysis of practice results.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/7008093-parallel-block-schemes-large-scale-least-squares-computations','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/7008093-parallel-block-schemes-large-scale-least-squares-computations"><span>Parallel block schemes for large scale least squares computations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Golub, G.H.; Plemmons, R.J.; Sameh, A.</p> <p>1986-04-01</p> <p>Large scale least squares computations arise in a variety of scientific and engineering problems, including geodetic adjustments and surveys, medical image analysis, molecular structures, partial differential equations and substructuring methods in structural engineering. In each of these problems, matrices often arise which possess a block structure which reflects the local connection nature of the underlying physical problem. For example, such super-large nonlinear least squares computations arise in geodesy. Here the coordinates of positions are calculated by iteratively solving overdetermined systems of nonlinear equations by the Gauss-Newton method. The US National Geodetic Survey will complete this year (1986) the readjustment ofmore » the North American Datum, a problem which involves over 540 thousand unknowns and over 6.5 million observations (equations). The observation matrix for these least squares computations has a block angular form with 161 diagnonal blocks, each containing 3 to 4 thousand unknowns. In this paper parallel schemes are suggested for the orthogonal factorization of matrices in block angular form and for the associated backsubstitution phase of the least squares computations. In addition, a parallel scheme for the calculation of certain elements of the covariance matrix for such problems is described. It is shown that these algorithms are ideally suited for multiprocessors with three levels of parallelism such as the Cedar system at the University of Illinois. 20 refs., 7 figs.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN51B1816P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN51B1816P"><span>SciSpark's SRDD : A Scientific Resilient Distributed Dataset for Multidimensional Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Palamuttam, R. S.; Wilson, B. D.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; McGibbney, L. J.; Ramirez, P.</p> <p>2015-12-01</p> <p>Remote sensing data and climate model output are multi-dimensional arrays of massive sizes locked away in heterogeneous file formats (HDF5/4, NetCDF 3/4) and metadata models (HDF-EOS, CF) making it difficult to perform multi-stage, iterative science processing since each stage requires writing and reading data to and from disk. We have developed SciSpark, a robust Big Data framework, that extends ApacheTM Spark for scaling scientific computations. Apache Spark improves the map-reduce implementation in ApacheTM Hadoop for parallel computing on a cluster, by emphasizing in-memory computation, "spilling" to disk only as needed, and relying on lazy evaluation. Central to Spark is the Resilient Distributed Dataset (RDD), an in-memory distributed data structure that extends the functional paradigm provided by the Scala programming language. However, RDDs are ideal for tabular or unstructured data, and not for highly dimensional data. The SciSpark project introduces the Scientific Resilient Distributed Dataset (sRDD), a distributed-computing array structure which supports iterative scientific algorithms for multidimensional data. SciSpark processes data stored in NetCDF and HDF files by partitioning them across time or space and distributing the partitions among a cluster of compute nodes. We show usability and extensibility of SciSpark by implementing distributed algorithms for geospatial operations on large collections of multi-dimensional grids. In particular we address the problem of scaling an automated method for finding Mesoscale Convective Complexes. SciSpark provides a tensor interface to support the pluggability of different matrix libraries. We evaluate performance of the various matrix libraries in distributed pipelines, such as Nd4jTM and BreezeTM. We detail the architecture and design of SciSpark, our efforts to integrate climate science algorithms, parallel ingest and partitioning (sharding) of A-Train satellite observations from model grids. These solutions are encompassed in SciSpark, an open-source software framework for distributed computing on scientific data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFMDI42A..07M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFMDI42A..07M"><span>Simulating Hydrologic Flow and Reactive Transport with PFLOTRAN and PETSc on Emerging Fine-Grained Parallel Computer Architectures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mills, R. T.; Rupp, K.; Smith, B. F.; Brown, J.; Knepley, M.; Zhang, H.; Adams, M.; Hammond, G. E.</p> <p>2017-12-01</p> <p>As the high-performance computing community pushes towards the exascale horizon, power and heat considerations have driven the increasing importance and prevalence of fine-grained parallelism in new computer architectures. High-performance computing centers have become increasingly reliant on GPGPU accelerators and "manycore" processors such as the Intel Xeon Phi line, and 512-bit SIMD registers have even been introduced in the latest generation of Intel's mainstream Xeon server processors. The high degree of fine-grained parallelism and more complicated memory hierarchy considerations of such "manycore" processors present several challenges to existing scientific software. Here, we consider how the massively parallel, open-source hydrologic flow and reactive transport code PFLOTRAN - and the underlying Portable, Extensible Toolkit for Scientific Computation (PETSc) library on which it is built - can best take advantage of such architectures. We will discuss some key features of these novel architectures and our code optimizations and algorithmic developments targeted at them, and present experiences drawn from working with a wide range of PFLOTRAN benchmark problems on these architectures.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA115440','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA115440"><span>A History of the ARPANET: The First Decade</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1981-04-01</p> <p>10 2.2 Major Technical Problems and Approaches 11-12 2.3 Major Changes in Objectives and Approaches 11-19 3, ,3 Scientific and Technical Results and...successful projects ever undertaken by DARPA! The program has initiated extensive changes in the Defense Department’s use of computers as well as in...the ARPANET project represents a similarly far-reaching change in the use of computers by mankind. The full impact of the technical changes set in</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006IJMPC..17..805F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006IJMPC..17..805F"><span>Nuclear Fuel Depletion Analysis Using Matlab Software</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Faghihi, F.; Nematollahi, M. R.</p> <p></p> <p>Coupled first order IVPs are frequently used in many parts of engineering and sciences. In this article, we presented a code including three computer programs which are joint with the Matlab software to solve and plot the solutions of the first order coupled stiff or non-stiff IVPs. Some engineering and scientific problems related to IVPs are given and fuel depletion (production of the 239Pu isotope) in a Pressurized Water Nuclear Reactor (PWR) are computed by the present code.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26968893','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26968893"><span>From the desktop to the grid: scalable bioinformatics via workflow conversion.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>de la Garza, Luis; Veit, Johannes; Szolek, Andras; Röttig, Marc; Aiche, Stephan; Gesing, Sandra; Reinert, Knut; Kohlbacher, Oliver</p> <p>2016-03-12</p> <p>Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free -an aspect that could potentially drive away members of the scientific community. We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_7");'>7</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li class="active"><span>9</span></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_9 --> <div id="page_10" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="181"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA375082','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA375082"><span>USSR and Eastern Europe Scientific Abstracts, Engineering and Equipment. Number 25.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1976-10-29</p> <p>is necessary to consider the problem of diffraction at a_cylindrical cavity. Some methods of solving this problem become very un- wieldy, when...applied to such a cavity of large wave dimensions, even with the aid of a digital computer. In the simpler Watson method , the series represent- ing the...potential of cylindrical waves is transformed to an integral in the complex plane and evaluated as the sum of residues. A difficulty in this method</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014EGUGA..16.3114F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014EGUGA..16.3114F"><span>Bringing Legacy Visualization Software to Modern Computing Devices via Application Streaming</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Fisher, Ward</p> <p>2014-05-01</p> <p>Planning software compatibility across forthcoming generations of computing platforms is a problem commonly encountered in software engineering and development. While this problem can affect any class of software, data analysis and visualization programs are particularly vulnerable. This is due in part to their inherent dependency on specialized hardware and computing environments. A number of strategies and tools have been designed to aid software engineers with this task. While generally embraced by developers at 'traditional' software companies, these methodologies are often dismissed by the scientific software community as unwieldy, inefficient and unnecessary. As a result, many important and storied scientific software packages can struggle to adapt to a new computing environment; for example, one in which much work is carried out on sub-laptop devices (such as tablets and smartphones). Rewriting these packages for a new platform often requires significant investment in terms of development time and developer expertise. In many cases, porting older software to modern devices is neither practical nor possible. As a result, replacement software must be developed from scratch, wasting resources better spent on other projects. Enabled largely by the rapid rise and adoption of cloud computing platforms, 'Application Streaming' technologies allow legacy visualization and analysis software to be operated wholly from a client device (be it laptop, tablet or smartphone) while retaining full functionality and interactivity. It mitigates much of the developer effort required by other more traditional methods while simultaneously reducing the time it takes to bring the software to a new platform. This work will provide an overview of Application Streaming and how it compares against other technologies which allow scientific visualization software to be executed from a remote computer. We will discuss the functionality and limitations of existing application streaming frameworks and how a developer might prepare their software for application streaming. We will also examine the secondary benefits realized by moving legacy software to the cloud. Finally, we will examine the process by which a legacy Java application, the Integrated Data Viewer (IDV), is to be adapted for tablet computing via Application Streaming.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JPhCS.454a2075S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JPhCS.454a2075S"><span>The challenges of developing computational physics: the case of South Africa</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Salagaram, T.; Chetty, N.</p> <p>2013-08-01</p> <p>Most modern scientific research problems are complex and interdisciplinary in nature. It is impossible to study such problems in detail without the use of computation in addition to theory and experiment. Although it is widely agreed that students should be introduced to computational methods at the undergraduate level, it remains a challenge to do this in a full traditional undergraduate curriculum. In this paper, we report on a survey that we conducted of undergraduate physics curricula in South Africa to determine the content and the approach taken in the teaching of computational physics. We also considered the pedagogy of computational physics at the postgraduate and research levels at various South African universities, research facilities and institutions. We conclude that the state of computational physics training in South Africa, especially at the undergraduate teaching level, is generally weak and needs to be given more attention at all universities. Failure to do so will impact negatively on the countrys capacity to grow its endeavours generally in the field of computational sciences, with negative impacts on research, and in commerce and industry.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950020946','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950020946"><span>NAS Technical Summaries, March 1993 - February 1994</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1995-01-01</p> <p>NASA created the Numerical Aerodynamic Simulation (NAS) Program in 1987 to focus resources on solving critical problems in aeroscience and related disciplines by utilizing the power of the most advanced supercomputers available. The NAS Program provides scientists with the necessary computing power to solve today's most demanding computational fluid dynamics problems and serves as a pathfinder in integrating leading-edge supercomputing technologies, thus benefitting other supercomputer centers in government and industry. The 1993-94 operational year concluded with 448 high-speed processor projects and 95 parallel projects representing NASA, the Department of Defense, other government agencies, private industry, and universities. This document provides a glimpse at some of the significant scientific results for the year.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940023261','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940023261"><span>NAS technical summaries. Numerical aerodynamic simulation program, March 1992 - February 1993</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1994-01-01</p> <p>NASA created the Numerical Aerodynamic Simulation (NAS) Program in 1987 to focus resources on solving critical problems in aeroscience and related disciplines by utilizing the power of the most advanced supercomputers available. The NAS Program provides scientists with the necessary computing power to solve today's most demanding computational fluid dynamics problems and serves as a pathfinder in integrating leading-edge supercomputing technologies, thus benefitting other supercomputer centers in government and industry. The 1992-93 operational year concluded with 399 high-speed processor projects and 91 parallel projects representing NASA, the Department of Defense, other government agencies, private industry, and universities. This document provides a glimpse at some of the significant scientific results for the year.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940029308','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940029308"><span>Mass storage: The key to success in high performance computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, Richard R.</p> <p>1993-01-01</p> <p>There are numerous High Performance Computing & Communications Initiatives in the world today. All are determined to help solve some 'Grand Challenges' type of problem, but each appears to be dominated by the pursuit of higher and higher levels of CPU performance and interconnection bandwidth as the approach to success, without any regard to the impact of Mass Storage. My colleagues and I at Data Storage Technologies believe that all will have their performance against their goals ultimately measured by their ability to efficiently store and retrieve the 'deluge of data' created by end-users who will be using these systems to solve Scientific Grand Challenges problems, and that the issue of Mass Storage will become then the determinant of success or failure in achieving each projects goals. In today's world of High Performance Computing and Communications (HPCC), the critical path to success in solving problems can only be traveled by designing and implementing Mass Storage Systems capable of storing and manipulating the truly 'massive' amounts of data associated with solving these challenges. Within my presentation I will explore this critical issue and hypothesize solutions to this problem.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23567157','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23567157"><span>The computationalist reformulation of the mind-body problem.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Marchal, Bruno</p> <p>2013-09-01</p> <p>Computationalism, or digital mechanism, or simply mechanism, is a hypothesis in the cognitive science according to which we can be emulated by a computer without changing our private subjective feeling. We provide a weaker form of that hypothesis, weaker than the one commonly referred to in the (vast) literature and show how to recast the mind-body problem in that setting. We show that such a mechanist hypothesis does not solve the mind-body problem per se, but does help to reduce partially the mind-body problem into another problem which admits a formulation in pure arithmetic. We will explain that once we adopt the computationalist hypothesis, which is a form of mechanist assumption, we have to derive from it how our belief in the physical laws can emerge from *only* arithmetic and classical computer science. In that sense we reduce the mind-body problem to a body problem appearance in computer science, or in arithmetic. The general shape of the possible solution of that subproblem, if it exists, is shown to be closer to "Platonist or neoplatonist theology" than to the "Aristotelian theology". In Plato's theology, the physical or observable reality is only the shadow of a vaster hidden nonphysical and nonobservable, perhaps mathematical, reality. The main point is that the derivation is constructive, and it provides the technical means to derive physics from arithmetic, and this will make the computationalist hypothesis empirically testable, and thus scientific in the Popperian analysis of science. In case computationalism is wrong, the derivation leads to a procedure for measuring "our local degree of noncomputationalism". Copyright © 2013 Elsevier Ltd. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1296665','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1296665"><span>Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.</p> <p></p> <p>It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1296665-large-scale-optimization-based-non-negative-computational-framework-diffusion-equations-parallel-implementation-performance-studies','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1296665-large-scale-optimization-based-non-negative-computational-framework-diffusion-equations-parallel-implementation-performance-studies"><span>Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.</p> <p>2016-07-26</p> <p>It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFMDI31A2208S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFMDI31A2208S"><span>TerraFERMA: The Transparent Finite Element Rapid Model Assembler for multi-physics problems in the solid Earth sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Spiegelman, M. W.; Wilson, C. R.; Van Keken, P. E.</p> <p>2013-12-01</p> <p>We announce the release of a new software infrastructure, TerraFERMA, the Transparent Finite Element Rapid Model Assembler for the exploration and solution of coupled multi-physics problems. The design of TerraFERMA is driven by two overarching computational needs in Earth sciences. The first is the need for increased flexibility in both problem description and solution strategies for coupled problems where small changes in model assumptions can often lead to dramatic changes in physical behavior. The second is the need for software and models that are more transparent so that results can be verified, reproduced and modified in a manner such that the best ideas in computation and earth science can be more easily shared and reused. TerraFERMA leverages three advanced open-source libraries for scientific computation that provide high level problem description (FEniCS), composable solvers for coupled multi-physics problems (PETSc) and a science neutral options handling system (SPuD) that allows the hierarchical management of all model options. TerraFERMA integrates these libraries into an easier to use interface that organizes the scientific and computational choices required in a model into a single options file, from which a custom compiled application is generated and run. Because all models share the same infrastructure, models become more reusable and reproducible. TerraFERMA inherits much of its functionality from the underlying libraries. It currently solves partial differential equations (PDE) using finite element methods on simplicial meshes of triangles (2D) and tetrahedra (3D). The software is particularly well suited for non-linear problems with complex coupling between components. We demonstrate the design and utility of TerraFERMA through examples of thermal convection and magma dynamics. TerraFERMA has been tested successfully against over 45 benchmark problems from 7 publications in incompressible and compressible convection, magmatic solitary waves and Stokes flow with free surfaces. We have been using it extensively for research in basic magma dynamics, fluid flow in subduction zones and reactive cracking in poro-elastic materials. TerraFERMA is open-source and available as a git repository at bitbucket.org/tferma/tferma and through CIG. Instability of a 1-D magmatic solitary wave to spherical 3D waves calculated using TerraFERMA</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008NJPh...10l5005S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008NJPh...10l5005S"><span>Visualizing a silicon quantum computer</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sanders, Barry C.; Hollenberg, Lloyd C. L.; Edmundson, Darran; Edmundson, Andrew</p> <p>2008-12-01</p> <p>Quantum computation is a fast-growing, multi-disciplinary research field. The purpose of a quantum computer is to execute quantum algorithms that efficiently solve computational problems intractable within the existing paradigm of 'classical' computing built on bits and Boolean gates. While collaboration between computer scientists, physicists, chemists, engineers, mathematicians and others is essential to the project's success, traditional disciplinary boundaries can hinder progress and make communicating the aims of quantum computing and future technologies difficult. We have developed a four minute animation as a tool for representing, understanding and communicating a silicon-based solid-state quantum computer to a variety of audiences, either as a stand-alone animation to be used by expert presenters or embedded into a longer movie as short animated sequences. The paper includes a generally applicable recipe for successful scientific animation production.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/760038','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/760038"><span>Instruction-Level Characterization of Scientific Computing Applications Using Hardware Performance Counters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Luo, Y.; Cameron, K.W.</p> <p>1998-11-24</p> <p>Workload characterization has been proven an essential tool to architecture design and performance evaluation in both scientific and commercial computing areas. Traditional workload characterization techniques include FLOPS rate, cache miss ratios, CPI (cycles per instruction or IPC, instructions per cycle) etc. With the complexity of sophisticated modern superscalar microprocessors, these traditional characterization techniques are not powerful enough to pinpoint the performance bottleneck of an application on a specific microprocessor. They are also incapable of immediately demonstrating the potential performance benefit of any architectural or functional improvement in a new processor design. To solve these problems, many people rely on simulators,more » which have substantial constraints especially on large-scale scientific computing applications. This paper presents a new technique of characterizing applications at the instruction level using hardware performance counters. It has the advantage of collecting instruction-level characteristics in a few runs virtually without overhead or slowdown. A variety of instruction counts can be utilized to calculate some average abstract workload parameters corresponding to microprocessor pipelines or functional units. Based on the microprocessor architectural constraints and these calculated abstract parameters, the architectural performance bottleneck for a specific application can be estimated. In particular, the analysis results can provide some insight to the problem that only a small percentage of processor peak performance can be achieved even for many very cache-friendly codes. Meanwhile, the bottleneck estimation can provide suggestions about viable architectural/functional improvement for certain workloads. Eventually, these abstract parameters can lead to the creation of an analytical microprocessor pipeline model and memory hierarchy model.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED562222.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED562222.pdf"><span>Using Generic and Context-Specific Scaffolding to Support Authentic Science Inquiry</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Belland, Brian R.; Gu, Jiangyue; Armbrust, Sara; Cook, Brant</p> <p>2013-01-01</p> <p>In this conceptual paper, we propose an heuristic to balance context-specific and generic scaffolding, as well as computer-based and teacher scaffolding, during instruction centered on authentic, scientific problems. This paper is novel in that many researchers ask a dichotomous question of whether generic or context-specific scaffolding is best,…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=TYPES+AND+OF+AND+DISABILITY&pg=5&id=EJ1016621','ERIC'); return false;" href="https://eric.ed.gov/?q=TYPES+AND+OF+AND+DISABILITY&pg=5&id=EJ1016621"><span>Not All Created Equally: Exploring Calculator Use by Students with Mild Intellectual Disability</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Yakubova, Gulnoza; Bouck, Emily C.</p> <p>2014-01-01</p> <p>Calculators are widely used in mathematics education, yet limited research examines the effects of calculators for students with mild intellectual disability. An alternating treatments design was used to study the effects of calculator types (i.e., scientific and graphing) on the mathematical performance (i.e., computation and word problems) of…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010APS..MARJ42014I','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010APS..MARJ42014I"><span>Computer Based Collaborative Problem Solving for Introductory Courses in Physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ilie, Carolina; Lee, Kevin</p> <p>2010-03-01</p> <p>We discuss collaborative problem solving computer-based recitation style. The course is designed by Lee [1], and the idea was proposed before by Christian, Belloni and Titus [2,3]. The students find the problems on a web-page containing simulations (physlets) and they write the solutions on an accompanying worksheet after discussing it with a classmate. Physlets have the advantage of being much more like real-world problems than textbook problems. We also compare two protocols for web-based instruction using simulations in an introductory physics class [1]. The inquiry protocol allowed students to control input parameters while the worked example protocol did not. We will discuss which of the two methods is more efficient in relation to Scientific Discovery Learning and Cognitive Load Theory. 1. Lee, Kevin M., Nicoll, Gayle and Brooks, Dave W. (2004). ``A Comparison of Inquiry and Worked Example Web-Based Instruction Using Physlets'', Journal of Science Education and Technology 13, No. 1: 81-88. 2. Christian, W., and Belloni, M. (2001). Physlets: Teaching Physics With Interactive Curricular Material, Prentice Hall, Englewood Cliffs, NJ. 3. Christian,W., and Titus,A. (1998). ``Developing web-based curricula using Java Physlets.'' Computers in Physics 12: 227--232.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1198062-improved-parallel-hashed-oct-tree-body-algorithm-cosmological-simulation','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1198062-improved-parallel-hashed-oct-tree-body-algorithm-cosmological-simulation"><span>2HOT: An Improved Parallel Hashed Oct-Tree N-Body Algorithm for Cosmological Simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Warren, Michael S.</p> <p>2014-01-01</p> <p>We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (2 18 ) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (4096 3 ) particle cosmological simulations, accounting for 4×10 20 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy andmore » scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29183935','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29183935"><span>Computer modeling in developmental biology: growing today, essential tomorrow.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sharpe, James</p> <p>2017-12-01</p> <p>D'Arcy Thompson was a true pioneer, applying mathematical concepts and analyses to the question of morphogenesis over 100 years ago. The centenary of his famous book, On Growth and Form , is therefore a great occasion on which to review the types of computer modeling now being pursued to understand the development of organs and organisms. Here, I present some of the latest modeling projects in the field, covering a wide range of developmental biology concepts, from molecular patterning to tissue morphogenesis. Rather than classifying them according to scientific question, or scale of problem, I focus instead on the different ways that modeling contributes to the scientific process and discuss the likely future of modeling in developmental biology. © 2017. Published by The Company of Biologists Ltd.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1417653','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1417653"><span>Crosscut report: Exascale Requirements Reviews, March 9–10, 2017 – Tysons Corner, Virginia. An Office of Science review sponsored by: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Gerber, Richard; Hack, James; Riley, Katherine</p> <p></p> <p>The mission of the U.S. Department of Energy Office of Science (DOE SC) is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and national security missions of the United States. To achieve these goals in today’s world requires investments in not only the traditional scientific endeavors of theory and experiment, but also in computational science and the facilities that support large-scale simulation and data analysis. The Advanced Scientific Computing Research (ASCR) program addresses these challenges in the Office of Science. ASCR’s mission is to discover, develop, andmore » deploy computational and networking capabilities to analyze, model, simulate, and predict complex phenomena important to DOE. ASCR supports research in computational science, three high-performance computing (HPC) facilities — the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory and Leadership Computing Facilities at Argonne (ALCF) and Oak Ridge (OLCF) National Laboratories — and the Energy Sciences Network (ESnet) at Berkeley Lab. ASCR is guided by science needs as it develops research programs, computers, and networks at the leading edge of technologies. As we approach the era of exascale computing, technology changes are creating challenges for science programs in SC for those who need to use high performance computing and data systems effectively. Numerous significant modifications to today’s tools and techniques will be needed to realize the full potential of emerging computing systems and other novel computing architectures. To assess these needs and challenges, ASCR held a series of Exascale Requirements Reviews in 2015–2017, one with each of the six SC program offices,1 and a subsequent Crosscut Review that sought to integrate the findings from each. Participants at the reviews were drawn from the communities of leading domain scientists, experts in computer science and applied mathematics, ASCR facility staff, and DOE program managers in ASCR and the respective program offices. The purpose of these reviews was to identify mission-critical scientific problems within the DOE Office of Science (including experimental facilities) and determine the requirements for the exascale ecosystem that would be needed to address those challenges. The exascale ecosystem includes exascale computing systems, high-end data capabilities, efficient software at scale, libraries, tools, and other capabilities. This effort will contribute to the development of a strategic roadmap for ASCR compute and data facility investments and will help the ASCR Facility Division establish partnerships with Office of Science stakeholders. It will also inform the Office of Science research needs and agenda. The results of the six reviews have been published in reports available on the web at http://exascaleage.org/. This report presents a summary of the individual reports and of common and crosscutting findings, and it identifies opportunities for productive collaborations among the DOE SC program offices.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AGUFM.H14H..07P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AGUFM.H14H..07P"><span>Identifying Key Features, Cutting Edge Cloud Resources, and Artificial Intelligence Tools to Achieve User-Friendly Water Science in the Cloud</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pierce, S. A.</p> <p>2017-12-01</p> <p>Decision making for groundwater systems is becoming increasingly important, as shifting water demands increasingly impact aquifers. As buffer systems, aquifers provide room for resilient responses and augment the actual timeframe for hydrological response. Yet the pace impacts, climate shifts, and degradation of water resources is accelerating. To meet these new drivers, groundwater science is transitioning toward the emerging field of Integrated Water Resources Management, or IWRM. IWRM incorporates a broad array of dimensions, methods, and tools to address problems that tend to be complex. Computational tools and accessible cyberinfrastructure (CI) are needed to cross the chasm between science and society. Fortunately cloud computing environments, such as the new Jetstream system, are evolving rapidly. While still targeting scientific user groups systems such as, Jetstream, offer configurable cyberinfrastructure to enable interactive computing and data analysis resources on demand. The web-based interfaces allow researchers to rapidly customize virtual machines, modify computing architecture and increase the usability and access for broader audiences to advanced compute environments. The result enables dexterous configurations and opening up opportunities for IWRM modelers to expand the reach of analyses, number of case studies, and quality of engagement with stakeholders and decision makers. The acute need to identify improved IWRM solutions paired with advanced computational resources refocuses the attention of IWRM researchers on applications, workflows, and intelligent systems that are capable of accelerating progress. IWRM must address key drivers of community concern, implement transdisciplinary methodologies, adapt and apply decision support tools in order to effectively support decisions about groundwater resource management. This presentation will provide an overview of advanced computing services in the cloud using integrated groundwater management case studies to highlight how Cloud CI streamlines the process for setting up an interactive decision support system. Moreover, advances in artificial intelligence offer new techniques for old problems from integrating data to adaptive sensing or from interactive dashboards to optimizing multi-attribute problems. The combination of scientific expertise, flexible cloud computing solutions, and intelligent systems opens new research horizons.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA230543','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA230543"><span>Stability Analysis of Finite Difference Approximations to Hyperbolic Systems,and Problems in Applied and Computational Matrix and Operator Theory</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1990-12-07</p> <p>Fundaqao Calouste Gulbenkian, Instituto Gulbenkian de Ci~ncia, Centro de C6lculo Cientifico , Coimbra, 1973. 28, Dirac, P. A. M., Spinors in Hilbert Space...Office of Scientific Research grants 1965 Mathematical Association of America Editorial Prize for the article entitled: "Linear Transformations on...matrices" 1966 L.R. Ford Memorial Prize awarded by the Mathematical Association of America for the article , "Permanents" 1989 Outstanding Computer</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_8");'>8</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li class="active"><span>10</span></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_10 --> <div id="page_11" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="201"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018MS%26E..363a2035K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018MS%26E..363a2035K"><span>Video control system for a drilling in furniture workpiece</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Khmelev, V. L.; Satarov, R. N.; Zavyalova, K. V.</p> <p>2018-05-01</p> <p>During last 5 years, Russian industry has being starting to be a robotic, therefore scientific groups got new tasks. One of new tasks is machine vision systems, which should solve problem of automatic quality control. This type of systems has a cost of several thousand dollars each. The price is impossible for regional small business. In this article, we describe principle and algorithm of cheap video control system, which one uses web-cameras and notebook or desktop computer as a computing unit.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19920018943','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19920018943"><span>SPAN: Ocean science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Thomas, Valerie L.; Koblinsky, Chester J.; Webster, Ferris; Zlotnicki, Victor; Green, James L.</p> <p>1987-01-01</p> <p>The Space Physics Analysis Network (SPAN) is a multi-mission, correlative data comparison network which links space and Earth science research and data analysis computers. It provides a common working environment for sharing computer resources, sharing computer peripherals, solving proprietary problems, and providing the potential for significant time and cost savings for correlative data analysis. This is one of a series of discipline-specific SPAN documents which are intended to complement the SPAN primer and SPAN Management documents. Their purpose is to provide the discipline scientists with a comprehensive set of documents to assist in the use of SPAN for discipline specific scientific research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70028460','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70028460"><span>Enabling scientific workflows in virtual reality</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Kreylos, O.; Bawden, G.; Bernardin, T.; Billen, M.I.; Cowgill, E.S.; Gold, R.D.; Hamann, B.; Jadamec, M.; Kellogg, L.H.; Staadt, O.G.; Sumner, D.Y.</p> <p>2006-01-01</p> <p>To advance research and improve the scientific return on data collection and interpretation efforts in the geosciences, we have developed methods of interactive visualization, with a special focus on immersive virtual reality (VR) environments. Earth sciences employ a strongly visual approach to the measurement and analysis of geologic data due to the spatial and temporal scales over which such data ranges, As observations and simulations increase in size and complexity, the Earth sciences are challenged to manage and interpret increasing amounts of data. Reaping the full intellectual benefits of immersive VR requires us to tailor exploratory approaches to scientific problems. These applications build on the visualization method's strengths, using both 3D perception and interaction with data and models, to take advantage of the skills and training of the geological scientists exploring their data in the VR environment. This interactive approach has enabled us to develop a suite of tools that are adaptable to a range of problems in the geosciences and beyond. Copyright ?? 2008 by the Association for Computing Machinery, Inc.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2011-07-13/pdf/2011-17647.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2011-07-13/pdf/2011-17647.pdf"><span>76 FR 41234 - Advanced Scientific Computing Advisory Committee Charter Renewal</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2011-07-13</p> <p>... Secretariat, General Services Administration, notice is hereby given that the Advanced Scientific Computing... advice and recommendations concerning the Advanced Scientific Computing program in response only to... Advanced Scientific Computing Research program and recommendations based thereon; --Advice on the computing...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3293204','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3293204"><span>Computational ecology as an emerging science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Petrovskii, Sergei; Petrovskaya, Natalia</p> <p>2012-01-01</p> <p>It has long been recognized that numerical modelling and computer simulations can be used as a powerful research tool to understand, and sometimes to predict, the tendencies and peculiarities in the dynamics of populations and ecosystems. It has been, however, much less appreciated that the context of modelling and simulations in ecology is essentially different from those that normally exist in other natural sciences. In our paper, we review the computational challenges arising in modern ecology in the spirit of computational mathematics, i.e. with our main focus on the choice and use of adequate numerical methods. Somewhat paradoxically, the complexity of ecological problems does not always require the use of complex computational methods. This paradox, however, can be easily resolved if we recall that application of sophisticated computational methods usually requires clear and unambiguous mathematical problem statement as well as clearly defined benchmark information for model validation. At the same time, many ecological problems still do not have mathematically accurate and unambiguous description, and available field data are often very noisy, and hence it can be hard to understand how the results of computations should be interpreted from the ecological viewpoint. In this scientific context, computational ecology has to deal with a new paradigm: conventional issues of numerical modelling such as convergence and stability become less important than the qualitative analysis that can be provided with the help of computational techniques. We discuss this paradigm by considering computational challenges arising in several specific ecological applications. PMID:23565336</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010IJMES..41..769D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010IJMES..41..769D"><span>A brief historical introduction to Euler's formula for polyhedra, topology, graph theory and networks</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Debnath, Lokenath</p> <p>2010-09-01</p> <p>This article is essentially devoted to a brief historical introduction to Euler's formula for polyhedra, topology, theory of graphs and networks with many examples from the real-world. Celebrated Königsberg seven-bridge problem and some of the basic properties of graphs and networks for some understanding of the macroscopic behaviour of real physical systems are included. We also mention some important and modern applications of graph theory or network problems from transportation to telecommunications. Graphs or networks are effectively used as powerful tools in industrial, electrical and civil engineering, communication networks in the planning of business and industry. Graph theory and combinatorics can be used to understand the changes that occur in many large and complex scientific, technical and medical systems. With the advent of fast large computers and the ubiquitous Internet consisting of a very large network of computers, large-scale complex optimization problems can be modelled in terms of graphs or networks and then solved by algorithms available in graph theory. Many large and more complex combinatorial problems dealing with the possible arrangements of situations of various kinds, and computing the number and properties of such arrangements can be formulated in terms of networks. The Knight's tour problem, Hamilton's tour problem, problem of magic squares, the Euler Graeco-Latin squares problem and their modern developments in the twentieth century are also included.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010hocc.book..493B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010hocc.book..493B"><span>HPC on Competitive Cloud Resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bientinesi, Paolo; Iakymchuk, Roman; Napper, Jeff</p> <p></p> <p>Computing as a utility has reached the mainstream. Scientists can now easily rent time on large commercial clusters that can be expanded and reduced on-demand in real-time. However, current commercial cloud computing performance falls short of systems specifically designed for scientific applications. Scientific computing needs are quite different from those of the web applications that have been the focus of cloud computing vendors. In this chapter we demonstrate through empirical evaluation the computational efficiency of high-performance numerical applications in a commercial cloud environment when resources are shared under high contention. Using the Linpack benchmark as a case study, we show that cache utilization becomes highly unpredictable and similarly affects computation time. For some problems, not only is it more efficient to underutilize resources, but the solution can be reached sooner in realtime (wall-time). We also show that the smallest, cheapest (64-bit) instance on the studied environment is the best for price to performance ration. In light of the high-contention we witness, we believe that alternative definitions of efficiency for commercial cloud environments should be introduced where strong performance guarantees do not exist. Concepts like average, expected performance and execution time, expected cost to completion, and variance measures--traditionally ignored in the high-performance computing context--now should complement or even substitute the standard definitions of efficiency.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JPhCS.542a1001.','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JPhCS.542a1001."><span>FOREWORD: 4th International Workshop on New Computational Methods for Inverse Problems (NCMIP2014)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p></p> <p>2014-10-01</p> <p>This volume of Journal of Physics: Conference Series is dedicated to the scientific contributions presented during the 4th International Workshop on New Computational Methods for Inverse Problems, NCMIP 2014 (http://www.farman.ens-cachan.fr/NCMIP_2014.html). This workshop took place at Ecole Normale Supérieure de Cachan, on May 23, 2014. The prior editions of NCMIP also took place in Cachan, France, firstly within the scope of ValueTools Conference, in May 2011 (http://www.ncmip.org/2011/), and secondly at the initiative of Institut Farman, in May 2012 and May 2013, (http://www.farman.ens-cachan.fr/NCMIP_2012.html), (http://www.farman.ens-cachan.fr/NCMIP_2013.html). The New Computational Methods for Inverse Problems (NCMIP) Workshop focused on recent advances in the resolution of inverse problems. Indeed, inverse problems appear in numerous scientific areas such as geophysics, biological and medical imaging, material and structure characterization, electrical, mechanical and civil engineering, and finances. The resolution of inverse problems consists of estimating the parameters of the observed system or structure from data collected by an instrumental sensing or imaging device. Its success firstly requires the collection of relevant observation data. It also requires accurate models describing the physical interactions between the instrumental device and the observed system, as well as the intrinsic properties of the solution itself. Finally, it requires the design of robust, accurate and efficient inversion algorithms. Advanced sensor arrays and imaging devices provide high rate and high volume data; in this context, the efficient resolution of the inverse problem requires the joint development of new models and inversion methods, taking computational and implementation aspects into account. During this one-day workshop, researchers had the opportunity to bring to light and share new techniques and results in the field of inverse problems. The topics of the workshop were: algorithms and computational aspects of inversion, Bayesian estimation, Kernel methods, learning methods, convex optimization, free discontinuity problems, metamodels, proper orthogonal decomposition, reduced models for the inversion, non-linear inverse scattering, image reconstruction and restoration, and applications (bio-medical imaging, non-destructive evaluation...). NCMIP 2014 was a one-day workshop held in May 2014 which attracted around sixty attendees. Each of the submitted papers has been reviewed by two reviewers. There have been nine accepted papers. In addition, three international speakers were invited to present a longer talk. The workshop was supported by Institut Farman (ENS Cachan, CNRS) and endorsed by the following French research networks (GDR ISIS, GDR MIA, GDR MOA, GDR Ondes). The program committee acknowledges the following research laboratories: CMLA, LMT, LURPA, SATIE. Eric Vourc'h and Thomas Rodet</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/430735-discovery-causes-leprosy-computational-analysis','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/430735-discovery-causes-leprosy-computational-analysis"><span>The discovery of the causes of leprosy: A computational analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Corruble, V.; Ganascia, J.G.</p> <p>1996-12-31</p> <p>The role played by the inductive inference has been studied extensively in the field of Scientific Discovery. The work presented here tackles the problem of induction in medical research. The discovery of the causes of leprosy is analyzed and simulated using computational means. An inductive algorithm is proposed, which is successful in simulating some essential steps in the progress of the understanding of the disease. It also allows us to simulate the false reasoning of previous centuries through the introduction of some medical a priori inherited form archaic medicine. Corroborating previous research, this problem illustrates the importance of the socialmore » and cultural environment on the way the inductive inference is performed in medicine.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19740026205','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19740026205"><span>Apollo experience report: Apollo lunar surface experiments package data processing system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Eason, R. L.</p> <p>1974-01-01</p> <p>Apollo Program experience in the processing of scientific data from the Apollo lunar surface experiments package, in which computers and associated hardware and software were used, is summarized. The facility developed for the preprocessing of the lunar science data is described, as are several computer facilities and programs used by the Principal Investigators. The handling, processing, and analyzing of lunar science data and the interface with the Principal Investigators are discussed. Pertinent problems that arose in the development of the data processing schemes are discussed so that future programs may benefit from the solutions to the problems. The evolution of the data processing techniques for lunar science data related to recommendations for future programs of this type.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Experimental+AND+design&pg=3&id=EJ1037100','ERIC'); return false;" href="https://eric.ed.gov/?q=Experimental+AND+design&pg=3&id=EJ1037100"><span>Scaffolding a Complex Task of Experimental Design in Chemistry with a Computer Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Girault, Isabelle; d'Ham, Cédric</p> <p>2014-01-01</p> <p>When solving a scientific problem through experimentation, students may have the responsibility to design the experiment. When students work in a conventional condition, with paper and pencil, the designed procedures stay at a very general level. There is a need for additional scaffolds to help the students perform this complex task. We propose a…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=earth+AND+science&pg=5&id=EJ1157028','ERIC'); return false;" href="https://eric.ed.gov/?q=earth+AND+science&pg=5&id=EJ1157028"><span>Enhancing Student Engagement to Positively Impact Mathematics Anxiety, Confidence and Achievement for Interdisciplinary Science Subjects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Everingham, Yvette L.; Gyuris, Emma; Connolly, Sean R.</p> <p>2017-01-01</p> <p>Contemporary science educators must equip their students with the knowledge and practical know-how to connect multiple disciplines like mathematics, computing and the natural sciences to gain a richer and deeper understanding of a scientific problem. However, many biology and earth science students are prejudiced against mathematics due to…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/5342566-expert-systems-research','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/5342566-expert-systems-research"><span>Expert systems research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Duda, R.O.; Shortliffe, E.H.</p> <p>1983-04-15</p> <p>Artificial intelligence, long a topic of basic computer science research, is now being applied to problems of scientific, technical, and commercial interest. Some consultation programs although limited in versatility, have achieved levels of performance rivaling those of human experts. A collateral benefit of this work is the systematization of previously unformalized knowledge in areas such as medical diagnosis and geology. 30 references.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/AD1049575','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/AD1049575"><span>COOPERATIVE ROUTING FOR DYNAMIC AERIAL LAYER NETWORKS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>2018-03-01</p> <p>Advisor, Computing & Communications Division Information Directorate This report is published in the interest of scientific and technical...information accumulation at the physical layer, and study the cooperative routing and resource allocation problems associated with such SU networks...interference power constraint is studied . In [Shi2012Joint], an optimal power and sub-carrier allocation strategy to maximize SUs’ throughput subject to</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/9162847','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/9162847"><span>[Automation of medical literature--and information services].</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bakker, S</p> <p>1997-01-04</p> <p>It is important for clinical practice to be able to find (or retrieve) relevant literature and to keep informed of the state of medical science. The fact that the contents of articles in journals are now accessible via computers is the result of integration of bibliographic techniques, medical knowledge and computer technology. Articles published in some 5000 medical journals can nowadays be retrieved electronically via Medline and Embase together (but medical literature in Dutch is underrepresented). Computerised insertion of publications into Internet dose not make information traceable or accessible, let alone reliable and readable. It cannot be predicted if electronic versions of scientific periodicals will replace the printed editions completely. However, valuable, reliable information will always have its price, even on Internet. It is unlikely that electronic information published privately (internet) will replace scientific publishers soon, for readers will still want selection and monitoring of contents and language. Good layout, professional typography and suitable illustrations to enhance reading comfort and cognitive processes, will even become more important. The problems arising from the immensity of scientific knowledge are not (any longer) of a technological nature-what is needed is a cultural about-turn of the information infrastructure in medical-scientific associations, organizations and institutions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT.......432C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT.......432C"><span>A Computational Framework for Quantifying and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cioaca, Alexandru</p> <p></p> <p>A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data assimila- tion is a rigorous procedure to fuse information from a priori knowledge of the system state, the physical laws governing the evolution of the system, and real measurements, all with associated error statistics. Data assimilation produces best (a posteriori) estimates of model states and parameter values, and results in considerably improved computer simulations. The acquisition and use of observations in data assimilation raises several important scientific questions related to optimal sensor network design, quantification of data impact, pruning redundant data, and identifying the most beneficial additional observations. These questions originate in operational data assimilation practice, and have started to attract considerable interest in the recent past. This dissertation advances the state of knowledge in four dimensional variational (4D-Var) data assimilation by developing, implementing, and validating a novel computational framework for estimating observation impact and for optimizing sensor networks. The framework builds on the powerful methodologies of second-order adjoint modeling and the 4D-Var sensitivity equations. Efficient computational approaches for quantifying the observation impact include matrix free linear algebra algorithms and low-rank approximations of the sensitivities to observations. The sensor network configuration problem is formulated as a meta-optimization problem. Best values for parameters such as sensor location are obtained by optimizing a performance criterion, subject to the constraint posed by the 4D-Var optimization. Tractable computational solutions to this "optimization-constrained" optimization problem are provided. The results of this work can be directly applied to the deployment of intelligent sensors and adaptive observations, as well as to reducing the operating costs of measuring networks, while preserving their ability to capture the essential features of the system under consideration.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2011-06-02/pdf/2011-13658.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2011-06-02/pdf/2011-13658.pdf"><span>76 FR 31945 - Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2011-06-02</p> <p>... DEPARTMENT OF ENERGY Advanced Scientific Computing Advisory Committee AGENCY: Department of Energy... teleconference meeting of the Advanced Scientific Computing Advisory Committee (ASCAC). The Federal [email protected] . FOR FURTHER INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19860052137&hterms=novel+computation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dnovel%2Bcomputation','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19860052137&hterms=novel+computation&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3Dnovel%2Bcomputation"><span>The Caltech Concurrent Computation Program - Project description</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fox, G.; Otto, S.; Lyzenga, G.; Rogstad, D.</p> <p>1985-01-01</p> <p>The Caltech Concurrent Computation Program wwhich studies basic issues in computational science is described. The research builds on initial work where novel concurrent hardware, the necessary systems software to use it and twenty significant scientific implementations running on the initial 32, 64, and 128 node hypercube machines have been constructed. A major goal of the program will be to extend this work into new disciplines and more complex algorithms including general packages that decompose arbitrary problems in major application areas. New high-performance concurrent processors with up to 1024-nodes, over a gigabyte of memory and multigigaflop performance are being constructed. The implementations cover a wide range of problems in areas such as high energy and astrophysics, condensed matter, chemical reactions, plasma physics, applied mathematics, geophysics, simulation, CAD for VLSI, graphics and image processing. The products of the research program include the concurrent algorithms, hardware, systems software, and complete program implementations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26508827','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26508827"><span>Publication Bias in Methodological Computational Research.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Boulesteix, Anne-Laure; Stierle, Veronika; Hapfelmeier, Alexander</p> <p>2015-01-01</p> <p>The problem of publication bias has long been discussed in research fields such as medicine. There is a consensus that publication bias is a reality and that solutions should be found to reduce it. In methodological computational research, including cancer informatics, publication bias may also be at work. The publication of negative research findings is certainly also a relevant issue, but has attracted very little attention to date. The present paper aims at providing a new formal framework to describe the notion of publication bias in the context of methodological computational research, facilitate and stimulate discussions on this topic, and increase awareness in the scientific community. We report an exemplary pilot study that aims at gaining experiences with the collection and analysis of information on unpublished research efforts with respect to publication bias, and we outline the encountered problems. Based on these experiences, we try to formalize the notion of publication bias.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013JSEdT..22..952K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013JSEdT..22..952K"><span>Learning Relative Motion Concepts in Immersive and Non-immersive Virtual Environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kozhevnikov, Michael; Gurlitt, Johannes; Kozhevnikov, Maria</p> <p>2013-12-01</p> <p>The focus of the current study is to understand which unique features of an immersive virtual reality environment have the potential to improve learning relative motion concepts. Thirty-seven undergraduate students learned relative motion concepts using computer simulation either in immersive virtual environment (IVE) or non-immersive desktop virtual environment (DVE) conditions. Our results show that after the simulation activities, both IVE and DVE groups exhibited a significant shift toward a scientific understanding in their conceptual models and epistemological beliefs about the nature of relative motion, and also a significant improvement on relative motion problem-solving tests. In addition, we analyzed students' performance on one-dimensional and two-dimensional questions in the relative motion problem-solving test separately and found that after training in the simulation, the IVE group performed significantly better than the DVE group on solving two-dimensional relative motion problems. We suggest that egocentric encoding of the scene in IVE (where the learner constitutes a part of a scene they are immersed in), as compared to allocentric encoding on a computer screen in DVE (where the learner is looking at the scene from "outside"), is more beneficial than DVE for studying more complex (two-dimensional) relative motion problems. Overall, our findings suggest that such aspects of virtual realities as immersivity, first-hand experience, and the possibility of changing different frames of reference can facilitate understanding abstract scientific phenomena and help in displacing intuitive misconceptions with more accurate mental models.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_9");'>9</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li class="active"><span>11</span></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_11 --> <div id="page_12" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="221"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24245712','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24245712"><span>Preface for the special issue of Mathematical Biosciences and Engineering, BIOCOMP 2012.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Buonocore, Aniello; Di Crescenzo, Antonio; Hastings, Alan</p> <p>2014-04-01</p> <p>The International Conference "BIOCOMP2012 - Mathematical Modeling and Computational Topics in Biosciences'', was held in Vietri sul Mare (Italy), June 4-8, 2012. It was dedicated to the Memory of Professor Luigi M. Ricciardi (1942-2011), who was a visionary and tireless promoter of the 3 previous editions of the BIOCOMP conference series. We thought that the best way to honor his memory was to continue the BIOCOMP program. Over the years, this conference promoted scientific activities related to his wide interests and scientific expertise, which ranged in various areas of applications of mathematics, probability and statistics to biosciences and cybernetics, also with emphasis on computational problems. We are pleased that many of his friends and colleagues, as well as many other scientists, were attracted by the goals of this recent event and offered to contribute to its success.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70196801','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70196801"><span>A suite of exercises for verifying dynamic earthquake rupture codes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Harris, Ruth A.; Barall, Michael; Aagaard, Brad T.; Ma, Shuo; Roten, Daniel; Olsen, Kim B.; Duan, Benchun; Liu, Dunyu; Luo, Bin; Bai, Kangchen; Ampuero, Jean-Paul; Kaneko, Yoshihiro; Gabriel, Alice-Agnes; Duru, Kenneth; Ulrich, Thomas; Wollherr, Stephanie; Shi, Zheqiang; Dunham, Eric; Bydlon, Sam; Zhang, Zhenguo; Chen, Xiaofei; Somala, Surendra N.; Pelties, Christian; Tago, Josue; Cruz-Atienza, Victor Manuel; Kozdon, Jeremy; Daub, Eric; Aslam, Khurram; Kase, Yuko; Withers, Kyle; Dalguer, Luis</p> <p>2018-01-01</p> <p>We describe a set of benchmark exercises that are designed to test if computer codes that simulate dynamic earthquake rupture are working as intended. These types of computer codes are often used to understand how earthquakes operate, and they produce simulation results that include earthquake size, amounts of fault slip, and the patterns of ground shaking and crustal deformation. The benchmark exercises examine a range of features that scientists incorporate in their dynamic earthquake rupture simulations. These include implementations of simple or complex fault geometry, off‐fault rock response to an earthquake, stress conditions, and a variety of formulations for fault friction. Many of the benchmarks were designed to investigate scientific problems at the forefronts of earthquake physics and strong ground motions research. The exercises are freely available on our website for use by the scientific community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2010-03-04/pdf/2010-4518.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2010-03-04/pdf/2010-4518.pdf"><span>75 FR 9887 - Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2010-03-04</p> <p>... DEPARTMENT OF ENERGY Advanced Scientific Computing Advisory Committee AGENCY: Department of Energy... Advanced Scientific Computing Advisory Committee (ASCAC). Federal Advisory Committee Act (Pub. L. 92-463... INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing Research; SC-21/Germantown Building...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2011-02-22/pdf/2011-3848.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2011-02-22/pdf/2011-3848.pdf"><span>76 FR 9765 - Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2011-02-22</p> <p>... DEPARTMENT OF ENERGY Advanced Scientific Computing Advisory Committee AGENCY: Office of Science... Advanced Scientific Computing Advisory Committee (ASCAC). The Federal Advisory Committee Act (Pub. L. 92... INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing Research, SC-21/Germantown Building...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2012-07-31/pdf/2012-18626.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2012-07-31/pdf/2012-18626.pdf"><span>77 FR 45345 - DOE/Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2012-07-31</p> <p>... Recompetition results for Scientific Discovery through Advanced Computing (SciDAC) applications Co-design Public... DEPARTMENT OF ENERGY DOE/Advanced Scientific Computing Advisory Committee AGENCY: Office of... the Advanced Scientific Computing Advisory Committee (ASCAC). The Federal Advisory Committee Act (Pub...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2010-10-20/pdf/2010-26475.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2010-10-20/pdf/2010-26475.pdf"><span>75 FR 64720 - DOE/Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2010-10-20</p> <p>... DEPARTMENT OF ENERGY DOE/Advanced Scientific Computing Advisory Committee AGENCY: Department of... the Advanced Scientific Computing Advisory Committee (ASCAC). Federal Advisory Committee Act (Pub. L.... FOR FURTHER INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing Research; SC-21...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2004JCoAM.168..341P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2004JCoAM.168..341P"><span>Development of an object-oriented finite element program: application to metal-forming and impact simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pantale, O.; Caperaa, S.; Rakotomalala, R.</p> <p>2004-07-01</p> <p>During the last 50 years, the development of better numerical methods and more powerful computers has been a major enterprise for the scientific community. In the same time, the finite element method has become a widely used tool for researchers and engineers. Recent advances in computational software have made possible to solve more physical and complex problems such as coupled problems, nonlinearities, high strain and high-strain rate problems. In this field, an accurate analysis of large deformation inelastic problems occurring in metal-forming or impact simulations is extremely important as a consequence of high amount of plastic flow. In this presentation, the object-oriented implementation, using the C++ language, of an explicit finite element code called DynELA is presented. The object-oriented programming (OOP) leads to better-structured codes for the finite element method and facilitates the development, the maintainability and the expandability of such codes. The most significant advantage of OOP is in the modeling of complex physical systems such as deformation processing where the overall complex problem is partitioned in individual sub-problems based on physical, mathematical or geometric reasoning. We first focus on the advantages of OOP for the development of scientific programs. Specific aspects of OOP, such as the inheritance mechanism, the operators overload procedure or the use of template classes are detailed. Then we present the approach used for the development of our finite element code through the presentation of the kinematics, conservative and constitutive laws and their respective implementation in C++. Finally, the efficiency and accuracy of our finite element program are investigated using a number of benchmark tests relative to metal forming and impact simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1339578-benchmark-problems-numerical-implementations-phase-field-models','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1339578-benchmark-problems-numerical-implementations-phase-field-models"><span>Benchmark problems for numerical implementations of phase field models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Jokisaari, A. M.; Voorhees, P. W.; Guyer, J. E.; ...</p> <p>2016-10-01</p> <p>Here, we present the first set of benchmark problems for phase field models that are being developed by the Center for Hierarchical Materials Design (CHiMaD) and the National Institute of Standards and Technology (NIST). While many scientific research areas use a limited set of well-established software, the growing phase field community continues to develop a wide variety of codes and lacks benchmark problems to consistently evaluate the numerical performance of new implementations. Phase field modeling has become significantly more popular as computational power has increased and is now becoming mainstream, driving the need for benchmark problems to validate and verifymore » new implementations. We follow the example set by the micromagnetics community to develop an evolving set of benchmark problems that test the usability, computational resources, numerical capabilities and physical scope of phase field simulation codes. In this paper, we propose two benchmark problems that cover the physics of solute diffusion and growth and coarsening of a second phase via a simple spinodal decomposition model and a more complex Ostwald ripening model. We demonstrate the utility of benchmark problems by comparing the results of simulations performed with two different adaptive time stepping techniques, and we discuss the needs of future benchmark problems. The development of benchmark problems will enable the results of quantitative phase field models to be confidently incorporated into integrated computational materials science and engineering (ICME), an important goal of the Materials Genome Initiative.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/983318','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/983318"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Bailey, David H.</p> <p></p> <p>The NAS Parallel Benchmarks (NPB) are a suite of parallel computer performance benchmarks. They were originally developed at the NASA Ames Research Center in 1991 to assess high-end parallel supercomputers. Although they are no longer used as widely as they once were for comparing high-end system performance, they continue to be studied and analyzed a great deal in the high-performance computing community. The acronym 'NAS' originally stood for the Numerical Aeronautical Simulation Program at NASA Ames. The name of this organization was subsequently changed to the Numerical Aerospace Simulation Program, and more recently to the NASA Advanced Supercomputing Center, althoughmore » the acronym remains 'NAS.' The developers of the original NPB suite were David H. Bailey, Eric Barszcz, John Barton, David Browning, Russell Carter, LeoDagum, Rod Fatoohi, Samuel Fineberg, Paul Frederickson, Thomas Lasinski, Rob Schreiber, Horst Simon, V. Venkatakrishnan and Sisira Weeratunga. The original NAS Parallel Benchmarks consisted of eight individual benchmark problems, each of which focused on some aspect of scientific computing. The principal focus was in computational aerophysics, although most of these benchmarks have much broader relevance, since in a much larger sense they are typical of many real-world scientific computing applications. The NPB suite grew out of the need for a more rational procedure to select new supercomputers for acquisition by NASA. The emergence of commercially available highly parallel computer systems in the late 1980s offered an attractive alternative to parallel vector supercomputers that had been the mainstay of high-end scientific computing. However, the introduction of highly parallel systems was accompanied by a regrettable level of hype, not only on the part of the commercial vendors but even, in some cases, by scientists using the systems. As a result, it was difficult to discern whether the new systems offered any fundamental performance advantage over vector supercomputers, and, if so, which of the parallel offerings would be most useful in real-world scientific computation. In part to draw attention to some of the performance reporting abuses prevalent at the time, the present author wrote a humorous essay 'Twelve Ways to Fool the Masses,' which described in a light-hearted way a number of the questionable ways in which both vendor marketing people and scientists were inflating and distorting their performance results. All of this underscored the need for an objective and scientifically defensible measure to compare performance on these systems.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AAS...21914709D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AAS...21914709D"><span>Computing Across the Physics and Astrophysics Curriculum</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>DeGioia Eastwood, Kathy; James, M.; Dolle, E.</p> <p>2012-01-01</p> <p>Computational skills are essential in today's marketplace. Bachelors entering the STEM workforce report that their undergraduate education does not adequately prepare them to use scientific software and to write programs. Computation can also increase student learning; not only are the students actively engaged, but computational problems allow them to explore physical problems that are more realistic than the few that can be solved analytically. We have received a grant from the NSF CCLI Phase I program to integrate computing into our upper division curriculum. Our language of choice is Matlab; this language had already been chosen for our required sophomore course in Computational Physics because of its prevalence in industry. For two summers we have held faculty workshops to help our professors develop the needed expertise, and we are now in the implementation and evaluation stage. The end product will be a set of learning materials in the form of computational modules that we will make freely available. These modules will include the assignment, pedagogical goals, Matlab code, samples of student work, and instructor comments. At this meeting we present an overview of the project as well as modules written for a course in upper division stellar astrophysics. We acknowledge the support of the NSF through DUE-0837368.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19980023503','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19980023503"><span>Trenholm State (AL) Technical College High School Science Enrichment Program 1996-1997 Evaluation Report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ross, Elizabeth G.</p> <p>1997-01-01</p> <p>This document presents findings based on a third-year evaluation of Trenholm State (AL) Technical College's National Aeronautics and Space Administration (NASA) - supported High School Science Enrichment Program (HSSEP). HSSEP is an external (to school) program for area students from groups that are underrepresented in the mathematics, science, engineering and technology (MSET) professions. In addition to gaining insight into scientific careers, HSSEP participants learn about and deliver presentations that focus on mathematics applications, scientific problem-solving and computer programming during a seven-week summer or 10-week Academic-Year Saturday session.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008ASPC..394..347V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008ASPC..394..347V"><span>The Methods of Cognitive Visualization for the Astronomical Databases Analyzing Tools Development</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Vitkovskiy, V.; Gorohov, V.</p> <p>2008-08-01</p> <p>There are two kinds of computer graphics: the illustrative one and the cognitive one. Appropriate the cognitive pictures not only make evident and clear the sense of complex and difficult scientific concepts, but promote, --- and not so very rarely, --- a birth of a new knowledge. On the basis of the cognitive graphics concept, we worked out the SW-system for visualization and analysis. It allows to train and to aggravate intuition of researcher, to raise his interest and motivation to the creative, scientific cognition, to realize process of dialogue with the very problems simultaneously.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2010-07-26/pdf/2010-18207.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2010-07-26/pdf/2010-18207.pdf"><span>75 FR 43518 - Advanced Scientific Computing Advisory Committee; Meeting</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2010-07-26</p> <p>... DEPARTMENT OF ENERGY Advanced Scientific Computing Advisory Committee; Meeting AGENCY: Office of... Scientific Computing Advisory Committee (ASCAC). Federal Advisory Committee Act (Pub. L. 92-463, 86 Stat. 770...: Melea Baker, Office of Advanced Scientific Computing Research; SC-21/Germantown Building; U. S...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20080048167','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20080048167"><span>Optimization of Angular-Momentum Biases of Reaction Wheels</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Lee, Clifford; Lee, Allan</p> <p>2008-01-01</p> <p>RBOT [RWA Bias Optimization Tool (wherein RWA signifies Reaction Wheel Assembly )] is a computer program designed for computing angular momentum biases for reaction wheels used for providing spacecraft pointing in various directions as required for scientific observations. RBOT is currently deployed to support the Cassini mission to prevent operation of reaction wheels at unsafely high speeds while minimizing time in undesirable low-speed range, where elasto-hydrodynamic lubrication films in bearings become ineffective, leading to premature bearing failure. The problem is formulated as a constrained optimization problem in which maximum wheel speed limit is a hard constraint and a cost functional that increases as speed decreases below a low-speed threshold. The optimization problem is solved using a parametric search routine known as the Nelder-Mead simplex algorithm. To increase computational efficiency for extended operation involving large quantity of data, the algorithm is designed to (1) use large time increments during intervals when spacecraft attitudes or rates of rotation are nearly stationary, (2) use sinusoidal-approximation sampling to model repeated long periods of Earth-point rolling maneuvers to reduce computational loads, and (3) utilize an efficient equation to obtain wheel-rate profiles as functions of initial wheel biases based on conservation of angular momentum (in an inertial frame) using pre-computed terms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/17254294','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/17254294"><span>Trends in life science grid: from computing grid to knowledge grid.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Konagaya, Akihiko</p> <p>2006-12-18</p> <p>Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1764466','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=1764466"><span>Trends in life science grid: from computing grid to knowledge grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Konagaya, Akihiko</p> <p>2006-01-01</p> <p>Background Grid computing has great potential to become a standard cyberinfrastructure for life sciences which often require high-performance computing and large data handling which exceeds the computing capacity of a single institution. Results This survey reviews the latest grid technologies from the viewpoints of computing grid, data grid and knowledge grid. Computing grid technologies have been matured enough to solve high-throughput real-world life scientific problems. Data grid technologies are strong candidates for realizing "resourceome" for bioinformatics. Knowledge grids should be designed not only from sharing explicit knowledge on computers but also from community formulation for sharing tacit knowledge among a community. Conclusion Extending the concept of grid from computing grid to knowledge grid, it is possible to make use of a grid as not only sharable computing resources, but also as time and place in which people work together, create knowledge, and share knowledge and experiences in a community. PMID:17254294</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19451110','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19451110"><span>Real science at the petascale.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Saksena, Radhika S; Boghosian, Bruce; Fazendeiro, Luis; Kenway, Owain A; Manos, Steven; Mazzeo, Marco D; Sadiq, S Kashif; Suter, James L; Wright, David; Coveney, Peter V</p> <p>2009-06-28</p> <p>We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of fundamental problems in turbulence through computational materials science research to biomedical applications at the forefront of HIV/AIDS research and cerebrovascular haemodynamics. This work was mainly performed on the US TeraGrid 'petascale' resource, Ranger, at Texas Advanced Computing Center, in the first half of 2008 when it was the largest computing system in the world available for open scientific research. We have sought to use this petascale supercomputer optimally across application domains and scales, exploiting the excellent parallel scaling performance found on up to at least 32 768 cores for certain of our codes in the so-called 'capability computing' category as well as high-throughput intermediate-scale jobs for ensemble simulations in the 32-512 core range. Furthermore, this activity provides evidence that conventional parallel programming with MPI should be successful at the petascale in the short to medium term. We also report on the parallel performance of some of our codes on up to 65 636 cores on the IBM Blue Gene/P system at the Argonne Leadership Computing Facility, which has recently been named the fastest supercomputer in the world for open science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19930005857','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19930005857"><span>Mass storage system experiences and future needs at the National Center for Atmospheric Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Olear, Bernard T.</p> <p>1991-01-01</p> <p>A summary and viewgraphs of a discussion presented at the National Space Science Data Center (NSSDC) Mass Storage Workshop is included. Some of the experiences of the Scientific Computing Division at the National Center for Atmospheric Research (NCAR) dealing the the 'data problem' are discussed. A brief history and a development of some basic mass storage system (MSS) principles are given. An attempt is made to show how these principles apply to the integration of various components into NCAR's MSS. Future MSS needs for future computing environments is discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AAS...20914603B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AAS...20914603B"><span>Using Microsoft PowerPoint as an Astronomical Image Analysis Tool</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Beck-Winchatz, Bernhard</p> <p>2006-12-01</p> <p>Engaging students in the analysis of authentic scientific data is an effective way to teach them about the scientific process and to develop their problem solving, teamwork and communication skills. In astronomy several image processing and analysis software tools have been developed for use in school environments. However, the practical implementation in the classroom is often difficult because the teachers may not have the comfort level with computers necessary to install and use these tools, they may not have adequate computer privileges and/or support, and they may not have the time to learn how to use specialized astronomy software. To address this problem, we have developed a set of activities in which students analyze astronomical images using basic tools provided in PowerPoint. These include measuring sizes, distances, and angles, and blinking images. In contrast to specialized software, PowerPoint is broadly available on school computers. Many teachers are already familiar with PowerPoint, and the skills developed while learning how to analyze astronomical images are highly transferable. We will discuss several practical examples of measurements, including the following: -Variations in the distances to the sun and moon from their angular sizes -Magnetic declination from images of shadows -Diameter of the moon from lunar eclipse images -Sizes of lunar craters -Orbital radii of the Jovian moons and mass of Jupiter -Supernova and comet searches -Expansion rate of the universe from images of distant galaxies</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1231535','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1231535"><span>Extensible Computational Chemistry Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p></p> <p>2012-08-09</p> <p>ECCE provides a sophisticated graphical user interface, scientific visualization tools, and the underlying data management framework enabling scientists to efficiently set up calculations and store, retrieve, and analyze the rapidly growing volumes of data produced by computational chemistry studies. ECCE was conceived as part of the Environmental Molecular Sciences Laboratory construction to solve the problem of researchers being able to effectively utilize complex computational chemistry codes and massively parallel high performance compute resources. Bringing the power of these codes and resources to the desktops of researcher and thus enabling world class research without users needing a detailed understanding of themore » inner workings of either the theoretical codes or the supercomputers needed to run them was a grand challenge problem in the original version of the EMSL. ECCE allows collaboration among researchers using a web-based data repository where the inputs and results for all calculations done within ECCE are organized. ECCE is a first of kind end-to-end problem solving environment for all phases of computational chemistry research: setting up calculations with sophisticated GUI and direct manipulation visualization tools, submitting and monitoring calculations on remote high performance supercomputers without having to be familiar with the details of using these compute resources, and performing results visualization and analysis including creating publication quality images. ECCE is a suite of tightly integrated applications that are employed as the user moves through the modeling process.« less</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_10");'>10</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li class="active"><span>12</span></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_12 --> <div id="page_13" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="241"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUFMIN43D..07W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUFMIN43D..07W"><span>Semantic Interoperability for Computational Mineralogy: Experiences of the eMinerals Consortium</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Walker, A. M.; White, T. O.; Dove, M. T.; Bruin, R. P.; Couch, P. A.; Tyer, R. P.</p> <p>2006-12-01</p> <p>The use of atomic scale computer simulation of minerals to obtain information for geophysics and environmental science has grown enormously over the past couple of decades. It is now routine to probe mineral behavior in the Earth's deep interior and in the surface environment by borrowing methods and simulation codes from computational chemistry and physics. It is becoming increasingly important to use methods embodied in more than one of these codes to solve any single scientific problem. However, scientific codes are rarely designed for easy interoperability and data exchange; data formats are often code-specific, poorly documented and fragile, liable to frequent change between software versions, and even compiler versions. This means that the scientist's simple desire to use the methodological approaches offered by multiple codes is frustrated, and even the sharing of data between collaborators becomes fraught with difficulties. The eMinerals consortium was formed in the early stages of the UK eScience program with the aim of developing the tools needed to apply atomic scale simulation to environmental problems in a grid-enabled world, and to harness the computational power offered by grid technologies to address some outstanding mineralogical problems. One example of the kind of problem we can tackle is the origin of the compressibility anomaly in silica glass. By passing data directly between simulation and analysis tools we were able to probe this effect in more detail than has previously been possible and have shown how the anomaly is related to the details of the amorphous structure. In order to approach this kind of problem we have constructed a mini-grid, a small scale and extensible combined compute- and data-grid that allows the execution of many calculations in parallel, and the transparent storage of semantically-rich marked-up result data. Importantly, we automatically capture multiple kinds of metadata and key results from each calculation. We believe that the lessons learned and tools developed will be useful in many areas of science beyond the computational mineralogy. Key tools that will be described include: a pure Fortran XML library (FoX) that presents XPath, SAX and DOM interfaces as well as permitting the easy production of valid XML from legacy Fortran programs; a job submission framework that automatically schedules calculations to remote grid resources, handles data staging and metadata capture; and a tool (AgentX) that map concepts from an ontology onto locations in documents of various formats that we use to enable data exchange.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016Natur.532..210S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016Natur.532..210S"><span>Exploring the quantum speed limit with computer games</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sørensen, Jens Jakob W. H.; Pedersen, Mads Kock; Munch, Michael; Haikka, Pinja; Jensen, Jesper Halkjær; Planke, Tilo; Andreasen, Morten Ginnerup; Gajdacz, Miroslav; Mølmer, Klaus; Lieberoth, Andreas; Sherson, Jacob F.</p> <p>2016-04-01</p> <p>Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies. Citizen science (or crowd sourcing) is a way of exploiting this ability by presenting scientific research problems to non-experts. ‘Gamification’—the application of game elements in a non-game context—is an effective tool with which to enable citizen scientists to provide solutions to research problems. The citizen science games Foldit, EteRNA and EyeWire have been used successfully to study protein and RNA folding and neuron mapping, but so far gamification has not been applied to problems in quantum physics. Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics. We show that human players are able to find solutions to difficult problems associated with the task of quantum computing. Players succeed where purely numerical optimization fails, and analyses of their solutions provide insights into the problem of optimization of a more profound and general nature. Using player strategies, we have thus developed a few-parameter heuristic optimization method that efficiently outperforms the most prominent established numerical methods. The numerical complexity associated with time-optimal solutions increases for shorter process durations. To understand this better, we produced a low-dimensional rendering of the optimization landscape. This rendering reveals why traditional optimization methods fail near the quantum speed limit (that is, the shortest process duration with perfect fidelity). Combined analyses of optimization landscapes and heuristic solution strategies may benefit wider classes of optimization problems in quantum physics and beyond.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27075097','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27075097"><span>Exploring the quantum speed limit with computer games.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Sørensen, Jens Jakob W H; Pedersen, Mads Kock; Munch, Michael; Haikka, Pinja; Jensen, Jesper Halkjær; Planke, Tilo; Andreasen, Morten Ginnerup; Gajdacz, Miroslav; Mølmer, Klaus; Lieberoth, Andreas; Sherson, Jacob F</p> <p>2016-04-14</p> <p>Humans routinely solve problems of immense computational complexity by intuitively forming simple, low-dimensional heuristic strategies. Citizen science (or crowd sourcing) is a way of exploiting this ability by presenting scientific research problems to non-experts. 'Gamification'--the application of game elements in a non-game context--is an effective tool with which to enable citizen scientists to provide solutions to research problems. The citizen science games Foldit, EteRNA and EyeWire have been used successfully to study protein and RNA folding and neuron mapping, but so far gamification has not been applied to problems in quantum physics. Here we report on Quantum Moves, an online platform gamifying optimization problems in quantum physics. We show that human players are able to find solutions to difficult problems associated with the task of quantum computing. Players succeed where purely numerical optimization fails, and analyses of their solutions provide insights into the problem of optimization of a more profound and general nature. Using player strategies, we have thus developed a few-parameter heuristic optimization method that efficiently outperforms the most prominent established numerical methods. The numerical complexity associated with time-optimal solutions increases for shorter process durations. To understand this better, we produced a low-dimensional rendering of the optimization landscape. This rendering reveals why traditional optimization methods fail near the quantum speed limit (that is, the shortest process duration with perfect fidelity). Combined analyses of optimization landscapes and heuristic solution strategies may benefit wider classes of optimization problems in quantum physics and beyond.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED015485.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED015485.pdf"><span>A SOCIO-ECONOMIST LOOKS AT THE CURRENT VALUES AND CHANGING NEEDS OF YOUTH. FINAL DRAFT.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>THEOBALD, ROBERT</p> <p></p> <p>MAN HAS ACHIEVED THE POWER TO CREATE AN ENVIRONMENT SUITED TO HIS NEEDS. THIS POWER COMES FROM DEVELOPMENTS IN THE UTILIZATION OF ENERGY, ADVANCEMENTS IN CHEMISTRY, AN INCREASE IN SCIENTIFIC PROBLEM SOLVING ABILITY AND COMPUTER TECHNOLOGY. THESE SOURCES OF POWER RESULT IN THE DRIVE TOWARD THE DEVELOPMENT OF DESTRUCTIVE POWER, THE CAPABILITY OF…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19880006403','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19880006403"><span>Aeropropulsion 1987. Session 2: Aeropropulsion Structures Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1987-01-01</p> <p>Aeropropulsion systems present unique problems to the structural engineer. The extremes in operating temperatures, rotational effects, and behaviors of advanced material systems combine into complexities that require advances in many scientific disciplines involved in structural analysis and design procedures. This session provides an overview of the complexities of aeropropulsion structures and the theoretical, computational, and experimental research conducted to achieve the needed advances.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA133285','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA133285"><span>Appraisal of Scientific Resources for Emergency Management.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1983-09-01</p> <p>water, communications, computers, and oil refineries or storage facilities. In addition, the growth of the number of operative nuclear power plants ...one from a nuclear power plant accident); one involved hazardous waste disposal problems; and finally two involved wartime scenarios, one focusing on...pro- tection research, radiological protection from nuclear power plant accidents, concepts and operation of public shelters, and post attack</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1343414','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1343414"><span>PuLP/XtraPuLP : Partitioning Tools for Extreme-Scale Graphs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Slota, George M; Rajamanickam, Sivasankaran; Madduri, Kamesh</p> <p>2017-09-21</p> <p>PuLP/XtraPulp is software for partitioning graphs from several real-world problems. Graphs occur in several places in real world from road networks, social networks and scientific simulations. For efficient parallel processing these graphs have to be partitioned (split) with respect to metrics such as computation and communication costs. Our software allows such partitioning for massive graphs.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030017995','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030017995"><span>Website on Protein Interaction and Protein Structure Related Work</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Samanta, Manoj; Liang, Shoudan; Biegel, Bryan (Technical Monitor)</p> <p>2003-01-01</p> <p>In today's world, three seemingly diverse fields - computer information technology, nanotechnology and biotechnology are joining forces to enlarge our scientific knowledge and solve complex technological problems. Our group is dedicated to conduct theoretical research exploring the challenges in this area. The major areas of research include: 1) Yeast Protein Interactions; 2) Protein Structures; and 3) Current Transport through Small Molecules.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1348331-enabling-nvm-data-intensive-scientific-services','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1348331-enabling-nvm-data-intensive-scientific-services"><span>Enabling NVM for Data-Intensive Scientific Services</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Carns, Philip; Jenkins, John; Seo, Sangmin</p> <p></p> <p>Specialized, transient data services are playing an increasingly prominent role in data-intensive scientific computing. These services offer flexible, on-demand pairing of applications with storage hardware using semantics that are optimized for the problem domain. Concurrent with this trend, upcoming scientific computing and big data systems will be deployed with emerging NVM technology to achieve the highest possible price/productivity ratio. Clearly, therefore, we must develop techniques to facilitate the confluence of specialized data services and NVM technology. In this work we explore how to enable the composition of NVM resources within transient distributed services while still retaining their essential performance characteristics.more » Our approach involves eschewing the conventional distributed file system model and instead projecting NVM devices as remote microservices that leverage user-level threads, RPC services, RMA-enabled network transports, and persistent memory libraries in order to maximize performance. We describe a prototype system that incorporates these concepts, evaluate its performance for key workloads on an exemplar system, and discuss how the system can be leveraged as a component of future data-intensive architectures.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/11314458','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/11314458"><span>Human computers: the first pioneers of the information age.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Grier, D A</p> <p>2001-03-01</p> <p>Before computers were machines, they were people. They were men and women, young and old, well educated and common. They were the workers who convinced scientists that large-scale calculation had value. Long before Presper Eckert and John Mauchly built the ENIAC at the Moore School of Electronics, Philadelphia, or Maurice Wilkes designed the EDSAC for Manchester University, human computers had created the discipline of computation. They developed numerical methodologies and proved them on practical problems. These human computers were not savants or calculating geniuses. Some knew little more than basic arithmetic. A few were near equals of the scientists they served and, in a different time or place, might have become practicing scientists had they not been barred from a scientific career by their class, education, gender or ethnicity.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19900050416&hterms=maple&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dmaple','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19900050416&hterms=maple&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Dmaple"><span>Merlin - Massively parallel heterogeneous computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wittie, Larry; Maples, Creve</p> <p>1989-01-01</p> <p>Hardware and software for Merlin, a new kind of massively parallel computing system, are described. Eight computers are linked as a 300-MIPS prototype to develop system software for a larger Merlin network with 16 to 64 nodes, totaling 600 to 3000 MIPS. These working prototypes help refine a mapped reflective memory technique that offers a new, very general way of linking many types of computer to form supercomputers. Processors share data selectively and rapidly on a word-by-word basis. Fast firmware virtual circuits are reconfigured to match topological needs of individual application programs. Merlin's low-latency memory-sharing interfaces solve many problems in the design of high-performance computing systems. The Merlin prototypes are intended to run parallel programs for scientific applications and to determine hardware and software needs for a future Teraflops Merlin network.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29374408','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29374408"><span>Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the Cloud Computing Environment Using an Improved PSO Algorithm</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>N, Sadhasivam; R, Balamurugan; M, Pandi</p> <p>2018-01-27</p> <p>Objective: Epigenetic modifications involving DNA methylation and histone statud are responsible for the stable maintenance of cellular phenotypes. Abnormalities may be causally involved in cancer development and therefore could have diagnostic potential. The field of epigenomics refers to all epigenetic modifications implicated in control of gene expression, with a focus on better understanding of human biology in both normal and pathological states. Epigenomics scientific workflow is essentially a data processing pipeline to automate the execution of various genome sequencing operations or tasks. Cloud platform is a popular computing platform for deploying large scale epigenomics scientific workflow. Its dynamic environment provides various resources to scientific users on a pay-per-use billing model. Scheduling epigenomics scientific workflow tasks is a complicated problem in cloud platform. We here focused on application of an improved particle swam optimization (IPSO) algorithm for this purpose. Methods: The IPSO algorithm was applied to find suitable resources and allocate epigenomics tasks so that the total cost was minimized for detection of epigenetic abnormalities of potential application for cancer diagnosis. Result: The results showed that IPSO based task to resource mapping reduced total cost by 6.83 percent as compared to the traditional PSO algorithm. Conclusion: The results for various cancer diagnosis tasks showed that IPSO based task to resource mapping can achieve better costs when compared to PSO based mapping for epigenomics scientific application workflow. Creative Commons Attribution License</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JCoPh.344..516L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JCoPh.344..516L"><span>A resilient and efficient CFD framework: Statistical learning tools for multi-fidelity and heterogeneous information fusion</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lee, Seungjoon; Kevrekidis, Ioannis G.; Karniadakis, George Em</p> <p>2017-09-01</p> <p>Exascale-level simulations require fault-resilient algorithms that are robust against repeated and expected software and/or hardware failures during computations, which may render the simulation results unsatisfactory. If each processor can share some global information about the simulation from a coarse, limited accuracy but relatively costless auxiliary simulator we can effectively fill-in the missing spatial data at the required times by a statistical learning technique - multi-level Gaussian process regression, on the fly; this has been demonstrated in previous work [1]. Based on the previous work, we also employ another (nonlinear) statistical learning technique, Diffusion Maps, that detects computational redundancy in time and hence accelerate the simulation by projective time integration, giving the overall computation a "patch dynamics" flavor. Furthermore, we are now able to perform information fusion with multi-fidelity and heterogeneous data (including stochastic data). Finally, we set the foundations of a new framework in CFD, called patch simulation, that combines information fusion techniques from, in principle, multiple fidelity and resolution simulations (and even experiments) with a new adaptive timestep refinement technique. We present two benchmark problems (the heat equation and the Navier-Stokes equations) to demonstrate the new capability that statistical learning tools can bring to traditional scientific computing algorithms. For each problem, we rely on heterogeneous and multi-fidelity data, either from a coarse simulation of the same equation or from a stochastic, particle-based, more "microscopic" simulation. We consider, as such "auxiliary" models, a Monte Carlo random walk for the heat equation and a dissipative particle dynamics (DPD) model for the Navier-Stokes equations. More broadly, in this paper we demonstrate the symbiotic and synergistic combination of statistical learning, domain decomposition, and scientific computing in exascale simulations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3597547','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3597547"><span>Chaste: An Open Source C++ Library for Computational Physiology and Biology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Mirams, Gary R.; Arthurs, Christopher J.; Bernabeu, Miguel O.; Bordas, Rafel; Cooper, Jonathan; Corrias, Alberto; Davit, Yohan; Dunn, Sara-Jane; Fletcher, Alexander G.; Harvey, Daniel G.; Marsh, Megan E.; Osborne, James M.; Pathmanathan, Pras; Pitt-Francis, Joe; Southern, James; Zemzemi, Nejib; Gavaghan, David J.</p> <p>2013-01-01</p> <p>Chaste — Cancer, Heart And Soft Tissue Environment — is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to ‘re-invent the wheel’ with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials. PMID:23516352</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030111816','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030111816"><span>Job Superscheduler Architecture and Performance in Computational Grid Environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shan, Hongzhang; Oliker, Leonid; Biswas, Rupak</p> <p>2003-01-01</p> <p>Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex scientific problems. However, a number of major technical hurdles, including distributed resource management and effective job scheduling, stand in the way of realizing these gains. In this paper, we propose a novel grid superscheduler architecture and three distributed job migration algorithms. We also model the critical interaction between the superscheduler and autonomous local schedulers. Extensive performance comparisons with ideal, central, and local schemes using real workloads from leading computational centers are conducted in a simulation environment. Additionally, synthetic workloads are used to perform a detailed sensitivity analysis of our superscheduler. Several key metrics demonstrate that substantial performance gains can be achieved via smart superscheduling in distributed computational grids.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960002980','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960002980"><span>Interactive access and management for four-dimensional environmental data sets using McIDAS</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hibbard, William L.; Tripoli, Gregory J.</p> <p>1995-01-01</p> <p>This grant has fundamentally changed the way that meteorologists look at the output of their atmospheric models, through the development and wide distribution of the Vis5D system. The Vis5D system is also gaining acceptance among oceanographers and atmospheric chemists. Vis5D gives these scientists an interactive three-dimensional movie of their very large data sets that they can use to understand physical mechanisms and to trace problems to their sources. This grant has also helped to define the future direction of scientific visualization through the development of the VisAD system and its lattice data model. The VisAD system can be used to interactively steer and visualize scientific computations. A key element of this capability is the flexibility of the system's data model to adapt to a wide variety of scientific data, including the integration of several forms of scientific metadata.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMNG13A..02L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMNG13A..02L"><span>Computational Challenges in the Analysis of Petrophysics Using Microtomography and Upscaling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Liu, J.; Pereira, G.; Freij-Ayoub, R.; Regenauer-Lieb, K.</p> <p>2014-12-01</p> <p>Microtomography provides detailed 3D internal structures of rocks in micro- to tens of nano-meter resolution and is quickly turning into a new technology for studying petrophysical properties of materials. An important step is the upscaling of these properties as micron or sub-micron resolution can only be done on the sample-scale of millimeters or even less than a millimeter. We present here a recently developed computational workflow for the analysis of microstructures including the upscaling of material properties. Computations of properties are first performed using conventional material science simulations at micro to nano-scale. The subsequent upscaling of these properties is done by a novel renormalization procedure based on percolation theory. We have tested the workflow using different rock samples, biological and food science materials. We have also applied the technique on high-resolution time-lapse synchrotron CT scans. In this contribution we focus on the computational challenges that arise from the big data problem of analyzing petrophysical properties and its subsequent upscaling. We discuss the following challenges: 1) Characterization of microtomography for extremely large data sets - our current capability. 2) Computational fluid dynamics simulations at pore-scale for permeability estimation - methods, computing cost and accuracy. 3) Solid mechanical computations at pore-scale for estimating elasto-plastic properties - computational stability, cost, and efficiency. 4) Extracting critical exponents from derivative models for scaling laws - models, finite element meshing, and accuracy. Significant progress in each of these challenges is necessary to transform microtomography from the current research problem into a robust computational big data tool for multi-scale scientific and engineering problems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4980074','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4980074"><span>Molecular dynamics simulations through GPU video games technologies</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Loukatou, Styliani; Papageorgiou, Louis; Fakourelis, Paraskevas; Filntisi, Arianna; Polychronidou, Eleftheria; Bassis, Ioannis; Megalooikonomou, Vasileios; Makałowski, Wojciech; Vlachakis, Dimitrios; Kossida, Sophia</p> <p>2016-01-01</p> <p>Bioinformatics is the scientific field that focuses on the application of computer technology to the management of biological information. Over the years, bioinformatics applications have been used to store, process and integrate biological and genetic information, using a wide range of methodologies. One of the most de novo techniques used to understand the physical movements of atoms and molecules is molecular dynamics (MD). MD is an in silico method to simulate the physical motions of atoms and molecules under certain conditions. This has become a state strategic technique and now plays a key role in many areas of exact sciences, such as chemistry, biology, physics and medicine. Due to their complexity, MD calculations could require enormous amounts of computer memory and time and therefore their execution has been a big problem. Despite the huge computational cost, molecular dynamics have been implemented using traditional computers with a central memory unit (CPU). A graphics processing unit (GPU) computing technology was first designed with the goal to improve video games, by rapidly creating and displaying images in a frame buffer such as screens. The hybrid GPU-CPU implementation, combined with parallel computing is a novel technology to perform a wide range of calculations. GPUs have been proposed and used to accelerate many scientific computations including MD simulations. Herein, we describe the new methodologies developed initially as video games and how they are now applied in MD simulations. PMID:27525251</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JSEdT..25..590C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JSEdT..25..590C"><span>Design and Application of Interactive Simulations in Problem-Solving in University-Level Physics Education</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ceberio, Mikel; Almudí, José Manuel; Franco, Ángel</p> <p>2016-08-01</p> <p>In recent years, interactive computer simulations have been progressively integrated in the teaching of the sciences and have contributed significant improvements in the teaching-learning process. Practicing problem-solving is a key factor in science and engineering education. The aim of this study was to design simulation-based problem-solving teaching materials and assess their effectiveness in improving students' ability to solve problems in university-level physics. Firstly, we analyze the effect of using simulation-based materials in the development of students' skills in employing procedures that are typically used in the scientific method of problem-solving. We found that a significant percentage of the experimental students used expert-type scientific procedures such as qualitative analysis of the problem, making hypotheses, and analysis of results. At the end of the course, only a minority of the students persisted with habits based solely on mathematical equations. Secondly, we compare the effectiveness in terms of problem-solving of the experimental group students with the students who are taught conventionally. We found that the implementation of the problem-solving strategy improved experimental students' results regarding obtaining a correct solution from the academic point of view, in standard textbook problems. Thirdly, we explore students' satisfaction with simulation-based problem-solving teaching materials and we found that the majority appear to be satisfied with the methodology proposed and took on a favorable attitude to learning problem-solving. The research was carried out among first-year Engineering Degree students.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JPhCS.898j2016C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JPhCS.898j2016C"><span>The INDIGO-Datacloud Authentication and Authorization Infrastructure</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ceccanti, A.; Hardt, M.; Wegh, B.; Millar, AP; Caberletti, M.; Vianello, E.; Licehammer, S.</p> <p>2017-10-01</p> <p>Contemporary distributed computing infrastructures (DCIs) are not easily and securely accessible by scientists. These computing environments are typically hard to integrate due to interoperability problems resulting from the use of different authentication mechanisms, identity negotiation protocols and access control policies. Such limitations have a big impact on the user experience making it hard for user communities to port and run their scientific applications on resources aggregated from multiple providers. The INDIGO-DataCloud project wants to provide the services and tools needed to enable a secure composition of resources from multiple providers in support of scientific applications. In order to do so, a common AAI architecture has to be defined that supports multiple authentication mechanisms, support delegated authorization across services and can be easily integrated in off-the-shelf software. In this contribution we introduce the INDIGO Authentication and Authorization Infrastructure, describing its main components and their status and how authentication, delegation and authorization flows are implemented across services.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_11");'>11</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li class="active"><span>13</span></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_13 --> <div id="page_14" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="261"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19840023890','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19840023890"><span>User's Guide for ERB 7 Matrix. Volume 1: Experiment Description and Quality Control Report for Year 1</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tighe, R. J.; Shen, M. Y. H.</p> <p>1984-01-01</p> <p>The Nimbus 7 ERB MATRIX Tape is a computer program in which radiances and irradiances are converted into fluxes which are used to compute the basic scientific output parameters, emitted flux, albedo, and net radiation. They are spatially averaged and presented as time averages over one-day, six-day, and monthly periods. MATRIX data for the period November 16, 1978 through October 31, 1979 are presented. Described are the Earth Radiation Budget experiment, the Science Quality Control Report, Items checked by the MATRIX Science Quality Control Program, and Science Quality Control Data Analysis Report. Additional material from the detailed scientific quality control of the tapes which may be very useful to a user of the MATRIX tapes is included. Known errors and data problems and some suggestions on how to use the data for further climatologic and atmospheric physics studies are also discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940023413','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940023413"><span>Review and analysis of dense linear system solver package for distributed memory machines</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Narang, H. N.</p> <p>1993-01-01</p> <p>A dense linear system solver package recently developed at the University of Texas at Austin for distributed memory machine (e.g. Intel Paragon) has been reviewed and analyzed. The package contains about 45 software routines, some written in FORTRAN, and some in C-language, and forms the basis for parallel/distributed solutions of systems of linear equations encountered in many problems of scientific and engineering nature. The package, being studied by the Computer Applications Branch of the Analysis and Computation Division, may provide a significant computational resource for NASA scientists and engineers in parallel/distributed computing. Since the package is new and not well tested or documented, many of its underlying concepts and implementations were unclear; our task was to review, analyze, and critique the package as a step in the process that will enable scientists and engineers to apply it to the solution of their problems. All routines in the package were reviewed and analyzed. Underlying theory or concepts which exist in the form of published papers or technical reports, or memos, were either obtained from the author, or from the scientific literature; and general algorithms, explanations, examples, and critiques have been provided to explain the workings of these programs. Wherever the things were still unclear, communications were made with the developer (author), either by telephone or by electronic mail, to understand the workings of the routines. Whenever possible, tests were made to verify the concepts and logic employed in their implementations. A detailed report is being separately documented to explain the workings of these routines.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005AGUFMIN21D..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005AGUFMIN21D..01R"><span>End-to-end Cyberinfrastructure and Data Services for Earth System Science Education and Research: Unidata's Plans and Directions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ramamurthy, M.</p> <p>2005-12-01</p> <p>A revolution is underway in the role played by cyberinfrastructure and data services in the conduct of research and education. We live in an era of an unprecedented data volume from diverse sources, multidisciplinary analysis and synthesis, and active, learner-centered education emphasis. For example, modern remote-sensing systems like hyperspectral satellite instruments generate terabytes of data each day. Environmental problems such as global change and water cycle transcend disciplinary as well as geographic boundaries, and their solution requires integrated earth system science approaches. Contemporary education strategies recommend adopting an Earth system science approach for teaching the geosciences, employing new pedagogical techniques such as enquiry-based learning and hands-on activities. Needless to add, today's education and research enterprise depends heavily on robust, flexible and scalable cyberinfrastructure, especially on the ready availability of quality data and appropriate tools to manipulate and integrate those data. Fortuitously, rapid advances in computing and communication technologies have also revolutionized how data, tools and services are being incorporated into the teaching and scientific enterprise. The exponential growth in the use of the Internet in education and research, largely due to the advent of the World Wide Web, is by now well documented. On the other hand, how some of the other technological and community trends that have shaped the use of cyberinfrastructure, especially data services, is less well understood. For example, the computing industry is converging on an approach called Web services that enables a standard and yet revolutionary way of building applications and methods to connect and exchange information over the Web. This new approach, based on XML - a widely accepted format for exchanging data and corresponding semantics over the Internet - enables applications, computer systems, and information processes to work together in a fundamentally different way. Likewise, the advent of digital libraries, grid computing platforms, interoperable frameworks, standards and protocols, open-source software, and community atmospheric models have been important drivers in shaping the use of a new generation of end-to-end cyberinfrastructure for solving some of the most challenging scientific and educational problems. In this talk, I will present an overview of the scientific, technological, and educational drivers and discuss recent developments in cyberinfrastructure and Unidata's role and directions in providing robust, end-to-end data services for solving geoscientific problems and advancing student learning.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26215546','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26215546"><span>Best behaviour? Ontologies and the formal description of animal behaviour.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Gkoutos, Georgios V; Hoehndorf, Robert; Tsaprouni, Loukia; Schofield, Paul N</p> <p>2015-10-01</p> <p>The development of ontologies for describing animal behaviour has proved to be one of the most difficult of all scientific knowledge domains. Ranging from neurological processes to human emotions, the range and scope needed for such ontologies is highly challenging, but if data integration and computational tools such as automated reasoning are to be fully applied in this important area the underlying principles of these ontologies need to be better established and development needs detailed coordination. Whilst the state of scientific knowledge is always paramount in ontology and formal description framework design, this is a particular problem with neurobehavioural ontologies where our understanding of the relationship between behaviour and its underlying biophysical basis is currently in its infancy. In this commentary, we discuss some of the fundamental problems in designing and using behaviour ontologies, and present some of the best developed tools in this domain.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2007SPIE.6602E..14A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2007SPIE.6602E..14A"><span>Complexity, information loss, and model building: from neuro- to cognitive dynamics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Arecchi, F. Tito</p> <p>2007-06-01</p> <p>A scientific problem described within a given code is mapped by a corresponding computational problem, We call complexity (algorithmic) the bit length of the shortest instruction which solves the problem. Deterministic chaos in general affects a dynamical systems making the corresponding problem experimentally and computationally heavy, since one must reset the initial conditions at a rate higher than that of information loss (Kolmogorov entropy). One can control chaos by adding to the system new degrees of freedom (information swapping: information lost by chaos is replaced by that arising from the new degrees of freedom). This implies a change of code, or a new augmented model. Within a single code, changing hypotheses is equivalent to fixing different sets of control parameters, each with a different a-priori probability, to be then confirmed and transformed to an a-posteriori probability via Bayes theorem. Sequential application of Bayes rule is nothing else than the Darwinian strategy in evolutionary biology. The sequence is a steepest ascent algorithm, which stops once maximum probability has been reached. At this point the hypothesis exploration stops. By changing code (and hence the set of relevant variables) one can start again to formulate new classes of hypotheses . We call semantic complexity the number of accessible scientific codes, or models, that describe a situation. It is however a fuzzy concept, in so far as this number changes due to interaction of the operator with the system under investigation. These considerations are illustrated with reference to a cognitive task, starting from synchronization of neuron arrays in a perceptual area and tracing the putative path toward a model building.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009EGUGA..1111488P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009EGUGA..1111488P"><span>An infrastructure for the integration of geoscience instruments and sensors on the Grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pugliese, R.; Prica, M.; Kourousias, G.; Del Linz, A.; Curri, A.</p> <p>2009-04-01</p> <p>The Grid, as a computing paradigm, has long been in the attention of both academia and industry[1]. The distributed and expandable nature of its general architecture result to scalability and more efficient utilisation of the computing infrastructures. The scientific community, including that of geosciences, often handles problems with very high requirements in data processing, transferring, and storing[2,3]. This has raised the interest on Grid technologies but these are often viewed solely as an access gateway to HPC. Suitable Grid infrastructures could provide the geoscience community with additional benefits like those of sharing, remote access and control of scientific systems. These systems can be scientific instruments, sensors, robots, cameras and any other device used in geosciences. The solution for practical, general, and feasible Grid-enabling of such devices requires non-intrusive extensions on core parts of the current Grid architecture. We propose an extended version of an architecture[4] that can serve as the solution to the problem. The solution we propose is called Grid Instrument Element (IE) [5]. It is an addition to the existing core Grid parts; the Computing Element (CE) and the Storage Element (SE) that serve the purposes that their name suggests. The IE that we will be referring to, and the related technologies have been developed in the EU project on the Deployment of Remote Instrumentation Infrastructure (DORII1). In DORII, partners of various scientific communities including those of Earthquake, Environmental science, and Experimental science, have adopted the technology of the Instrument Element in order to integrate to the Grid their devices. The Oceanographic and coastal observation and modelling Mediterranean Ocean Observing Network (OGS2), a DORII partner, is in the process of deploying the above mentioned Grid technologies on two types of observational modules: Argo profiling floats and a novel Autonomous Underwater Vehicle (AUV). In this paper i) we define the need for integration of instrumentation in the Grid, ii) we introduce the solution of the Instrument Element, iii) we demonstrate a suitable end-user web portal for accessing Grid resources, iv) we describe from the Grid-technological point of view the process of the integration to the Grid of two advanced environmental monitoring devices. References [1] M. Surridge, S. Taylor, D. De Roure, and E. Zaluska, "Experiences with GRIA—Industrial Applications on a Web Services Grid," e-Science and Grid Computing, First International Conference on e-Science and Grid Computing, 2005, pp. 98-105. [2] A. Chervenak, I. Foster, C. Kesselman, C. Salisbury, and S. Tuecke, "The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets," Journal of Network and Computer Applications, vol. 23, 2000, pp. 187-200. [3] B. Allcock, J. Bester, J. Bresnahan, A.L. Chervenak, I. Foster, C. Kesselman, S. Meder, V. Nefedova, D. Quesnel, and S. Tuecke, "Data management and transfer in high-performance computational grid environments," Parallel Computing, vol. 28, 2002, pp. 749-771. [4] E. Frizziero, M. Gulmini, F. Lelli, G. Maron, A. Oh, S. Orlando, A. Petrucci, S. Squizzato, and S. Traldi, "Instrument Element: A New Grid component that Enables the Control of Remote Instrumentation," Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06)-Volume 00, IEEE Computer Society Washington, DC, USA, 2006. [5] R. Ranon, L. De Marco, A. Senerchia, S. Gabrielli, L. Chittaro, R. Pugliese, L. Del Cano, F. Asnicar, and M. Prica, "A Web-based Tool for Collaborative Access to Scientific Instruments in Cyberinfrastructures." 1 The DORII project is supported by the European Commission within the 7th Framework Programme (FP7/2007-2013) under grant agreement no. RI-213110. URL: http://www.dorii.eu 2 Istituto Nazionale di Oceanografia e di Geofisica Sperimentale. URL: http://www.ogs.trieste.it</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JPhCS.675c2014B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JPhCS.675c2014B"><span>The scientific data acquisition system of the GAMMA-400 space project</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bobkov, S. G.; Serdin, O. V.; Gorbunov, M. S.; Arkhangelskiy, A. I.; Topchiev, N. P.</p> <p>2016-02-01</p> <p>The description of scientific data acquisition system (SDAS) designed by SRISA for the GAMMA-400 space project is presented. We consider the problem of different level electronics unification: the set of reliable fault-tolerant integrated circuits fabricated on Silicon-on-Insulator 0.25 mkm CMOS technology and the high-speed interfaces and reliable modules used in the space instruments. The characteristics of reliable fault-tolerant very large scale integration (VLSI) technology designed by SRISA for the developing of computation systems for space applications are considered. The scalable net structure of SDAS based on Serial RapidIO interface including real-time operating system BAGET is described too.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN53D3822M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN53D3822M"><span>'Tagger' - a Mac OS X Interactive Graphical Application for Data Inference and Analysis of N-Dimensional Datasets in the Natural Physical Sciences.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Morse, P. E.; Reading, A. M.; Lueg, C.</p> <p>2014-12-01</p> <p>Pattern-recognition in scientific data is not only a computational problem but a human-observer problem as well. Human observation of - and interaction with - data visualization software can augment, select, interrupt and modify computational routines and facilitate processes of pattern and significant feature recognition for subsequent human analysis, machine learning, expert and artificial intelligence systems.'Tagger' is a Mac OS X interactive data visualisation tool that facilitates Human-Computer interaction for the recognition of patterns and significant structures. It is a graphical application developed using the Quartz Composer framework. 'Tagger' follows a Model-View-Controller (MVC) software architecture: the application problem domain (the model) is to facilitate novel ways of abstractly representing data to a human interlocutor, presenting these via different viewer modalities (e.g. chart representations, particle systems, parametric geometry) to the user (View) and enabling interaction with the data (Controller) via a variety of Human Interface Devices (HID). The software enables the user to create an arbitrary array of tags that may be appended to the visualised data, which are then saved into output files as forms of semantic metadata. Three fundamental problems that are not strongly supported by conventional scientific visualisation software are addressed:1] How to visually animate data over time, 2] How to rapidly deploy unconventional parametrically driven data visualisations, 3] How to construct and explore novel interaction models that capture the activity of the end-user as semantic metadata that can be used to computationally enhance subsequent interrogation. Saved tagged data files may be loaded into Tagger, so that tags may be tagged, if desired. Recursion opens up the possibility of refining or overlapping different types of tags, tagging a variety of different POIs or types of events, and of capturing different types of specialist observations of important or noticeable events. Other visualisations and modes of interaction will also be demonstrated, with the aim of discovering knowledge in large datasets in the natural, physical sciences. Fig.1 Wave height data from an oceanographic Wave Rider Buoy. Colors/radii are driven by wave height data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/943286-solving-optimization-problems-computational-grids','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/943286-solving-optimization-problems-computational-grids"><span>Solving optimization problems on computational grids.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Wright, S. J.; Mathematics and Computer Science</p> <p>2001-05-01</p> <p>Multiprocessor computing platforms, which have become more and more widely available since the mid-1980s, are now heavily used by organizations that need to solve very demanding computational problems. Parallel computing is now central to the culture of many research communities. Novel parallel approaches were developed for global optimization, network optimization, and direct-search methods for nonlinear optimization. Activity was particularly widespread in parallel branch-and-bound approaches for various problems in combinatorial and network optimization. As the cost of personal computers and low-end workstations has continued to fall, while the speed and capacity of processors and networks have increased dramatically, 'cluster' platforms havemore » become popular in many settings. A somewhat different type of parallel computing platform know as a computational grid (alternatively, metacomputer) has arisen in comparatively recent times. Broadly speaking, this term refers not to a multiprocessor with identical processing nodes but rather to a heterogeneous collection of devices that are widely distributed, possibly around the globe. The advantage of such platforms is obvious: they have the potential to deliver enormous computing power. Just as obviously, however, the complexity of grids makes them very difficult to use. The Condor team, headed by Miron Livny at the University of Wisconsin, were among the pioneers in providing infrastructure for grid computations. More recently, the Globus project has developed technologies to support computations on geographically distributed platforms consisting of high-end computers, storage and visualization devices, and other scientific instruments. In 1997, we started the metaneos project as a collaborative effort between optimization specialists and the Condor and Globus groups. Our aim was to address complex, difficult optimization problems in several areas, designing and implementing the algorithms and the software infrastructure need to solve these problems on computational grids. This article describes some of the results we have obtained during the first three years of the metaneos project. Our efforts have led to development of the runtime support library MW for implementing algorithms with master-worker control structure on Condor platforms. This work is discussed here, along with work on algorithms and codes for integer linear programming, the quadratic assignment problem, and stochastic linear programmming. Our experiences in the metaneos project have shown that cheap, powerful computational grids can be used to tackle large optimization problems of various types. In an industrial or commercial setting, the results demonstrate that one may not have to buy powerful computational servers to solve many of the large problems arising in areas such as scheduling, portfolio optimization, or logistics; the idle time on employee workstations (or, at worst, an investment in a modest cluster of PCs) may do the job. For the optimization research community, our results motivate further work on parallel, grid-enabled algorithms for solving very large problems of other types. The fact that very large problems can be solved cheaply allows researchers to better understand issues of 'practical' complexity and of the role of heuristics.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/831113','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/831113"><span>ORNL Cray X1 evaluation status report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Agarwal, P.K.; Alexander, R.A.; Apra, E.</p> <p>2004-05-01</p> <p>On August 15, 2002 the Department of Energy (DOE) selected the Center for Computational Sciences (CCS) at Oak Ridge National Laboratory (ORNL) to deploy a new scalable vector supercomputer architecture for solving important scientific problems in climate, fusion, biology, nanoscale materials and astrophysics. ''This program is one of the first steps in an initiative designed to provide U.S. scientists with the computational power that is essential to 21st century scientific leadership,'' said Dr. Raymond L. Orbach, director of the department's Office of Science. In FY03, CCS procured a 256-processor Cray X1 to evaluate the processors, memory subsystem, scalability of themore » architecture, software environment and to predict the expected sustained performance on key DOE applications codes. The results of the micro-benchmarks and kernel bench marks show the architecture of the Cray X1 to be exceptionally fast for most operations. The best results are shown on large problems, where it is not possible to fit the entire problem into the cache of the processors. These large problems are exactly the types of problems that are important for the DOE and ultra-scale simulation. Application performance is found to be markedly improved by this architecture: - Large-scale simulations of high-temperature superconductors run 25 times faster than on an IBM Power4 cluster using the same number of processors. - Best performance of the parallel ocean program (POP v1.4.3) is 50 percent higher than on Japan s Earth Simulator and 5 times higher than on an IBM Power4 cluster. - A fusion application, global GYRO transport, was found to be 16 times faster on the X1 than on an IBM Power3. The increased performance allowed simulations to fully resolve questions raised by a prior study. - The transport kernel in the AGILE-BOLTZTRAN astrophysics code runs 15 times faster than on an IBM Power4 cluster using the same number of processors. - Molecular dynamics simulations related to the phenomenon of photon echo run 8 times faster than previously achieved. Even at 256 processors, the Cray X1 system is already outperforming other supercomputers with thousands of processors for a certain class of applications such as climate modeling and some fusion applications. This evaluation is the outcome of a number of meetings with both high-performance computing (HPC) system vendors and application experts over the past 9 months and has received broad-based support from the scientific community and other agencies.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8499M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8499M"><span>Coupling of a continuum ice sheet model and a discrete element calving model using a scientific workflow system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Memon, Shahbaz; Vallot, Dorothée; Zwinger, Thomas; Neukirchen, Helmut</p> <p>2017-04-01</p> <p>Scientific communities generate complex simulations through orchestration of semi-structured analysis pipelines which involves execution of large workflows on multiple, distributed and heterogeneous computing and data resources. Modeling ice dynamics of glaciers requires workflows consisting of many non-trivial, computationally expensive processing tasks which are coupled to each other. From this domain, we present an e-Science use case, a workflow, which requires the execution of a continuum ice flow model and a discrete element based calving model in an iterative manner. Apart from the execution, this workflow also contains data format conversion tasks that support the execution of ice flow and calving by means of transition through sequential, nested and iterative steps. Thus, the management and monitoring of all the processing tasks including data management and transfer of the workflow model becomes more complex. From the implementation perspective, this workflow model was initially developed on a set of scripts using static data input and output references. In the course of application usage when more scripts or modifications introduced as per user requirements, the debugging and validation of results were more cumbersome to achieve. To address these problems, we identified a need to have a high-level scientific workflow tool through which all the above mentioned processes can be achieved in an efficient and usable manner. We decided to make use of the e-Science middleware UNICORE (Uniform Interface to Computing Resources) that allows seamless and automated access to different heterogenous and distributed resources which is supported by a scientific workflow engine. Based on this, we developed a high-level scientific workflow model for coupling of massively parallel High-Performance Computing (HPC) jobs: a continuum ice sheet model (Elmer/Ice) and a discrete element calving and crevassing model (HiDEM). In our talk we present how the use of a high-level scientific workflow middleware enables reproducibility of results more convenient and also provides a reusable and portable workflow template that can be deployed across different computing infrastructures. Acknowledgements This work was kindly supported by NordForsk as part of the Nordic Center of Excellence (NCoE) eSTICC (eScience Tools for Investigating Climate Change at High Northern Latitudes) and the Top-level Research Initiative NCoE SVALI (Stability and Variation of Arctic Land Ice).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.H52E..02K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.H52E..02K"><span>Making it Easy to Construct Accurate Hydrological Models that Exploit High Performance Computers (Invited)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kees, C. E.; Farthing, M. W.; Terrel, A.; Certik, O.; Seljebotn, D.</p> <p>2013-12-01</p> <p>This presentation will focus on two barriers to progress in the hydrological modeling community, and research and development conducted to lessen or eliminate them. The first is a barrier to sharing hydrological models among specialized scientists that is caused by intertwining the implementation of numerical methods with the implementation of abstract numerical modeling information. In the Proteus toolkit for computational methods and simulation, we have decoupled these two important parts of computational model through separate "physics" and "numerics" interfaces. More recently we have begun developing the Strong Form Language for easy and direct representation of the mathematical model formulation in a domain specific language embedded in Python. The second major barrier is sharing ANY scientific software tools that have complex library or module dependencies, as most parallel, multi-physics hydrological models must have. In this setting, users and developer are dependent on an entire distribution, possibly depending on multiple compilers and special instructions depending on the environment of the target machine. To solve these problem we have developed, hashdist, a stateless package management tool and a resulting portable, open source scientific software distribution.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JCoPh.320...40K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JCoPh.320...40K"><span>On improving the algorithm efficiency in the particle-particle force calculations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kozynchenko, Alexander I.; Kozynchenko, Sergey A.</p> <p>2016-09-01</p> <p>The problem of calculating inter-particle forces in the particle-particle (PP) simulation models takes an important place in scientific computing. Such simulation models are used in diverse scientific applications arising in astrophysics, plasma physics, particle accelerators, etc., where the long-range forces are considered. The inverse-square laws such as Coulomb's law of electrostatic forces and Newton's law of universal gravitation are the examples of laws pertaining to the long-range forces. The standard naïve PP method outlined, for example, by Hockney and Eastwood [1] is straightforward, processing all pairs of particles in a double nested loop. The PP algorithm provides the best accuracy of all possible methods, but its computational complexity is O (Np2), where Np is a total number of particles involved. Too low efficiency of the PP algorithm seems to be the challenging issue in some cases where the high accuracy is required. An example can be taken from the charged particle beam dynamics where, under computing the own space charge of the beam, so-called macro-particles are used (see e.g., Humphries Jr. [2], Kozynchenko and Svistunov [3]).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/951557','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/951557"><span>Adaptive LES Methodology for Turbulent Flow Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Oleg V. Vasilyev</p> <p>2008-06-12</p> <p>Although turbulent flows are common in the world around us, a solution to the fundamental equations that govern turbulence still eludes the scientific community. Turbulence has often been called one of the last unsolved problem in classical physics, yet it is clear that the need to accurately predict the effect of turbulent flows impacts virtually every field of science and engineering. As an example, a critical step in making modern computational tools useful in designing aircraft is to be able to accurately predict the lift, drag, and other aerodynamic characteristics in numerical simulations in a reasonable amount of time. Simulationsmore » that take months to years to complete are much less useful to the design cycle. Much work has been done toward this goal (Lee-Rausch et al. 2003, Jameson 2003) and as cost effective accurate tools for simulating turbulent flows evolve, we will all benefit from new scientific and engineering breakthroughs. The problem of simulating high Reynolds number (Re) turbulent flows of engineering and scientific interest would have been solved with the advent of Direct Numerical Simulation (DNS) techniques if unlimited computing power, memory, and time could be applied to each particular problem. Yet, given the current and near future computational resources that exist and a reasonable limit on the amount of time an engineer or scientist can wait for a result, the DNS technique will not be useful for more than 'unit' problems for the foreseeable future (Moin & Kim 1997, Jimenez & Moin 1991). The high computational cost for the DNS of three dimensional turbulent flows results from the fact that they have eddies of significant energy in a range of scales from the characteristic length scale of the flow all the way down to the Kolmogorov length scale. The actual cost of doing a three dimensional DNS scales as Re{sup 9/4} due to the large disparity in scales that need to be fully resolved. State-of-the-art DNS calculations of isotropic turbulence have recently been completed at the Japanese Earth Simulator (Yokokawa et al. 2002, Kaneda et al. 2003) using a resolution of 40963 (approximately 10{sup 11}) grid points with a Taylor-scale Reynolds number of 1217 (Re {approx} 10{sup 6}). Impressive as these calculations are, performed on one of the world's fastest super computers, more brute computational power would be needed to simulate the flow over the fuselage of a commercial aircraft at cruising speed. Such a calculation would require on the order of 10{sup 16} grid points and would have a Reynolds number in the range of 108. Such a calculation would take several thousand years to simulate one minute of flight time on today's fastest super computers (Moin & Kim 1997). Even using state-of-the-art zonal approaches, which allow DNS calculations that resolve the necessary range of scales within predefined 'zones' in the flow domain, this calculation would take far too long for the result to be of engineering interest when it is finally obtained. Since computing power, memory, and time are all scarce resources, the problem of simulating turbulent flows has become one of how to abstract or simplify the complexity of the physics represented in the full Navier-Stokes (NS) equations in such a way that the 'important' physics of the problem is captured at a lower cost. To do this, a portion of the modes of the turbulent flow field needs to be approximated by a low order model that is cheaper than the full NS calculation. This model can then be used along with a numerical simulation of the 'important' modes of the problem that cannot be well represented by the model. The decision of what part of the physics to model and what kind of model to use has to be based on what physical properties are considered 'important' for the problem. It should be noted that 'nothing is free', so any use of a low order model will by definition lose some information about the original flow.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19860055913&hterms=design+graphic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Ddesign%2Bgraphic','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19860055913&hterms=design+graphic&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D40%26Ntt%3Ddesign%2Bgraphic"><span>Computer graphics and the graphic artist</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Taylor, N. L.; Fedors, E. G.; Pinelli, T. E.</p> <p>1985-01-01</p> <p>A centralized computer graphics system is being developed at the NASA Langley Research Center. This system was required to satisfy multiuser needs, ranging from presentation quality graphics prepared by a graphic artist to 16-mm movie simulations generated by engineers and scientists. While the major thrust of the central graphics system was directed toward engineering and scientific applications, hardware and software capabilities to support the graphic artists were integrated into the design. This paper briefly discusses the importance of computer graphics in research; the central graphics system in terms of systems, software, and hardware requirements; the application of computer graphics to graphic arts, discussed in terms of the requirements for a graphic arts workstation; and the problems encountered in applying computer graphics to the graphic arts. The paper concludes by presenting the status of the central graphics system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/5865467-skyshine-ii-procedure-calculation-effects-structure-design-neutron-primary-gamma-ray-secondary-gamma-ray-dose-rates-air-supplement-number-technical-report','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/5865467-skyshine-ii-procedure-calculation-effects-structure-design-neutron-primary-gamma-ray-secondary-gamma-ray-dose-rates-air-supplement-number-technical-report"><span>SKYSHINE-II procedure: calculation of the effects of structure design on neutron, primary gamma-ray and secondary gamma-ray dose rates in air. Supplement number 1. Technical report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Lampley, C.M.</p> <p>1981-01-01</p> <p>This report describes many of the computational methods employed within the SKYSHINE-II program. A brief description of the new data base is included, as is a description of the input data requirements and formats needed to properly execute a SKYSHINE-II problem. Utilization instructions for the program are provided for operation of the SKYSHINE-II Code on the Brookhaven National Laboratory Central Scientific Computing Facility (See NUREG/CR-0781, RRA-T7901 for complete information).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/12804265','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/12804265"><span>Floods in a changing climate: a review.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hunt, J C R</p> <p>2002-07-15</p> <p>This paper begins with an analysis of flooding as a natural disaster for which the solutions to the environmental, social and economic problems are essentially those of identifying and overcoming hazards and vulnerability, reducing risk and damaging consequences. Long-term solutions to flooding problems, especially in a changing climate, should be sought in the wider context of developing more sustainable social organization, economics and technology. Then, developments are described of how scientific understanding, supported by practical modelling, is leading to predictions of how human-induced changes to climatic and geological conditions are likely to influence flooding over at least the next 300 years, through their influences on evaporation, precipitation, run-off, wind storm and sea-level rise. Some of the outstanding scientific questions raised by these problems are highlighted, such as the statistical and deterministic prediction of extreme events, the understanding and modelling of mechanisms that operate on varying length- and time-scales, and the complex interactions between biological, ecological and physical problems. Some options for reducing the impact of flooding by new technology include both improved prediction and monitoring with computer models, and remote sensing, flexible and focused warning systems, and permanent and temporary flood-reduction systems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014APS..MARB27012R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014APS..MARB27012R"><span>Computational Science at the Argonne Leadership Computing Facility</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Romero, Nichols</p> <p>2014-03-01</p> <p>The goal of the Argonne Leadership Computing Facility (ALCF) is to extend the frontiers of science by solving problems that require innovative approaches and the largest-scale computing systems. ALCF's most powerful computer - Mira, an IBM Blue Gene/Q system - has nearly one million cores. How does one program such systems? What software tools are available? Which scientific and engineering applications are able to utilize such levels of parallelism? This talk will address these questions and describe a sampling of projects that are using ALCF systems in their research, including ones in nanoscience, materials science, and chemistry. Finally, the ways to gain access to ALCF resources will be presented. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3835373','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3835373"><span>Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kadima, Hubert; Granado, Bertrand</p> <p>2013-01-01</p> <p>We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19870018011','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19870018011"><span>Automated problem scheduling and reduction of synchronization delay effects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Saltz, Joel H.</p> <p>1987-01-01</p> <p>It is anticipated that in order to make effective use of many future high performance architectures, programs will have to exhibit at least a medium grained parallelism. A framework is presented for partitioning very sparse triangular systems of linear equations that is designed to produce favorable preformance results in a wide variety of parallel architectures. Efficient methods for solving these systems are of interest because: (1) they provide a useful model problem for use in exploring heuristics for the aggregation, mapping and scheduling of relatively fine grained computations whose data dependencies are specified by directed acrylic graphs, and (2) because such efficient methods can find direct application in the development of parallel algorithms for scientific computation. Simple expressions are derived that describe how to schedule computational work with varying degrees of granularity. The Encore Multimax was used as a hardware simulator to investigate the performance effects of using the partitioning techniques presented in shared memory architectures with varying relative synchronization costs.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_12");'>12</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li class="active"><span>14</span></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_14 --> <div id="page_15" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="281"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24319361','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24319361"><span>Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand</p> <p>2013-01-01</p> <p>We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017PhDT.......220M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017PhDT.......220M"><span>Emerging Nanophotonic Applications Explored with Advanced Scientific Parallel Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Meng, Xiang</p> <p></p> <p>The domain of nanoscale optical science and technology is a combination of the classical world of electromagnetics and the quantum mechanical regime of atoms and molecules. Recent advancements in fabrication technology allows the optical structures to be scaled down to nanoscale size or even to the atomic level, which are far smaller than the wavelength they are designed for. These nanostructures can have unique, controllable, and tunable optical properties and their interactions with quantum materials can have important near-field and far-field optical response. Undoubtedly, these optical properties can have many important applications, ranging from the efficient and tunable light sources, detectors, filters, modulators, high-speed all-optical switches; to the next-generation classical and quantum computation, and biophotonic medical sensors. This emerging research of nanoscience, known as nanophotonics, is a highly interdisciplinary field requiring expertise in materials science, physics, electrical engineering, and scientific computing, modeling and simulation. It has also become an important research field for investigating the science and engineering of light-matter interactions that take place on wavelength and subwavelength scales where the nature of the nanostructured matter controls the interactions. In addition, the fast advancements in the computing capabilities, such as parallel computing, also become as a critical element for investigating advanced nanophotonic devices. This role has taken on even greater urgency with the scale-down of device dimensions, and the design for these devices require extensive memory and extremely long core hours. Thus distributed computing platforms associated with parallel computing are required for faster designs processes. Scientific parallel computing constructs mathematical models and quantitative analysis techniques, and uses the computing machines to analyze and solve otherwise intractable scientific challenges. In particular, parallel computing are forms of computation operating on the principle that large problems can often be divided into smaller ones, which are then solved concurrently. In this dissertation, we report a series of new nanophotonic developments using the advanced parallel computing techniques. The applications include the structure optimizations at the nanoscale to control both the electromagnetic response of materials, and to manipulate nanoscale structures for enhanced field concentration, which enable breakthroughs in imaging, sensing systems (chapter 3 and 4) and improve the spatial-temporal resolutions of spectroscopies (chapter 5). We also report the investigations on the confinement study of optical-matter interactions at the quantum mechanical regime, where the size-dependent novel properties enhanced a wide range of technologies from the tunable and efficient light sources, detectors, to other nanophotonic elements with enhanced functionality (chapter 6 and 7).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19990019273&hterms=Population+ecology&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DPopulation%2Becology','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19990019273&hterms=Population+ecology&qs=N%3D0%26Ntk%3DAll%26Ntx%3Dmode%2Bmatchall%26Ntt%3DPopulation%2Becology"><span>Mathematical and Computational Challenges in Population Biology and Ecosystems Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.</p> <p>1997-01-01</p> <p>Mathematical and computational approaches provide powerful tools in the study of problems in population biology and ecosystems science. The subject has a rich history intertwined with the development of statistics and dynamical systems theory, but recent analytical advances, coupled with the enhanced potential of high-speed computation, have opened up new vistas and presented new challenges. Key challenges involve ways to deal with the collective dynamics of heterogeneous ensembles of individuals, and to scale from small spatial regions to large ones. The central issues-understanding how detail at one scale makes its signature felt at other scales, and how to relate phenomena across scales-cut across scientific disciplines and go to the heart of algorithmic development of approaches to high-speed computation. Examples are given from ecology, genetics, epidemiology, and immunology.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940019940','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940019940"><span>Performance of the engineering analysis and data system 2 common file system</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Debrunner, Linda S.</p> <p>1993-01-01</p> <p>The Engineering Analysis and Data System (EADS) was used from April 1986 to July 1993 to support large scale scientific and engineering computation (e.g. computational fluid dynamics) at Marshall Space Flight Center. The need for an updated system resulted in a RFP in June 1991, after which a contract was awarded to Cray Grumman. EADS II was installed in February 1993, and by July 1993 most users were migrated. EADS II is a network of heterogeneous computer systems supporting scientific and engineering applications. The Common File System (CFS) is a key component of this system. The CFS provides a seamless, integrated environment to the users of EADS II including both disk and tape storage. UniTree software is used to implement this hierarchical storage management system. The performance of the CFS suffered during the early months of the production system. Several of the performance problems were traced to software bugs which have been corrected. Other problems were associated with hardware. However, the use of NFS in UniTree UCFM software limits the performance of the system. The performance issues related to the CFS have led to a need to develop a greater understanding of the CFS organization. This paper will first describe the EADS II with emphasis on the CFS. Then, a discussion of mass storage systems will be presented, and methods of measuring the performance of the Common File System will be outlined. Finally, areas for further study will be identified and conclusions will be drawn.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1985CoPhC..37..109O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1985CoPhC..37..109O"><span>WATERLOPP V2/64: A highly parallel machine for numerical computation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ostlund, Neil S.</p> <p>1985-07-01</p> <p>Current technological trends suggest that the high performance scientific machines of the future are very likely to consist of a large number (greater than 1024) of processors connected and communicating with each other in some as yet undetermined manner. Such an assembly of processors should behave as a single machine in obtaining numerical solutions to scientific problems. However, the appropriate way of organizing both the hardware and software of such an assembly of processors is an unsolved and active area of research. It is particularly important to minimize the organizational overhead of interprocessor comunication, global synchronization, and contention for shared resources if the performance of a large number ( n) of processors is to be anything like the desirable n times the performance of a single processor. In many situations, adding a processor actually decreases the performance of the overall system since the extra organizational overhead is larger than the extra processing power added. The systolic loop architecture is a new multiple processor architecture which attemps at a solution to the problem of how to organize a large number of asynchronous processors into an effective computational system while minimizing the organizational overhead. This paper gives a brief overview of the basic systolic loop architecture, systolic loop algorithms for numerical computation, and a 64-processor implementation of the architecture, WATERLOOP V2/64, that is being used as a testbed for exploring the hardware, software, and algorithmic aspects of the architecture.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017AIPC.1797b0010L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017AIPC.1797b0010L"><span>Methodology of problem-based learning engineering and technology and of its implementation with modern computer resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lebedev, A. A.; Ivanova, E. G.; Komleva, V. A.; Klokov, N. M.; Komlev, A. A.</p> <p>2017-01-01</p> <p>The considered method of learning the basics of microelectronic circuits and systems amplifier enables one to understand electrical processes deeper, to understand the relationship between static and dynamic characteristics and, finally, bring the learning process to the cognitive process. The scheme of problem-based learning can be represented by the following sequence of procedures: the contradiction is perceived and revealed; the cognitive motivation is provided by creating a problematic situation (the mental state of the student), moving the desire to solve the problem, to raise the question "why?", the hypothesis is made; searches for solutions are implemented; the answer is looked for. Due to the complexity of architectural schemes in the work the modern methods of computer analysis and synthesis are considered in the work. Examples of engineering by students in the framework of students' scientific and research work of analog circuits with improved performance based on standard software and software developed at the Department of Microelectronics MEPhI.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3145372','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3145372"><span>Performance analysis of a dual-tree algorithm for computing spatial distance histograms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Shaoping; Tu, Yi-Cheng; Xia, Yuni</p> <p>2011-01-01</p> <p>Many scientific and engineering fields produce large volume of spatiotemporal data. The storage, retrieval, and analysis of such data impose great challenges to database systems design. Analysis of scientific spatiotemporal data often involves computing functions of all point-to-point interactions. One such analytics, the Spatial Distance Histogram (SDH), is of vital importance to scientific discovery. Recently, algorithms for efficient SDH processing in large-scale scientific databases have been proposed. These algorithms adopt a recursive tree-traversing strategy to process point-to-point distances in the visited tree nodes in batches, thus require less time when compared to the brute-force approach where all pairwise distances have to be computed. Despite the promising experimental results, the complexity of such algorithms has not been thoroughly studied. In this paper, we present an analysis of such algorithms based on a geometric modeling approach. The main technique is to transform the analysis of point counts into a problem of quantifying the area of regions where pairwise distances can be processed in batches by the algorithm. From the analysis, we conclude that the number of pairwise distances that are left to be processed decreases exponentially with more levels of the tree visited. This leads to the proof of a time complexity lower than the quadratic time needed for a brute-force algorithm and builds the foundation for a constant-time approximate algorithm. Our model is also general in that it works for a wide range of point spatial distributions, histogram types, and space-partitioning options in building the tree. PMID:21804753</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960045440','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960045440"><span>Artificial Boundary Conditions Based on the Difference Potentials Method</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tsynkov, Semyon V.</p> <p>1996-01-01</p> <p>While numerically solving a problem initially formulated on an unbounded domain, one typically truncates this domain, which necessitates setting the artificial boundary conditions (ABC's) at the newly formed external boundary. The issue of setting the ABC's appears to be most significant in many areas of scientific computing, for example, in problems originating from acoustics, electrodynamics, solid mechanics, and fluid dynamics. In particular, in computational fluid dynamics (where external problems present a wide class of practically important formulations) the proper treatment of external boundaries may have a profound impact on the overall quality and performance of numerical algorithms. Most of the currently used techniques for setting the ABC's can basically be classified into two groups. The methods from the first group (global ABC's) usually provide high accuracy and robustness of the numerical procedure but often appear to be fairly cumbersome and (computationally) expensive. The methods from the second group (local ABC's) are, as a rule, algorithmically simple, numerically cheap, and geometrically universal; however, they usually lack accuracy of computations. In this paper we first present a survey and provide a comparative assessment of different existing methods for constructing the ABC's. Then, we describe a relatively new ABC's technique of ours and review the corresponding results. This new technique, in our opinion, is currently one of the most promising in the field. It enables one to construct such ABC's that combine the advantages relevant to the two aforementioned classes of existing methods. Our approach is based on application of the difference potentials method attributable to V. S. Ryaben'kii. This approach allows us to obtain highly accurate ABC's in the form of certain (nonlocal) boundary operator equations. The operators involved are analogous to the pseudodifferential boundary projections first introduced by A. P. Calderon and then also studied by R. T. Seeley. The apparatus of the boundary pseudodifferential equations, which has formerly been used mostly in the qualitative theory of integral equations and PDE'S, is now effectively employed for developing numerical methods in the different fields of scientific computing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948467','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3948467"><span>Link-Based Similarity Measures Using Reachability Vectors</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Yoon, Seok-Ho; Kim, Ji-Soo; Ryu, Minsoo; Choi, Ho-Jin</p> <p>2014-01-01</p> <p>We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the “Random Walk with Restart” strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures. PMID:24701188</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/21367284-principle-quantumness-quantumness-relativity-computational-grand-unification','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/21367284-principle-quantumness-quantumness-relativity-computational-grand-unification"><span>On the 'principle of the quantumness', the quantumness of Relativity, and the computational grand-unification</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>D'Ariano, Giacomo Mauro</p> <p>2010-05-04</p> <p>I will argue that the proposal of establishing operational foundations of Quantum Theory should have top-priority, and that the Lucien Hardy's program on Quantum Gravity should be paralleled by an analogous program on Quantum Field Theory (QFT), which needs to be reformulated, notwithstanding its experimental success. In this paper, after reviewing recently suggested operational 'principles of the quantumness', I address the problem on whether Quantum Theory and Special Relativity are unrelated theories, or instead, if the one implies the other. I show how Special Relativity can be indeed derived from causality of Quantum Theory, within the computational paradigm 'the universemore » is a huge quantum computer', reformulating QFT as a Quantum-Computational Field Theory (QCFT). In QCFT Special Relativity emerges from the fabric of the computational network, which also naturally embeds gauge invariance. In this scheme even the quantization rule and the Planck constant can in principle be derived as emergent from the underlying causal tapestry of space-time. In this way Quantum Theory remains the only theory operating the huge computer of the universe.Is the computational paradigm only a speculative tautology (theory as simulation of reality), or does it have a scientific value? The answer will come from Occam's razor, depending on the mathematical simplicity of QCFT. Here I will just start scratching the surface of QCFT, analyzing simple field theories, including Dirac's. The number of problems and unmotivated recipes that plague QFT strongly motivates us to undertake the QCFT project, since QCFT makes all such problems manifest, and forces a re-foundation of QFT.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFMIN23B1729K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFMIN23B1729K"><span>EarthCube Activities: Community Engagement Advancing Geoscience Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kinkade, D.</p> <p>2015-12-01</p> <p>Our ability to advance scientific research in order to better understand complex Earth systems, address emerging geoscience problems, and meet societal challenges is increasingly dependent upon the concept of Open Science and Data. Although these terms are relatively new to the world of research, Open Science and Data in this context may be described as transparency in the scientific process. This includes the discoverability, public accessibility and reusability of scientific data, as well as accessibility and transparency of scientific communication (www.openscience.org). Scientists and the US government alike are realizing the critical need for easy discovery and access to multidisciplinary data to advance research in the geosciences. The NSF-supported EarthCube project was created to meet this need. EarthCube is developing a community-driven common cyberinfrastructure for the purpose of accessing, integrating, analyzing, sharing and visualizing all forms of data and related resources through advanced technological and computational capabilities. Engaging the geoscience community in EarthCube's development is crucial to its success, and EarthCube is providing several opportunities for geoscience involvement. This presentation will provide an overview of the activities EarthCube is employing to entrain the community in the development process, from governance development and strategic planning, to technical needs gathering. Particular focus will be given to the collection of science-driven use cases as a means of capturing scientific and technical requirements. Such activities inform the development of key technical and computational components that collectively will form a cyberinfrastructure to meet the research needs of the geoscience community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1367456','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1367456"><span>XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Mid-year report FY17 Q2</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Moreland, Kenneth D.; Pugmire, David; Rogers, David</p> <p></p> <p>The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1408391','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1408391"><span>XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY17.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Moreland, Kenneth D.; Pugmire, David; Rogers, David</p> <p></p> <p>The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1257617','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1257617"><span>XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem. Mid-year report FY16 Q2</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank</p> <p></p> <p>The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1234815','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1234815"><span>XVis: Visualization for the Extreme-Scale Scientific-Computation Ecosystem: Year-end report FY15 Q4.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Moreland, Kenneth D.; Sewell, Christopher; Childs, Hank</p> <p></p> <p>The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. This project provides the necessary research and infrastructure for scientific discovery in this new computational ecosystem by addressingmore » four interlocking challenges: emerging processor technology, in situ integration, usability, and proxy analysis.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=ALS&pg=2&id=EJ1163435','ERIC'); return false;" href="https://eric.ed.gov/?q=ALS&pg=2&id=EJ1163435"><span>A Letter on Ancaiani et al. "Evaluating Scientific Research in Italy: The 2004-10 Research Evaluation Exercise"</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Baccini, Alberto; De Nicolao, Giuseppe</p> <p>2017-01-01</p> <p>This letter documents some problems in Ancaiani et al. (2015). Namely the evaluation of concordance, based on Cohen's kappa, reported by Ancaiani et al. was not computed on the whole random sample of 9,199 articles, but on a subset of 7,597 articles. The kappas relative to the whole random sample were in the range 0.07-0.15, indicating an…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940011325','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940011325"><span>NAS technical summaries: Numerical aerodynamic simulation program, March 1991 - February 1992</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1992-01-01</p> <p>NASA created the Numerical Aerodynamic Simulation (NAS) Program in 1987 to focus resources on solving critical problems in aeroscience and related disciplines by utilizing the power of the most advanced supercomputers available. The NAS Program provides scientists with the necessary computing power to solve today's most demanding computational fluid dynamics problems and serves as a pathfinder in integrating leading-edge supercomputing technologies, thus benefiting other supercomputer centers in Government and industry. This report contains selected scientific results from the 1991-92 NAS Operational Year, March 4, 1991 to March 3, 1992, which is the fifth year of operation. During this year, the scientific community was given access to a Cray-2 and a Cray Y-MP. The Cray-2, the first generation supercomputer, has four processors, 256 megawords of central memory, and a total sustained speed of 250 million floating point operations per second. The Cray Y-MP, the second generation supercomputer, has eight processors and a total sustained speed of one billion floating point operations per second. Additional memory was installed this year, doubling capacity from 128 to 256 megawords of solid-state storage-device memory. Because of its higher performance, the Cray Y-MP delivered approximately 77 percent of the total number of supercomputer hours used during this year.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060026534','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060026534"><span>Distributed Computing Architecture for Image-Based Wavefront Sensing and 2 D FFTs</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Smith, Jeffrey S.; Dean, Bruce H.; Haghani, Shadan</p> <p>2006-01-01</p> <p>Image-based wavefront sensing (WFS) provides significant advantages over interferometric-based wavefi-ont sensors such as optical design simplicity and stability. However, the image-based approach is computational intensive, and therefore, specialized high-performance computing architectures are required in applications utilizing the image-based approach. The development and testing of these high-performance computing architectures are essential to such missions as James Webb Space Telescope (JWST), Terrestial Planet Finder-Coronagraph (TPF-C and CorSpec), and Spherical Primary Optical Telescope (SPOT). The development of these specialized computing architectures require numerous two-dimensional Fourier Transforms, which necessitate an all-to-all communication when applied on a distributed computational architecture. Several solutions for distributed computing are presented with an emphasis on a 64 Node cluster of DSPs, multiple DSP FPGAs, and an application of low-diameter graph theory. Timing results and performance analysis will be presented. The solutions offered could be applied to other all-to-all communication and scientifically computationally complex problems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009CoPhC.180.2303M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009CoPhC.180.2303M"><span>GANGA: A tool for computational-task management and easy access to Grid resources</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mościcki, J. T.; Brochu, F.; Ebke, J.; Egede, U.; Elmsheuser, J.; Harrison, K.; Jones, R. W. L.; Lee, H. C.; Liko, D.; Maier, A.; Muraru, A.; Patrick, G. N.; Pajchel, K.; Reece, W.; Samset, B. H.; Slater, M. W.; Soroko, A.; Tan, C. L.; van der Ster, D. C.; Williams, M.</p> <p>2009-11-01</p> <p>In this paper, we present the computational task-management tool GANGA, which allows for the specification, submission, bookkeeping and post-processing of computational tasks on a wide set of distributed resources. GANGA has been developed to solve a problem increasingly common in scientific projects, which is that researchers must regularly switch between different processing systems, each with its own command set, to complete their computational tasks. GANGA provides a homogeneous environment for processing data on heterogeneous resources. We give examples from High Energy Physics, demonstrating how an analysis can be developed on a local system and then transparently moved to a Grid system for processing of all available data. GANGA has an API that can be used via an interactive interface, in scripts, or through a GUI. Specific knowledge about types of tasks or computational resources is provided at run-time through a plugin system, making new developments easy to integrate. We give an overview of the GANGA architecture, give examples of current use, and demonstrate how GANGA can be used in many different areas of science. Catalogue identifier: AEEN_v1_0 Program summary URL:<ce:inter-ref xlink:role="http://www.elsevier.com/xml/linking-roles/text/html" xlink:href="http://cpc.cs.qub.ac.uk/summaries/AEEN_v1_0.html" xlink:type="simple">http://cpc.cs.qub.ac.uk/summaries/AEEN_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL No. of lines in distributed program, including test data, etc.: 224 590 No. of bytes in distributed program, including test data, etc.: 14 365 315 Distribution format: tar.gz Programming language: Python Computer: personal computers, laptops Operating system: Linux/Unix RAM: 1 MB Classification: 6.2, 6.5 Nature of problem: Management of computational tasks for scientific applications on heterogenous distributed systems, including local, batch farms, opportunistic clusters and Grids. Solution method: High-level job management interface, including command line, scripting and GUI components. Restrictions: Access to the distributed resources depends on the installed, 3rd party software such as batch system client or Grid user interface.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3544923','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3544923"><span>Solving Hard Computational Problems Efficiently: Asymptotic Parametric Complexity 3-Coloring Algorithm</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Martín H., José Antonio</p> <p>2013-01-01</p> <p>Many practical problems in almost all scientific and technological disciplines have been classified as computationally hard (NP-hard or even NP-complete). In life sciences, combinatorial optimization problems frequently arise in molecular biology, e.g., genome sequencing; global alignment of multiple genomes; identifying siblings or discovery of dysregulated pathways. In almost all of these problems, there is the need for proving a hypothesis about certain property of an object that can be present if and only if it adopts some particular admissible structure (an NP-certificate) or be absent (no admissible structure), however, none of the standard approaches can discard the hypothesis when no solution can be found, since none can provide a proof that there is no admissible structure. This article presents an algorithm that introduces a novel type of solution method to “efficiently” solve the graph 3-coloring problem; an NP-complete problem. The proposed method provides certificates (proofs) in both cases: present or absent, so it is possible to accept or reject the hypothesis on the basis of a rigorous proof. It provides exact solutions and is polynomial-time (i.e., efficient) however parametric. The only requirement is sufficient computational power, which is controlled by the parameter . Nevertheless, here it is proved that the probability of requiring a value of to obtain a solution for a random graph decreases exponentially: , making tractable almost all problem instances. Thorough experimental analyses were performed. The algorithm was tested on random graphs, planar graphs and 4-regular planar graphs. The obtained experimental results are in accordance with the theoretical expected results. PMID:23349711</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_13");'>13</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li class="active"><span>15</span></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_15 --> <div id="page_16" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="301"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19800008508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19800008508"><span>Symbolic-numeric interface: A review</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ng, E. W.</p> <p>1980-01-01</p> <p>A survey of the use of a combination of symbolic and numerical calculations is presented. Symbolic calculations primarily refer to the computer processing of procedures from classical algebra, analysis, and calculus. Numerical calculations refer to both numerical mathematics research and scientific computation. This survey is intended to point out a large number of problem areas where a cooperation of symbolic and numerical methods is likely to bear many fruits. These areas include such classical operations as differentiation and integration, such diverse activities as function approximations and qualitative analysis, and such contemporary topics as finite element calculations and computation complexity. It is contended that other less obvious topics such as the fast Fourier transform, linear algebra, nonlinear analysis and error analysis would also benefit from a synergistic approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006AGUSMIN34A..01R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006AGUSMIN34A..01R"><span>End-to-end Cyberinfrastructure and Data Services for Earth System Science Education and Research: A vision for the future</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ramamurthy, M. K.</p> <p>2006-05-01</p> <p>We live in an era of an unprecedented data volumes, multidisciplinary analysis and synthesis, and active, learner-centered education emphasis. For instance, a new generation of satellite instruments is being designed for GOES-R and NPOESS programs to deliver terabytes of data each day. Similarly, high-resolution, coupled models run over a wide range of temporal scales are generating data at unprecedented rates. Complex environmental problems such as El Nino/Southern Oscillation, climate change, and water cycle transcend not only disciplinary but also geographic boundaries, with their impacts and implications touching every region and community of the world. The understanding and solution to these inherently global scientific and social problems requires integrated observations that cover all areas of the globe, international sharing and flow of data, and earth system science approaches. Contemporary education strategies recommend adopting an Earth system science approach for teaching the geosciences, employing new pedagogical techniques such as enquiry-based learning and hands-on activities. Needless to add, today's education and research enterprise depends heavily on easy to use, robust, flexible and scalable cyberinfrastructure, especially on the ready availability of quality data and appropriate tools to manipulate and integrate those data. Fortunately, rapid advances in computing, communication and information technologies have provided solutions that can are being applied to advance teaching, research, and service. The exponential growth in the use of the Internet in education and research, largely due to the advent of the World Wide Web, is well documented. On the other hand, how other technological and community trends have shaped the development and application of cyberinfrastructure, especially in the data services area, is less well understood. For example, the computing industry is converging on an approach called Web services that enables a standard and yet revolutionary way of building applications and methods to connect and exchange information over the Web. This new approach, based on XML - a widely accepted format for exchanging data and corresponding semantics over the Internet - enables applications, computer systems, and information processes to work together in fundamentally different ways. Likewise, the advent of digital libraries, grid computing platforms, interoperable frameworks, standards and protocols, open-source software, and community atmospheric models have been important drivers in shaping the use of a new generation of end-to-end cyberinfrastructure for solving some of the most challenging scientific and educational problems. In this talk, I will present an overview of the scientific, technological, and educational landscape, discuss recent developments in cyberinfrastructure, and Unidata's role in and vision for providing easy-to use, robust, end-to-end data services for solving geoscientific problems and advancing student learning.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2144920','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2144920"><span>Identifying problem and compulsive gamblers.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>van Es, R.</p> <p>2000-01-01</p> <p>OBJECTIVE: To present a meta-analysis of current research on the prevalence, identification, and treatment of problem and compulsive gamblers. QUALITY OF EVIDENCE: Problem and compulsive gambling was not a socio-scientific concern until the last two decades. Hence research on this topic is limited. The summary and analysis for this paper relied on computer searches of journal and news abstracts in addition to direct contact with organizations addressing the identification and treatment of compulsive gamblers. MAIN MESSAGE: An estimated 5% of those who gamble run into problems. About 1% of those who gamble are predicted to experience serious problems. Successful treatment of problem and compulsive gambling continues to be a challenge. Although cognitive therapy has been the favoured approach, a combination of several therapeutic approaches is advocated. CONCLUSIONS: Problem and compulsive gambling can present a real health threat. As with other addictions, treatment strategies continue to be a baffling social problem. Aware and informed physicians can have a pivotal role in the difficult process of identifying, acknowledging, and remediating problem and compulsive gambling. PMID:10907572</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3874632','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3874632"><span>An automated and reproducible workflow for running and analyzing neural simulations using Lancet and IPython Notebook</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Stevens, Jean-Luc R.; Elver, Marco; Bednar, James A.</p> <p>2013-01-01</p> <p>Lancet is a new, simulator-independent Python utility for succinctly specifying, launching, and collating results from large batches of interrelated computationally demanding program runs. This paper demonstrates how to combine Lancet with IPython Notebook to provide a flexible, lightweight, and agile workflow for fully reproducible scientific research. This informal and pragmatic approach uses IPython Notebook to capture the steps in a scientific computation as it is gradually automated and made ready for publication, without mandating the use of any separate application that can constrain scientific exploration and innovation. The resulting notebook concisely records each step involved in even very complex computational processes that led to a particular figure or numerical result, allowing the complete chain of events to be replicated automatically. Lancet was originally designed to help solve problems in computational neuroscience, such as analyzing the sensitivity of a complex simulation to various parameters, or collecting the results from multiple runs with different random starting points. However, because it is never possible to know in advance what tools might be required in future tasks, Lancet has been designed to be completely general, supporting any type of program as long as it can be launched as a process and can return output in the form of files. For instance, Lancet is also heavily used by one of the authors in a separate research group for launching batches of microprocessor simulations. This general design will allow Lancet to continue supporting a given research project even as the underlying approaches and tools change. PMID:24416014</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1364635','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1364635"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin</p> <p></p> <p>The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3296692','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3296692"><span>Phylo: A Citizen Science Approach for Improving Multiple Sequence Alignment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kam, Alfred; Kwak, Daniel; Leung, Clarence; Wu, Chu; Zarour, Eleyine; Sarmenta, Luis; Blanchette, Mathieu; Waldispühl, Jérôme</p> <p>2012-01-01</p> <p>Background Comparative genomics, or the study of the relationships of genome structure and function across different species, offers a powerful tool for studying evolution, annotating genomes, and understanding the causes of various genetic disorders. However, aligning multiple sequences of DNA, an essential intermediate step for most types of analyses, is a difficult computational task. In parallel, citizen science, an approach that takes advantage of the fact that the human brain is exquisitely tuned to solving specific types of problems, is becoming increasingly popular. There, instances of hard computational problems are dispatched to a crowd of non-expert human game players and solutions are sent back to a central server. Methodology/Principal Findings We introduce Phylo, a human-based computing framework applying “crowd sourcing” techniques to solve the Multiple Sequence Alignment (MSA) problem. The key idea of Phylo is to convert the MSA problem into a casual game that can be played by ordinary web users with a minimal prior knowledge of the biological context. We applied this strategy to improve the alignment of the promoters of disease-related genes from up to 44 vertebrate species. Since the launch in November 2010, we received more than 350,000 solutions submitted from more than 12,000 registered users. Our results show that solutions submitted contributed to improving the accuracy of up to 70% of the alignment blocks considered. Conclusions/Significance We demonstrate that, combined with classical algorithms, crowd computing techniques can be successfully used to help improving the accuracy of MSA. More importantly, we show that an NP-hard computational problem can be embedded in casual game that can be easily played by people without significant scientific training. This suggests that citizen science approaches can be used to exploit the billions of “human-brain peta-flops” of computation that are spent every day playing games. Phylo is available at: http://phylo.cs.mcgill.ca. PMID:22412834</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4456611','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4456611"><span>Redrawing the frontiers in the age of post-publication review</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Galbraith, David W.</p> <p>2015-01-01</p> <p>Publication forms the core structure supporting the development and transmission of scientific knowledge. For this reason, it is essential that the highest standards of quality control be maintained, in particular to ensure that the information being transmitted allows reproducible replication of the described experiments, and that the interpretation of the results is sound. Quality control has traditionally involved editorial decisions based on anonymous pre-publication peer review. Post-publication review of individual articles took the lesser role since it did not feed directly back to the original literature. Rapid advances in computer and communications technologies over the last thirty years have revolutionized scientific publication, and the role and scope of post-publication review has greatly expanded. This perspective examines the ways in which pre- and post-publication peer review influence the scientific literature, and in particular how they might best be redrawn to deal with the twin problems of scientific non-reproducibility and fraud increasingly encountered at the frontiers of science. PMID:26097488</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT........93K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT........93K"><span>Physical modeling and high-performance GPU computing for characterization, interception, and disruption of hazardous near-Earth objects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kaplinger, Brian Douglas</p> <p></p> <p>For the past few decades, both the scientific community and the general public have been becoming more aware that the Earth lives in a shooting gallery of small objects. We classify all of these asteroids and comets, known or unknown, that cross Earth's orbit as near-Earth objects (NEOs). A look at our geologic history tells us that NEOs have collided with Earth in the past, and we expect that they will continue to do so. With thousands of known NEOs crossing the orbit of Earth, there has been significant scientific interest in developing the capability to deflect an NEO from an impacting trajectory. This thesis applies the ideas of Smoothed Particle Hydrodynamics (SPH) theory to the NEO disruption problem. A simulation package was designed that allows efficacy simulation to be integrated into the mission planning and design process. This is done by applying ideas in high-performance computing (HPC) on the computer graphics processing unit (GPU). Rather than prove a concept through large standalone simulations on a supercomputer, a highly parallel structure allows for flexible, target dependent questions to be resolved. Built around nonclassified data and analysis, this computer package will allow academic institutions to better tackle the issue of NEO mitigation effectiveness.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010hocc.book..379C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010hocc.book..379C"><span>Scientific Services on the Cloud</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chapman, David; Joshi, Karuna P.; Yesha, Yelena; Halem, Milt; Yesha, Yaacov; Nguyen, Phuong</p> <p></p> <p>Scientific Computing was one of the first every applications for parallel and distributed computation. To this date, scientific applications remain some of the most compute intensive, and have inspired creation of petaflop compute infrastructure such as the Oak Ridge Jaguar and Los Alamos RoadRunner. Large dedicated hardware infrastructure has become both a blessing and a curse to the scientific community. Scientists are interested in cloud computing for much the same reason as businesses and other professionals. The hardware is provided, maintained, and administrated by a third party. Software abstraction and virtualization provide reliability, and fault tolerance. Graduated fees allow for multi-scale prototyping and execution. Cloud computing resources are only a few clicks away, and by far the easiest high performance distributed platform to gain access to. There may still be dedicated infrastructure for ultra-scale science, but the cloud can easily play a major part of the scientific computing initiative.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1168433','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1168433"><span>Center for Center for Technology for Advanced Scientific Component Software (TASCS)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Kostadin, Damevski</p> <p></p> <p>A resounding success of the Scientific Discovery through Advanced Computing (SciDAC) program is that high-performance computational science is now universally recognized as a critical aspect of scientific discovery [71], complementing both theoretical and experimental research. As scientific communities prepare to exploit unprecedented computing capabilities of emerging leadership-class machines for multi-model simulations at the extreme scale [72], it is more important than ever to address the technical and social challenges of geographically distributed teams that combine expertise in domain science, applied mathematics, and computer science to build robust and flexible codes that can incorporate changes over time. The Center for Technologymore » for Advanced Scientific Component Software (TASCS)1 tackles these these issues by exploiting component-based software development to facilitate collaborative high-performance scientific computing.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27047610','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27047610"><span>Games that Enlist Collective Intelligence to Solve Complex Scientific Problems.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Burnett, Stephen; Furlong, Michelle; Melvin, Paul Guy; Singiser, Richard</p> <p>2016-03-01</p> <p>There is great value in employing the collective problem-solving power of large groups of people. Technological advances have allowed computer games to be utilized by a diverse population to solve problems. Science games are becoming more popular and cover various areas such as sequence alignments, DNA base-pairing, and protein and RNA folding. While these tools have been developed for the general population, they can also be used effectively in the classroom to teach students about various topics. Many games also employ a social component that entices students to continue playing and thereby to continue learning. The basic functions of game play and the potential of game play as a tool in the classroom are discussed in this article.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4798797','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4798797"><span>Games that Enlist Collective Intelligence to Solve Complex Scientific Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Burnett, Stephen; Furlong, Michelle; Melvin, Paul Guy; Singiser, Richard</p> <p>2016-01-01</p> <p>There is great value in employing the collective problem-solving power of large groups of people. Technological advances have allowed computer games to be utilized by a diverse population to solve problems. Science games are becoming more popular and cover various areas such as sequence alignments, DNA base-pairing, and protein and RNA folding. While these tools have been developed for the general population, they can also be used effectively in the classroom to teach students about various topics. Many games also employ a social component that entices students to continue playing and thereby to continue learning. The basic functions of game play and the potential of game play as a tool in the classroom are discussed in this article. PMID:27047610</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030034662','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030034662"><span>Decision-Theoretic Control of Planetary Rovers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Zilberstein, Shlomo; Washington, Richard; Bernstein, Daniel S.; Mouaddib, Abdel-Illah; Morris, Robert (Technical Monitor)</p> <p>2003-01-01</p> <p>Planetary rovers are small unmanned vehicles equipped with cameras and a variety of sensors used for scientific experiments. They must operate under tight constraints over such resources as operation time, power, storage capacity, and communication bandwidth. Moreover, the limited computational resources of the rover limit the complexity of on-line planning and scheduling. We describe two decision-theoretic approaches to maximize the productivity of planetary rovers: one based on adaptive planning and the other on hierarchical reinforcement learning. Both approaches map the problem into a Markov decision problem and attempt to solve a large part of the problem off-line, exploiting the structure of the plan and independence between plan components. We examine the advantages and limitations of these techniques and their scalability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21955100','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21955100"><span>An Interview with Matthew P. Greving, PhD. Interview by Vicki Glaser.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Greving, Matthew P</p> <p>2011-10-01</p> <p>Matthew P. Greving is Chief Scientific Officer at Nextval Inc., a company founded in early 2010 that has developed a discovery platform called MassInsight™.. He received his PhD in Biochemistry from Arizona State University, and prior to that he spent nearly 7 years working as a software engineer. This experience in solving complex computational problems fueled his interest in developing technologies and algorithms related to acquisition and analysis of high-dimensional biochemical data. To address the existing problems associated with label-based microarray readouts, he beganwork on a technique for label-free mass spectrometry (MS) microarray readout compatible with both matrix-assisted laser/desorption ionization (MALDI) and matrix-free nanostructure initiator mass spectrometry (NIMS). This is the core of Nextval’s MassInsight technology, which utilizes picoliter noncontact deposition of high-density arrays on mass-readout substrates along with computational algorithms for high-dimensional data processingand reduction.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/25320814','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/25320814"><span>Human connectome module pattern detection using a new multi-graph MinMax cut model.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>De, Wang; Wang, Yang; Nie, Feiping; Yan, Jingwen; Cai, Weidong; Saykin, Andrew J; Shen, Li; Huang, Heng</p> <p>2014-01-01</p> <p>Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding the large-scale brain networks that underlie higher-level cognition in human. However, suitable computational network analysis tools are still lacking in human connectome research. To address this problem, we propose a novel multi-graph min-max cut model to detect the consistent network modules from the brain connectivity networks of all studied subjects. A new multi-graph MinMax cut model is introduced to solve this challenging computational neuroscience problem and the efficient optimization algorithm is derived. In the identified connectome module patterns, each network module shows similar connectivity patterns in all subjects, which potentially associate to specific brain functions shared by all subjects. We validate our method by analyzing the weighted fiber connectivity networks. The promising empirical results demonstrate the effectiveness of our method.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JPhCS1015b2015R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JPhCS1015b2015R"><span>Parallelization of combinatorial search when solving knapsack optimization problem on computing systems based on multicore processors</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rahman, P. A.</p> <p>2018-05-01</p> <p>This scientific paper deals with the model of the knapsack optimization problem and method of its solving based on directed combinatorial search in the boolean space. The offered by the author specialized mathematical model of decomposition of the search-zone to the separate search-spheres and the algorithm of distribution of the search-spheres to the different cores of the multi-core processor are also discussed. The paper also provides an example of decomposition of the search-zone to the several search-spheres and distribution of the search-spheres to the different cores of the quad-core processor. Finally, an offered by the author formula for estimation of the theoretical maximum of the computational acceleration, which can be achieved due to the parallelization of the search-zone to the search-spheres on the unlimited number of the processor cores, is also given.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960003512','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960003512"><span>Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Ma, Kwan-Liu</p> <p>1995-01-01</p> <p>As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructured in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image composing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without using dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21768143','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21768143"><span>Leveraging e-Science infrastructure for electrochemical research.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Peachey, Tom; Mashkina, Elena; Lee, Chong-Yong; Enticott, Colin; Abramson, David; Bond, Alan M; Elton, Darrell; Gavaghan, David J; Stevenson, Gareth P; Kennedy, Gareth F</p> <p>2011-08-28</p> <p>As in many scientific disciplines, modern chemistry involves a mix of experimentation and computer-supported theory. Historically, these skills have been provided by different groups, and range from traditional 'wet' laboratory science to advanced numerical simulation. Increasingly, progress is made by global collaborations, in which new theory may be developed in one part of the world and applied and tested in the laboratory elsewhere. e-Science, or cyber-infrastructure, underpins such collaborations by providing a unified platform for accessing scientific instruments, computers and data archives, and collaboration tools. In this paper we discuss the application of advanced e-Science software tools to electrochemistry research performed in three different laboratories--two at Monash University in Australia and one at the University of Oxford in the UK. We show that software tools that were originally developed for a range of application domains can be applied to electrochemical problems, in particular Fourier voltammetry. Moreover, we show that, by replacing ad-hoc manual processes with e-Science tools, we obtain more accurate solutions automatically.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JCoPh.230.4811A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JCoPh.230.4811A"><span>Data mining techniques for scientific computing: Application to asymptotic paraxial approximations to model ultrarelativistic particles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Assous, Franck; Chaskalovic, Joël</p> <p>2011-06-01</p> <p>We propose a new approach that consists in using data mining techniques for scientific computing. Indeed, data mining has proved to be efficient in other contexts which deal with huge data like in biology, medicine, marketing, advertising and communications. Our aim, here, is to deal with the important problem of the exploitation of the results produced by any numerical method. Indeed, more and more data are created today by numerical simulations. Thus, it seems necessary to look at efficient tools to analyze them. In this work, we focus our presentation to a test case dedicated to an asymptotic paraxial approximation to model ultrarelativistic particles. Our method directly deals with numerical results of simulations and try to understand what each order of the asymptotic expansion brings to the simulation results over what could be obtained by other lower-order or less accurate means. This new heuristic approach offers new potential applications to treat numerical solutions to mathematical models.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29102608','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29102608"><span>hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fulcher, Ben D; Jones, Nick S</p> <p>2017-11-22</p> <p>Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_14");'>14</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li class="active"><span>16</span></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_16 --> <div id="page_17" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="321"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970011182','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970011182"><span>Beyond the Renderer: Software Architecture for Parallel Graphics and Visualization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Crockett, Thomas W.</p> <p>1996-01-01</p> <p>As numerous implementations have demonstrated, software-based parallel rendering is an effective way to obtain the needed computational power for a variety of challenging applications in computer graphics and scientific visualization. To fully realize their potential, however, parallel renderers need to be integrated into a complete environment for generating, manipulating, and delivering visual data. We examine the structure and components of such an environment, including the programming and user interfaces, rendering engines, and image delivery systems. We consider some of the constraints imposed by real-world applications and discuss the problems and issues involved in bringing parallel rendering out of the lab and into production.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20010084905&hterms=computer+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dcomputer%2Bscience','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20010084905&hterms=computer+science&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dcomputer%2Bscience"><span>Air, Ocean and Climate Monitoring Enhancing Undergraduate Training in the Physical, Environmental and Computer Sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Hope, W. W.; Johnson, L. P.; Obl, W.; Stewart, A.; Harris, W. C.; Craig, R. D.</p> <p>2000-01-01</p> <p>Faculty in the Department of Physical, Environmental and Computer Sciences strongly believe in the concept that undergraduate research and research-related activities must be integrated into the fabric of our undergraduate Science and Technology curricula. High level skills, such as problem solving, reasoning, collaboration and the ability to engage in research, are learned for advanced study in graduate school or for competing for well paying positions in the scientific community. One goal of our academic programs is to have a pipeline of research activities from high school to four year college, to graduate school, based on the GISS Institute on Climate and Planets model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001APS..SES.GD003S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001APS..SES.GD003S"><span>Computational Physics in a Nutshell</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schillaci, Michael</p> <p>2001-11-01</p> <p>Too often students of science are expected to ``pick-up'' what they need to know about the Art of Science. A description of the two-semester Computational Physics course being taught by the author offers a remedy to this situation. The course teaches students the three pillars of modern scientific research: Problem Solving, Programming, and Presentation. Using FORTRAN, LaTeXe, MAPLE V, HTML, and JAVA, students learn the fundamentals of algorithm development, how to implement classes and packages written by others, how to produce publication quality graphics and documents and how to publish them on the world-wide-web. The course content is outlined and project examples are offered.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1148696','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1148696"><span>Final Report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hollingsworth, Jeff</p> <p>2014-07-31</p> <p>The purpose of this project was to develop tools and techniques to improve the ability of computational scientists to investigate and correct problems (bugs) in their programs. Specifically, the University of Maryland component of this project focused on the problems associated with the finite number of bits available in a computer to represent numeric values. In large scale scientific computation, numbers are frequently added to and multiplied with each other billions of times. Thus even small errors due to the representation of numbers can accumulate into big errors. However, using too many bits to represent a number results in additionalmore » computation, memory, and energy costs. Thus it is critical to find the right size for numbers. This project focused on several aspects of this general problem. First, we developed a tool to look for cancelations, the catastrophic loss of precision in numbers due to the addition of two numbers whose actual values are close to each other, but whose representation in a computer is identical or nearly so. Second, we developed a suite of tools to allow programmers to identify exactly how much precision is required for each operation in their program. This tool allows programmers to both verify that enough precision is available, but more importantly find cases where extra precision could be eliminated to allow the program to use less memory, computer time, or energy. These tools use advanced binary modification techniques to allow the analysis of actual optimized code. The system, called Craft, has been applied to a number of benchmarks and real applications.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1405121','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1405121"><span>XVIS: Visualization for the Extreme-Scale Scientific-Computation Ecosystem Final Scientific/Technical Report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Geveci, Berk; Maynard, Robert</p> <p></p> <p>The XVis project brings together the key elements of research to enable scientific discovery at extreme scale. Scientific computing will no longer be purely about how fast computations can be performed. Energy constraints, processor changes, and I/O limitations necessitate significant changes in both the software applications used in scientific computation and the ways in which scientists use them. Components for modeling, simulation, analysis, and visualization must work together in a computational ecosystem, rather than working independently as they have in the past. The XVis project brought together collaborators from predominant DOE projects for visualization on accelerators and combining their respectivemore » features into a new visualization toolkit called VTK-m.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA233494','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA233494"><span>ONRASIA Scientific Information Bulletin, Volume 16, Number 1</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1991-03-01</p> <p>be expressed naturally in an and hence the programs produced by pline. They range from computing the algebraic language such as Fortran, these efforts...years devel- gram an iterative scheme to solve the function satisfies oping vectorizing compilers for Hitachi. problem. This is quite natural to do in...for it ential equations to be expressed in a on the plate, with 0,=1 at the outside to compile into efficient vectorizable natural mathematical syntax</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA140275','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA140275"><span>Ship Structure Committee Long-Range Research Plan - Guidelines for Program Development.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1982-01-01</p> <p>many scientists and engineers who contributed their time and expertise. We are indebted especially to Mr. J. J. Hopkinson, Dr. J. G. Giannotti, Mr...projection is better than assuming extension of the status quo. There is a long lead time in the use of new knowledge. Scientific research maturation...They even promise to remove traditional constraints previously too intractable to be labelled problems. The modern computer is an outstanding example</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA375977','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA375977"><span>USSR and Eastern Europe Scientific Abstracts, Cybernetics, Computers and Automation Technology, Number 35</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1978-09-12</p> <p>the population. Only a socialist, planned economy can cope with such problems. However, the in- creasing complexity of the tasks faced’ by...the development of systems allowing man-machine dialogue does not decrease, but rather increase the complexity of the systems involved, simply...shifting the complexity to another sphere, where it is invisible to the human utilizing the system. Figures 5; refer- ences 3: 2 Russian, 1 Western</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA164043','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA164043"><span>Control Circuitry for High Speed VLSI (Very Large Scale Integration) Winograd Fourier Transform Processors.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1985-12-01</p> <p>Office of Scientific Research , and Air Force Space Division are sponsoring research for the development of a high speed DFT processor. This DFT...to the arithmetic circuitry through a master/slave 11-15 %v OPR ONESHOT OUTPUT OUTPUT .., ~ INITIALIZATION COLUMN’ 00 N DONE CUTRPLANE PLAtNE Figure...Since the TSP is an NP-complete problem, many mathematicians, operations researchers , computer scientists and the like have proposed heuristic</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19521952','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19521952"><span>Life science research and drug discovery at the turn of the 21st century: the experience of SwissBioGrid.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>den Besten, Matthijs; Thomas, Arthur J; Schroeder, Ralph</p> <p>2009-04-22</p> <p>It is often said that the life sciences are transforming into an information science. As laboratory experiments are starting to yield ever increasing amounts of data and the capacity to deal with those data is catching up, an increasing share of scientific activity is seen to be taking place outside the laboratories, sifting through the data and modelling "in silico" the processes observed "in vitro." The transformation of the life sciences and similar developments in other disciplines have inspired a variety of initiatives around the world to create technical infrastructure to support the new scientific practices that are emerging. The e-Science programme in the United Kingdom and the NSF Office for Cyberinfrastructure are examples of these. In Switzerland there have been no such national initiatives. Yet, this has not prevented scientists from exploring the development of similar types of computing infrastructures. In 2004, a group of researchers in Switzerland established a project, SwissBioGrid, to explore whether Grid computing technologies could be successfully deployed within the life sciences. This paper presents their experiences as a case study of how the life sciences are currently operating as an information science and presents the lessons learned about how existing institutional and technical arrangements facilitate or impede this operation. SwissBioGrid gave rise to two pilot projects: one for proteomics data analysis and the other for high-throughput molecular docking ("virtual screening") to find new drugs for neglected diseases (specifically, for dengue fever). The proteomics project was an example of a data management problem, applying many different analysis algorithms to Terabyte-sized datasets from mass spectrometry, involving comparisons with many different reference databases; the virtual screening project was more a purely computational problem, modelling the interactions of millions of small molecules with a limited number of protein targets on the coat of the dengue virus. Both present interesting lessons about how scientific practices are changing when they tackle the problems of large-scale data analysis and data management by means of creating a novel technical infrastructure. In the experience of SwissBioGrid, data intensive discovery has a lot to gain from close collaboration with industry and harnessing distributed computing power. Yet the diversity in life science research implies only a limited role for generic infrastructure; and the transience of support means that researchers need to integrate their efforts with others if they want to sustain the benefits of their success, which are otherwise lost.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMDI22A..04W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMDI22A..04W"><span>TerraFERMA: Harnessing Advanced Computational Libraries in Earth Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wilson, C. R.; Spiegelman, M.; van Keken, P.</p> <p>2012-12-01</p> <p>Many important problems in Earth sciences can be described by non-linear coupled systems of partial differential equations. These "multi-physics" problems include thermo-chemical convection in Earth and planetary interiors, interactions of fluids and magmas with the Earth's mantle and crust and coupled flow of water and ice. These problems are of interest to a large community of researchers but are complicated to model and understand. Much of this complexity stems from the nature of multi-physics where small changes in the coupling between variables or constitutive relations can lead to radical changes in behavior, which in turn affect critical computational choices such as discretizations, solvers and preconditioners. To make progress in understanding such coupled systems requires a computational framework where multi-physics problems can be described at a high-level while maintaining the flexibility to easily modify the solution algorithm. Fortunately, recent advances in computational science provide a basis for implementing such a framework. Here we present the Transparent Finite Element Rapid Model Assembler (TerraFERMA), which leverages several advanced open-source libraries for core functionality. FEniCS (fenicsproject.org) provides a high level language for describing the weak forms of coupled systems of equations, and an automatic code generator that produces finite element assembly code. PETSc (www.mcs.anl.gov/petsc) provides a wide range of scalable linear and non-linear solvers that can be composed into effective multi-physics preconditioners. SPuD (amcg.ese.ic.ac.uk/Spud) is an application neutral options system that provides both human and machine-readable interfaces based on a single xml schema. Our software integrates these libraries and provides the user with a framework for exploring multi-physics problems. A single options file fully describes the problem, including all equations, coefficients and solver options. Custom compiled applications are generated from this file but share an infrastructure for services common to all models, e.g. diagnostics, checkpointing and global non-linear convergence monitoring. This maximizes code reusability, reliability and longevity ensuring that scientific results and the methods used to acquire them are transparent and reproducible. TerraFERMA has been tested against many published geodynamic benchmarks including 2D/3D thermal convection problems, the subduction zone benchmarks and benchmarks for magmatic solitary waves. It is currently being used in the investigation of reactive cracking phenomena with applications to carbon sequestration, but we will principally discuss its use in modeling the migration of fluids in subduction zones. Subduction zones require an understanding of the highly nonlinear interactions of fluids with solids and thus provide an excellent scientific driver for the development of multi-physics software.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016JChPh.145f4307J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016JChPh.145f4307J"><span>An O(N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; Qin, Jian; Karpeev, Dmitry; Hernandez-Ortiz, Juan; de Pablo, Juan J.; Heinonen, Olle</p> <p>2016-08-01</p> <p>Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O(N2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Method (FMM) to evaluate the integrals in O(N) operations, with O(N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. The results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1339573-parallel-approach-integral-problems-kernel-independent-fast-multipole-method-application-polarization-magnetization-interacting-particles','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1339573-parallel-approach-integral-problems-kernel-independent-fast-multipole-method-application-polarization-magnetization-interacting-particles"><span>An O( N) and parallel approach to integral problems by a kernel-independent fast multipole method: Application to polarization and magnetization of interacting particles</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Jiang, Xikai; Li, Jiyuan; Zhao, Xujun; ...</p> <p>2016-08-10</p> <p>Large classes of materials systems in physics and engineering are governed by magnetic and electrostatic interactions. Continuum or mesoscale descriptions of such systems can be cast in terms of integral equations, whose direct computational evaluation requires O( N 2) operations, where N is the number of unknowns. Such a scaling, which arises from the many-body nature of the relevant Green's function, has precluded wide-spread adoption of integral methods for solution of large-scale scientific and engineering problems. In this work, a parallel computational approach is presented that relies on using scalable open source libraries and utilizes a kernel-independent Fast Multipole Methodmore » (FMM) to evaluate the integrals in O( N) operations, with O( N) memory cost, thereby substantially improving the scalability and efficiency of computational integral methods. We demonstrate the accuracy, efficiency, and scalability of our approach in the context of two examples. In the first, we solve a boundary value problem for a ferroelectric/ferromagnetic volume in free space. In the second, we solve an electrostatic problem involving polarizable dielectric bodies in an unbounded dielectric medium. Lastly, the results from these test cases show that our proposed parallel approach, which is built on a kernel-independent FMM, can enable highly efficient and accurate simulations and allow for considerable flexibility in a broad range of applications.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1198061-science-dmz-network-design-pattern-data-intensive-science','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1198061-science-dmz-network-design-pattern-data-intensive-science"><span>The Science DMZ: A Network Design Pattern for Data-Intensive Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Dart, Eli; Rotman, Lauren; Tierney, Brian; ...</p> <p>2014-01-01</p> <p>The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers andmore » research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015AGUFM.H41B1299L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015AGUFM.H41B1299L"><span>A Computationally Efficient Parallel Levenberg-Marquardt Algorithm for Large-Scale Big-Data Inversion</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lin, Y.; O'Malley, D.; Vesselinov, V. V.</p> <p>2015-12-01</p> <p>Inverse modeling seeks model parameters given a set of observed state variables. However, for many practical problems due to the facts that the observed data sets are often large and model parameters are often numerous, conventional methods for solving the inverse modeling can be computationally expensive. We have developed a new, computationally-efficient Levenberg-Marquardt method for solving large-scale inverse modeling. Levenberg-Marquardt methods require the solution of a dense linear system of equations which can be prohibitively expensive to compute for large-scale inverse problems. Our novel method projects the original large-scale linear problem down to a Krylov subspace, such that the dimensionality of the measurements can be significantly reduced. Furthermore, instead of solving the linear system for every Levenberg-Marquardt damping parameter, we store the Krylov subspace computed when solving the first damping parameter and recycle it for all the following damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved by using these computational techniques. We apply this new inverse modeling method to invert for a random transitivity field. Our algorithm is fast enough to solve for the distributed model parameters (transitivity) at each computational node in the model domain. The inversion is also aided by the use regularization techniques. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). Julia is an advanced high-level scientific programing language that allows for efficient memory management and utilization of high-performance computational resources. By comparing with a Levenberg-Marquardt method using standard linear inversion techniques, our Levenberg-Marquardt method yields speed-up ratio of 15 in a multi-core computational environment and a speed-up ratio of 45 in a single-core computational environment. Therefore, our new inverse modeling method is a powerful tool for large-scale applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29652587','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29652587"><span>SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Zenke, Friedemann; Ganguli, Surya</p> <p>2018-06-01</p> <p>A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016MS%26E..161a2040P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016MS%26E..161a2040P"><span>The application of artificial intelligence in the optimal design of mechanical systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Poteralski, A.; Szczepanik, M.</p> <p>2016-11-01</p> <p>The paper is devoted to new computational techniques in mechanical optimization where one tries to study, model, analyze and optimize very complex phenomena, for which more precise scientific tools of the past were incapable of giving low cost and complete solution. Soft computing methods differ from conventional (hard) computing in that, unlike hard computing, they are tolerant of imprecision, uncertainty, partial truth and approximation. The paper deals with an application of the bio-inspired methods, like the evolutionary algorithms (EA), the artificial immune systems (AIS) and the particle swarm optimizers (PSO) to optimization problems. Structures considered in this work are analyzed by the finite element method (FEM), the boundary element method (BEM) and by the method of fundamental solutions (MFS). The bio-inspired methods are applied to optimize shape, topology and material properties of 2D, 3D and coupled 2D/3D structures, to optimize the termomechanical structures, to optimize parameters of composites structures modeled by the FEM, to optimize the elastic vibrating systems to identify the material constants for piezoelectric materials modeled by the BEM and to identify parameters in acoustics problem modeled by the MFS.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/2290373','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/2290373"><span>[Mathematical model of technical equipment of a clinical-diagnostic laboratory].</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bukin, S I; Busygin, D V; Tilevich, M E</p> <p>1990-01-01</p> <p>The paper is concerned with the problems of technical equipment of standard clinico-diagnostic laboratories (CDL) in this country. The authors suggest a mathematic model that may minimize expenditures for laboratory studies. The model enables the following problems to be solved: to issue scientifically-based recommendations for technical equipment of CDL; to validate the medico-technical requirements for newly devised items; to select the optimum types of uniform items; to define optimal technical decisions at the stage of the design; to determine the lab assistant's labour productivity and the cost of some investigations; to compute the medical laboratory engineering requirement for treatment and prophylactic institutions of this country.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970013703','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970013703"><span>Research Projects, Technical Reports and Publications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Oliger, Joseph</p> <p>1996-01-01</p> <p>The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Advanced Methods for Scientific Computing High Performance Networks During this report pefiod Professor Antony Jameson of Princeton University, Professor Wei-Pai Tang of the University of Waterloo, Professor Marsha Berger of New York University, Professor Tony Chan of UCLA, Associate Professor David Zingg of University of Toronto, Canada and Assistant Professor Andrew Sohn of New Jersey Institute of Technology have been visiting RIACS. January 1, 1996 through September 30, 1996 RIACS had three staff scientists, four visiting scientists, one post-doctoral scientist, three consultants, two research associates and one research assistant. RIACS held a joint workshop with Code 1 29-30 July 1996. The workshop was held to discuss needs and opportunities in basic research in computer science in and for NASA applications. There were 14 talks given by NASA, industry and university scientists and three open discussion sessions. There were approximately fifty participants. A proceedings is being prepared. It is planned to have similar workshops on an annual basis. RIACS technical reports are usually preprints of manuscripts that have been submitted to research 'ournals or conference proceedings. A list of these reports for the period January i 1, 1996 through September 30, 1996 is in the Reports and Abstracts section of this report.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2013-07-09/pdf/2013-16414.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2013-07-09/pdf/2013-16414.pdf"><span>78 FR 41046 - Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2013-07-09</p> <p>... Services Administration, notice is hereby given that the Advanced Scientific Computing Advisory Committee will be renewed for a two-year period beginning on July 1, 2013. The Committee will provide advice to the Director, Office of Science (DOE), on the Advanced Scientific Computing Research Program managed...</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_15");'>15</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li class="active"><span>17</span></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_17 --> <div id="page_18" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="341"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010hocc.book..453C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010hocc.book..453C"><span>Open Science in the Cloud: Towards a Universal Platform for Scientific and Statistical Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chine, Karim</p> <p></p> <p>The UK, through the e-Science program, the US through the NSF-funded cyber infrastructure and the European Union through the ICT Calls aimed to provide "the technological solution to the problem of efficiently connecting data, computers, and people with the goal of enabling derivation of novel scientific theories and knowledge".1 The Grid (Foster, 2002; Foster; Kesselman, Nick, & Tuecke, 2002), foreseen as a major accelerator of discovery, didn't meet the expectations it had excited at its beginnings and was not adopted by the broad population of research professionals. The Grid is a good tool for particle physicists and it has allowed them to tackle the tremendous computational challenges inherent to their field. However, as a technology and paradigm for delivering computing on demand, it doesn't work and it can't be fixed. On one hand, "the abstractions that Grids expose - to the end-user, to the deployers and to application developers - are inappropriate and they need to be higher level" (Jha, Merzky, & Fox), and on the other hand, academic Grids are inherently economically unsustainable. They can't compete with a service outsourced to the Industry whose quality and price would be driven by market forces. The virtualization technologies and their corollary, the Infrastructure-as-a-Service (IaaS) style cloud, hold the promise to enable what the Grid failed to deliver: a sustainable environment for computational sciences that would lower the barriers for accessing federated computational resources, software tools and data; enable collaboration and resources sharing and provide the building blocks of a ubiquitous platform for traceable and reproducible computational research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://pubs.er.usgs.gov/publication/70194451','USGSPUBS'); return false;" href="https://pubs.er.usgs.gov/publication/70194451"><span>Distributed geospatial model sharing based on open interoperability standards</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://pubs.er.usgs.gov/pubs/index.jsp?view=adv">USGS Publications Warehouse</a></p> <p>Feng, Min; Liu, Shuguang; Euliss, Ned H.; Fang, Yin</p> <p>2009-01-01</p> <p>Numerous geospatial computational models have been developed based on sound principles and published in journals or presented in conferences. However modelers have made few advances in the development of computable modules that facilitate sharing during model development or utilization. Constraints hampering development of model sharing technology includes limitations on computing, storage, and connectivity; traditional stand-alone and closed network systems cannot fully support sharing and integrating geospatial models. To address this need, we have identified methods for sharing geospatial computational models using Service Oriented Architecture (SOA) techniques and open geospatial standards. The service-oriented model sharing service is accessible using any tools or systems compliant with open geospatial standards, making it possible to utilize vast scientific resources available from around the world to solve highly sophisticated application problems. The methods also allow model services to be empowered by diverse computational devices and technologies, such as portable devices and GRID computing infrastructures. Based on the generic and abstract operations and data structures required for Web Processing Service (WPS) standards, we developed an interactive interface for model sharing to help reduce interoperability problems for model use. Geospatial computational models are shared on model services, where the computational processes provided by models can be accessed through tools and systems compliant with WPS. We developed a platform to help modelers publish individual models in a simplified and efficient way. Finally, we illustrate our technique using wetland hydrological models we developed for the prairie pothole region of North America.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018Sci...360..195N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018Sci...360..195N"><span>A blueprint for demonstrating quantum supremacy with superconducting qubits</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Neill, C.; Roushan, P.; Kechedzhi, K.; Boixo, S.; Isakov, S. V.; Smelyanskiy, V.; Megrant, A.; Chiaro, B.; Dunsworth, A.; Arya, K.; Barends, R.; Burkett, B.; Chen, Y.; Chen, Z.; Fowler, A.; Foxen, B.; Giustina, M.; Graff, R.; Jeffrey, E.; Huang, T.; Kelly, J.; Klimov, P.; Lucero, E.; Mutus, J.; Neeley, M.; Quintana, C.; Sank, D.; Vainsencher, A.; Wenner, J.; White, T. C.; Neven, H.; Martinis, J. M.</p> <p>2018-04-01</p> <p>A key step toward demonstrating a quantum system that can address difficult problems in physics and chemistry will be performing a computation beyond the capabilities of any classical computer, thus achieving so-called quantum supremacy. In this study, we used nine superconducting qubits to demonstrate a promising path toward quantum supremacy. By individually tuning the qubit parameters, we were able to generate thousands of distinct Hamiltonian evolutions and probe the output probabilities. The measured probabilities obey a universal distribution, consistent with uniformly sampling the full Hilbert space. As the number of qubits increases, the system continues to explore the exponentially growing number of states. Extending these results to a system of 50 qubits has the potential to address scientific questions that are beyond the capabilities of any classical computer.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27547674','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27547674"><span>Developing science gateways for drug discovery in a grid environment.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Pérez-Sánchez, Horacio; Rezaei, Vahid; Mezhuyev, Vitaliy; Man, Duhu; Peña-García, Jorge; den-Haan, Helena; Gesing, Sandra</p> <p>2016-01-01</p> <p>Methods for in silico screening of large databases of molecules increasingly complement and replace experimental techniques to discover novel compounds to combat diseases. As these techniques become more complex and computationally costly we are faced with an increasing problem to provide the research community of life sciences with a convenient tool for high-throughput virtual screening on distributed computing resources. To this end, we recently integrated the biophysics-based drug-screening program FlexScreen into a service, applicable for large-scale parallel screening and reusable in the context of scientific workflows. Our implementation is based on Pipeline Pilot and Simple Object Access Protocol and provides an easy-to-use graphical user interface to construct complex workflows, which can be executed on distributed computing resources, thus accelerating the throughput by several orders of magnitude.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ccr.cancer.gov/careers/postdoctoral-fellow/19680','NCI'); return false;" href="https://ccr.cancer.gov/careers/postdoctoral-fellow/19680"><span>Postdoctoral Fellow | Center for Cancer Research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.cancer.gov">Cancer.gov</a></p> <p></p> <p></p> <p>The Neuro-Oncology Branch (NOB), Center for Cancer Research (CCR), National Cancer Institute (NCI) of the National Institutes of Health (NIH) is seeking outstanding postdoctoral candidates interested in studying metabolic and cell signaling pathways in the context of brain cancers through construction of computational models amenable to formal computational analysis and simulation. The ability to closely collaborate with the modern metabolomics center developed at CCR provides a unique opportunity for a postdoctoral candidate with a strong theoretical background and interest in demonstrating the incredible potential of computational approaches to solve problems from scientific disciplines and improve lives. The candidate will be given the opportunity to both construct data-driven models, as well as biologically validate the models by demonstrating the ability to predict the effects of altering tumor metabolism in laboratory and clinical settings.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016AGUFMED43A0846M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016AGUFMED43A0846M"><span>Measuring scientific reasoning through behavioral analysis in a computer-based problem solving exercise</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mead, C.; Horodyskyj, L.; Buxner, S.; Semken, S. C.; Anbar, A. D.</p> <p>2016-12-01</p> <p>Developing scientific reasoning skills is a common learning objective for general-education science courses. However, effective assessments for such skills typically involve open-ended questions or tasks, which must be hand-scored and may not be usable online. Using computer-based learning environments, reasoning can be assessed automatically by analyzing student actions within the learning environment. We describe such an assessment under development and present pilot results. In our content-neutral instrument, students solve a problem by collecting and interpreting data in a logical, systematic manner. We then infer reasoning skill automatically based on student actions. Specifically, students investigate why Earth has seasons, a scientifically simple but commonly misunderstood topic. Students are given three possible explanations and asked to select a set of locations on a world map from which to collect temperature data. They then explain how the data support or refute each explanation. The best approaches will use locations in both the Northern and Southern hemispheres to argue that the contrasting seasonality of the hemispheres supports only the correct explanation. We administered a pilot version to students at the beginning of an online, introductory science course (n = 223) as an optional extra credit exercise. We were able to categorize students' data collection decisions as more and less logically sound. Students who choose the most logical measurement locations earned higher course grades, but not significantly higher. This result is encouraging, but not definitive. In the future, we will clarify our results in two ways. First, we plan to incorporate more open-ended interactions into the assessment to improve the resolving power of this tool. Second, to avoid relying on course grades, we will independently measure reasoning skill with one of the existing hand-scored assessments (e.g., Critical Thinking Assessment Test) to cross-validate our new assessment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010067806','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010067806"><span>Wavelet-Based Interpolation and Representation of Non-Uniformly Sampled Spacecraft Mission Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Bose, Tamal</p> <p>2000-01-01</p> <p>A well-documented problem in the analysis of data collected by spacecraft instruments is the need for an accurate, efficient representation of the data set. The data may suffer from several problems, including additive noise, data dropouts, an irregularly-spaced sampling grid, and time-delayed sampling. These data irregularities render most traditional signal processing techniques unusable, and thus the data must be interpolated onto an even grid before scientific analysis techniques can be applied. In addition, the extremely large volume of data collected by scientific instrumentation presents many challenging problems in the area of compression, visualization, and analysis. Therefore, a representation of the data is needed which provides a structure which is conducive to these applications. Wavelet representations of data have already been shown to possess excellent characteristics for compression, data analysis, and imaging. The main goal of this project is to develop a new adaptive filtering algorithm for image restoration and compression. The algorithm should have low computational complexity and a fast convergence rate. This will make the algorithm suitable for real-time applications. The algorithm should be able to remove additive noise and reconstruct lost data samples from images.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950016787','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950016787"><span>Microgravity sciences application visiting scientist program</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Glicksman, Martin; Vanalstine, James</p> <p>1995-01-01</p> <p>Marshall Space Flight Center pursues scientific research in the area of low-gravity effects on materials and processes. To facilitate these Government performed research responsibilities, a number of supplementary research tasks were accomplished by a group of specialized visiting scientists. They participated in work on contemporary research problems with specific objectives related to current or future space flight experiments and defined and established independent programs of research which were based on scientific peer review and the relevance of the defined research to NASA microgravity for implementing a portion of the national program. The programs included research in the following areas: protein crystal growth, X-ray crystallography and computer analysis of protein crystal structure, optimization and analysis of protein crystal growth techniques, and design and testing of flight hardware.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008JPhCS.125a2072W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008JPhCS.125a2072W"><span>Data management and analysis for the Earth System Grid</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Williams, D. N.; Ananthakrishnan, R.; Bernholdt, D. E.; Bharathi, S.; Brown, D.; Chen, M.; Chervenak, A. L.; Cinquini, L.; Drach, R.; Foster, I. T.; Fox, P.; Hankin, S.; Henson, V. E.; Jones, P.; Middleton, D. E.; Schwidder, J.; Schweitzer, R.; Schuler, R.; Shoshani, A.; Siebenlist, F.; Sim, A.; Strand, W. G.; Wilhelmi, N.; Su, M.</p> <p>2008-07-01</p> <p>The international climate community is expected to generate hundreds of petabytes of simulation data within the next five to seven years. This data must be accessed and analyzed by thousands of analysts worldwide in order to provide accurate and timely estimates of the likely impact of climate change on physical, biological, and human systems. Climate change is thus not only a scientific challenge of the first order but also a major technological challenge. In order to address this technological challenge, the Earth System Grid Center for Enabling Technologies (ESG-CET) has been established within the U.S. Department of Energy's Scientific Discovery through Advanced Computing (SciDAC)-2 program, with support from the offices of Advanced Scientific Computing Research and Biological and Environmental Research. ESG-CET's mission is to provide climate researchers worldwide with access to the data, information, models, analysis tools, and computational capabilities required to make sense of enormous climate simulation datasets. Its specific goals are to (1) make data more useful to climate researchers by developing Grid technology that enhances data usability; (2) meet specific distributed database, data access, and data movement needs of national and international climate projects; (3) provide a universal and secure web-based data access portal for broad multi-model data collections; and (4) provide a wide-range of Grid-enabled climate data analysis tools and diagnostic methods to international climate centers and U.S. government agencies. Building on the successes of the previous Earth System Grid (ESG) project, which has enabled thousands of researchers to access tens of terabytes of data from a small number of ESG sites, ESG-CET is working to integrate a far larger number of distributed data providers, high-bandwidth wide-area networks, and remote computers in a highly collaborative problem-solving environment.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/ED324219.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/ED324219.pdf"><span>Integrating Data Base into the Elementary School Science Program.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Schlenker, Richard M.</p> <p></p> <p>This document describes seven science activities that combine scientific principles and computers. The objectives for the activities are to show students how the computer can be used as a tool to store and arrange scientific data, provide students with experience using the computer as a tool to manage scientific data, and provide students with…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012CoPhC.183.1911J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012CoPhC.183.1911J"><span>A high performance scientific cloud computing environment for materials simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Jorissen, K.; Vila, F. D.; Rehr, J. J.</p> <p>2012-09-01</p> <p>We describe the development of a scientific cloud computing (SCC) platform that offers high performance computation capability. The platform consists of a scientific virtual machine prototype containing a UNIX operating system and several materials science codes, together with essential interface tools (an SCC toolset) that offers functionality comparable to local compute clusters. In particular, our SCC toolset provides automatic creation of virtual clusters for parallel computing, including tools for execution and monitoring performance, as well as efficient I/O utilities that enable seamless connections to and from the cloud. Our SCC platform is optimized for the Amazon Elastic Compute Cloud (EC2). We present benchmarks for prototypical scientific applications and demonstrate performance comparable to local compute clusters. To facilitate code execution and provide user-friendly access, we have also integrated cloud computing capability in a JAVA-based GUI. Our SCC platform may be an alternative to traditional HPC resources for materials science or quantum chemistry applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/23529114','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/23529114"><span>A comparative analysis of soft computing techniques for gene prediction.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Goel, Neelam; Singh, Shailendra; Aseri, Trilok Chand</p> <p>2013-07-01</p> <p>The rapid growth of genomic sequence data for both human and nonhuman species has made analyzing these sequences, especially predicting genes in them, very important and is currently the focus of many research efforts. Beside its scientific interest in the molecular biology and genomics community, gene prediction is of considerable importance in human health and medicine. A variety of gene prediction techniques have been developed for eukaryotes over the past few years. This article reviews and analyzes the application of certain soft computing techniques in gene prediction. First, the problem of gene prediction and its challenges are described. These are followed by different soft computing techniques along with their application to gene prediction. In addition, a comparative analysis of different soft computing techniques for gene prediction is given. Finally some limitations of the current research activities and future research directions are provided. Copyright © 2013 Elsevier Inc. All rights reserved.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010LNCS.6139..163R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010LNCS.6139..163R"><span>The Quantitative Analysis of User Behavior Online - Data, Models and Algorithms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Raghavan, Prabhakar</p> <p></p> <p>By blending principles from mechanism design, algorithms, machine learning and massive distributed computing, the search industry has become good at optimizing monetization on sound scientific principles. This represents a successful and growing partnership between computer science and microeconomics. When it comes to understanding how online users respond to the content and experiences presented to them, we have more of a lacuna in the collaboration between computer science and certain social sciences. We will use a concrete technical example from image search results presentation, developing in the process some algorithmic and machine learning problems of interest in their own right. We then use this example to motivate the kinds of studies that need to grow between computer science and the social sciences; a critical element of this is the need to blend large-scale data analysis with smaller-scale eye-tracking and "individualized" lab studies.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19850039625&hterms=nuclear+physics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dnuclear%2Bphysics','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19850039625&hterms=nuclear+physics&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D20%26Ntt%3Dnuclear%2Bphysics"><span>Nuclear Science Symposium, 31st and Symposium on Nuclear Power Systems, 16th, Orlando, FL, October 31-November 2, 1984, Proceedings</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Biggerstaff, J. A. (Editor)</p> <p>1985-01-01</p> <p>Topics related to physics instrumentation are discussed, taking into account cryostat and electronic development associated with multidetector spectrometer systems, the influence of materials and counting-rate effects on He-3 neutron spectrometry, a data acquisition system for time-resolved muscle experiments, and a sensitive null detector for precise measurements of integral linearity. Other subjects explored are concerned with space instrumentation, computer applications, detectors, instrumentation for high energy physics, instrumentation for nuclear medicine, environmental monitoring and health physics instrumentation, nuclear safeguards and reactor instrumentation, and a 1984 symposium on nuclear power systems. Attention is given to the application of multiprocessors to scientific problems, a large-scale computer facility for computational aerodynamics, a single-board 32-bit computer for the Fastbus, the integration of detector arrays and readout electronics on a single chip, and three-dimensional Monte Carlo simulation of the electron avalanche in a proportional counter.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010isa..conf..209A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010isa..conf..209A"><span>Security Risks of Cloud Computing and Its Emergence as 5th Utility Service</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ahmad, Mushtaq</p> <p></p> <p>Cloud Computing is being projected by the major cloud services provider IT companies such as IBM, Google, Yahoo, Amazon and others as fifth utility where clients will have access for processing those applications and or software projects which need very high processing speed for compute intensive and huge data capacity for scientific, engineering research problems and also e- business and data content network applications. These services for different types of clients are provided under DASM-Direct Access Service Management based on virtualization of hardware, software and very high bandwidth Internet (Web 2.0) communication. The paper reviews these developments for Cloud Computing and Hardware/Software configuration of the cloud paradigm. The paper also examines the vital aspects of security risks projected by IT Industry experts, cloud clients. The paper also highlights the cloud provider's response to cloud security risks.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19910048414&hterms=4th&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D4th','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19910048414&hterms=4th&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3D4th"><span>SIAM Conference on Parallel Processing for Scientific Computing, 4th, Chicago, IL, Dec. 11-13, 1989, Proceedings</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Dongarra, Jack (Editor); Messina, Paul (Editor); Sorensen, Danny C. (Editor); Voigt, Robert G. (Editor)</p> <p>1990-01-01</p> <p>Attention is given to such topics as an evaluation of block algorithm variants in LAPACK and presents a large-grain parallel sparse system solver, a multiprocessor method for the solution of the generalized Eigenvalue problem on an interval, and a parallel QR algorithm for iterative subspace methods on the CM2. A discussion of numerical methods includes the topics of asynchronous numerical solutions of PDEs on parallel computers, parallel homotopy curve tracking on a hypercube, and solving Navier-Stokes equations on the Cedar Multi-Cluster system. A section on differential equations includes a discussion of a six-color procedure for the parallel solution of elliptic systems using the finite quadtree structure, data parallel algorithms for the finite element method, and domain decomposition methods in aerodynamics. Topics dealing with massively parallel computing include hypercube vs. 2-dimensional meshes and massively parallel computation of conservation laws. Performance and tools are also discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20060013449','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20060013449"><span>Implementing Scientific Simulation Codes Highly Tailored for Vector Architectures Using Custom Configurable Computing Machines</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Rutishauser, David</p> <p>2006-01-01</p> <p>The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters that attempts to minimize execution time, while staying within resource constraints. The flexibility of using a custom reconfigurable implementation is exploited in a unique manner to leverage the lessons learned in vector supercomputer development. The vector processing framework is tailored to the application, with variable parameters that are fixed in traditional vector processing. Benchmark data that demonstrates the functionality and utility of the approach is presented. The benchmark data includes an identified bottleneck in a real case study example vector code, the NASA Langley Terminal Area Simulation System (TASS) application.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1379895','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1379895"><span>Snowflake: A Lightweight Portable Stencil DSL</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Zhang, Nathan; Driscoll, Michael; Markley, Charles</p> <p></p> <p>Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a 'micro-compiler' approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higher-order operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded inmore » the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2x of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1379895-snowflake-lightweight-portable-stencil-dsl','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1379895-snowflake-lightweight-portable-stencil-dsl"><span>Snowflake: A Lightweight Portable Stencil DSL</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Zhang, Nathan; Driscoll, Michael; Markley, Charles; ...</p> <p>2017-05-01</p> <p>Stencil computations are not well optimized by general-purpose production compilers and the increased use of multicore, manycore, and accelerator-based systems makes the optimization problem even more challenging. In this paper we present Snowflake, a Domain Specific Language (DSL) for stencils that uses a 'micro-compiler' approach, i.e., small, focused, domain-specific code generators. The approach is similar to that used in image processing stencils, but Snowflake handles the much more complex stencils that arise in scientific computing, including complex boundary conditions, higher-order operators (larger stencils), higher dimensions, variable coefficients, non-unit-stride iteration spaces, and multiple input or output meshes. Snowflake is embedded inmore » the Python language, allowing it to interoperate with popular scientific tools like SciPy and iPython; it also takes advantage of built-in Python libraries for powerful dependence analysis as part of a just-in-time compiler. We demonstrate the power of the Snowflake language and the micro-compiler approach with a complex scientific benchmark, HPGMG, that exercises the generality of stencil support in Snowflake. By generating OpenMP comparable to, and OpenCL within a factor of 2x of hand-optimized HPGMG, Snowflake demonstrates that a micro-compiler can support diverse processor architectures and is performance-competitive whilst preserving a high-level Python implementation.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=19940004563&hterms=methodological&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dmethodological','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=19940004563&hterms=methodological&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D60%26Ntt%3Dmethodological"><span>Artificial intelligence and design: Opportunities, research problems and directions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Amarel, Saul</p> <p>1990-01-01</p> <p>The issues of industrial productivity and economic competitiveness are of major significance in the U.S. at present. By advancing the science of design, and by creating a broad computer-based methodology for automating the design of artifacts and of industrial processes, we can attain dramatic improvements in productivity. It is our thesis that developments in computer science, especially in Artificial Intelligence (AI) and in related areas of advanced computing, provide us with a unique opportunity to push beyond the present level of computer aided automation technology and to attain substantial advances in the understanding and mechanization of design processes. To attain these goals, we need to build on top of the present state of AI, and to accelerate research and development in areas that are especially relevant to design problems of realistic complexity. We propose an approach to the special challenges in this area, which combines 'core work' in AI with the development of systems for handling significant design tasks. We discuss the general nature of design problems, the scientific issues involved in studying them with the help of AI approaches, and the methodological/technical issues that one must face in developing AI systems for handling advanced design tasks. Looking at basic work in AI from the perspective of design automation, we identify a number of research problems that need special attention. These include finding solution methods for handling multiple interacting goals, formation problems, problem decompositions, and redesign problems; choosing representations for design problems with emphasis on the concept of a design record; and developing approaches for the acquisition and structuring of domain knowledge with emphasis on finding useful approximations to domain theories. Progress in handling these research problems will have major impact both on our understanding of design processes and their automation, and also on several fundamental questions that are of intrinsic concern to AI. We present examples of current AI work on specific design tasks, and discuss new directions of research, both as extensions of current work and in the context of new design tasks where domain knowledge is either intractable or incomplete. The domains discussed include Digital Circuit Design, Mechanical Design of Rotational Transmissions, Design of Computer Architectures, Marine Design, Aircraft Design, and Design of Chemical Processes and Materials. Work in these domains is significant on technical grounds, and it is also important for economic and policy reasons.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_16");'>16</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li class="active"><span>18</span></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_18 --> <div id="page_19" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="361"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=climate+AND+change+AND+evidence&pg=3&id=EJ1054744','ERIC'); return false;" href="https://eric.ed.gov/?q=climate+AND+change+AND+evidence&pg=3&id=EJ1054744"><span>Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Pallant, Amy; Lee, Hee-Sun</p> <p>2015-01-01</p> <p>Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students (N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19950018143','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19950018143"><span>Parallel processing for scientific computations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Alkhatib, Hasan S.</p> <p>1995-01-01</p> <p>The scope of this project dealt with the investigation of the requirements to support distributed computing of scientific computations over a cluster of cooperative workstations. Various experiments on computations for the solution of simultaneous linear equations were performed in the early phase of the project to gain experience in the general nature and requirements of scientific applications. A specification of a distributed integrated computing environment, DICE, based on a distributed shared memory communication paradigm has been developed and evaluated. The distributed shared memory model facilitates porting existing parallel algorithms that have been designed for shared memory multiprocessor systems to the new environment. The potential of this new environment is to provide supercomputing capability through the utilization of the aggregate power of workstations cooperating in a cluster interconnected via a local area network. Workstations, generally, do not have the computing power to tackle complex scientific applications, making them primarily useful for visualization, data reduction, and filtering as far as complex scientific applications are concerned. There is a tremendous amount of computing power that is left unused in a network of workstations. Very often a workstation is simply sitting idle on a desk. A set of tools can be developed to take advantage of this potential computing power to create a platform suitable for large scientific computations. The integration of several workstations into a logical cluster of distributed, cooperative, computing stations presents an alternative to shared memory multiprocessor systems. In this project we designed and evaluated such a system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19970010814','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19970010814"><span>Load Balancing Unstructured Adaptive Grids for CFD Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Biswas, Rupak; Oliker, Leonid</p> <p>1996-01-01</p> <p>Mesh adaption is a powerful tool for efficient unstructured-grid computations but causes load imbalance among processors on a parallel machine. A dynamic load balancing method is presented that balances the workload across all processors with a global view. After each parallel tetrahedral mesh adaption, the method first determines if the new mesh is sufficiently unbalanced to warrant a repartitioning. If so, the adapted mesh is repartitioned, with new partitions assigned to processors so that the redistribution cost is minimized. The new partitions are accepted only if the remapping cost is compensated by the improved load balance. Results indicate that this strategy is effective for large-scale scientific computations on distributed-memory multiprocessors.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013PhDT.......317M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013PhDT.......317M"><span>A Modular Environment for Geophysical Inversion and Run-time Autotuning using Heterogeneous Computing Systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Myre, Joseph M.</p> <p></p> <p>Heterogeneous computing systems have recently come to the forefront of the High-Performance Computing (HPC) community's interest. HPC computer systems that incorporate special purpose accelerators, such as Graphics Processing Units (GPUs), are said to be heterogeneous. Large scale heterogeneous computing systems have consistently ranked highly on the Top500 list since the beginning of the heterogeneous computing trend. By using heterogeneous computing systems that consist of both general purpose processors and special- purpose accelerators, the speed and problem size of many simulations could be dramatically increased. Ultimately this results in enhanced simulation capabilities that allows, in some cases for the first time, the execution of parameter space and uncertainty analyses, model optimizations, and other inverse modeling techniques that are critical for scientific discovery and engineering analysis. However, simplifying the usage and optimization of codes for heterogeneous computing systems remains a challenge. This is particularly true for scientists and engineers for whom understanding HPC architectures and undertaking performance analysis may not be primary research objectives. To enable scientists and engineers to remain focused on their primary research objectives, a modular environment for geophysical inversion and run-time autotuning on heterogeneous computing systems is presented. This environment is composed of three major components: 1) CUSH---a framework for reducing the complexity of programming heterogeneous computer systems, 2) geophysical inversion routines which can be used to characterize physical systems, and 3) run-time autotuning routines designed to determine configurations of heterogeneous computing systems in an attempt to maximize the performance of scientific and engineering codes. Using three case studies, a lattice-Boltzmann method, a non-negative least squares inversion, and a finite-difference fluid flow method, it is shown that this environment provides scientists and engineers with means to reduce the programmatic complexity of their applications, to perform geophysical inversions for characterizing physical systems, and to determine high-performing run-time configurations of heterogeneous computing systems using a run-time autotuner.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://files.eric.ed.gov/fulltext/EJ1175347.pdf','ERIC'); return false;" href="http://files.eric.ed.gov/fulltext/EJ1175347.pdf"><span>Problems Encountered during the Scientific Research Process in Graduate Education: The Institute of Educational Sciences</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Akyürek, Erkan; Afacan, Özlem</p> <p>2018-01-01</p> <p>This study was conducted to determine the problems faced by graduate students when conducting scientific research and to make suggestions for solving these problems. The research model was a case study. Semi-structured interviews were conducted with participants in the study with questions about the problems encountered during scientific research…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5742150','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5742150"><span>Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fonseca Guerra, Gabriel A.; Furber, Steve B.</p> <p>2017-01-01</p> <p>Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart. PMID:29311791</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/21431244','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/21431244"><span>Biomedical ontologies: toward scientific debate.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Maojo, V; Crespo, J; García-Remesal, M; de la Iglesia, D; Perez-Rey, D; Kulikowski, C</p> <p>2011-01-01</p> <p>Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective. We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies. We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies. Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940017627','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940017627"><span>Introduction to the LaRC central scientific computing complex</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Shoosmith, John N.</p> <p>1993-01-01</p> <p>The computers and associated equipment that make up the Central Scientific Computing Complex of the Langley Research Center are briefly described. The electronic networks that provide access to the various components of the complex and a number of areas that can be used by Langley and contractors staff for special applications (scientific visualization, image processing, software engineering, and grid generation) are also described. Flight simulation facilities that use the central computers are described. Management of the complex, procedures for its use, and available services and resources are discussed. This document is intended for new users of the complex, for current users who wish to keep appraised of changes, and for visitors who need to understand the role of central scientific computers at Langley.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26262360','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26262360"><span>Data Curation: Improving Environmental Health Data Quality.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Yang, Lin; Li, Jiao; Hou, Li; Qian, Qing</p> <p>2015-01-01</p> <p>With the growing recognition of the influence of climate change on human health, scientists' attention to analyzing the relationship between meteorological factors and adverse health effects. However, the paucity of high quality integrated data is one of the great challenges, especially when scientific studies rely on data-intensive computing. This paper aims to design an appropriate curation process to address this problem. We present a data curation workflow that: (i) follows the guidance of DCC Curation Lifecycle Model; (ii) combines manual curation with automatic curation; (iii) and solves environmental health data curation problem. The workflow was applied to a medical knowledge service system and showed that it was capable of improving work efficiency and data quality.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2644618','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2644618"><span>OMPC: an Open-Source MATLAB®-to-Python Compiler</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Jurica, Peter; van Leeuwen, Cees</p> <p>2008-01-01</p> <p>Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB®, the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB®-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB® functions into Python programs. The imported MATLAB® modules will run independently of MATLAB®, relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB®. OMPC is available at http://ompc.juricap.com. PMID:19225577</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20010124102','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20010124102"><span>Scientific Computing Paradigm</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>VanZandt, John</p> <p>1994-01-01</p> <p>The usage model of supercomputers for scientific applications, such as computational fluid dynamics (CFD), has changed over the years. Scientific visualization has moved scientists away from looking at numbers to looking at three-dimensional images, which capture the meaning of the data. This change has impacted the system models for computing. This report details the model which is used by scientists at NASA's research centers.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=sessa&pg=3&id=EJ1081578','ERIC'); return false;" href="https://eric.ed.gov/?q=sessa&pg=3&id=EJ1081578"><span>Commentary: Considerations in Pedagogy and Assessment in the Use of Computers to Promote Learning about Scientific Models</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Adams, Stephen T.</p> <p>2004-01-01</p> <p>Although one role of computers in science education is to help students learn specific science concepts, computers are especially intriguing as a vehicle for fostering the development of epistemological knowledge about the nature of scientific knowledge--what it means to "know" in a scientific sense (diSessa, 1985). In this vein, the…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.epa.gov/hesc','PESTICIDES'); return false;" href="https://www.epa.gov/hesc"><span>High-End Scientific Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.epa.gov/pesticides/search.htm">EPA Pesticide Factsheets</a></p> <p></p> <p></p> <p>EPA uses high-end scientific computing, geospatial services and remote sensing/imagery analysis to support EPA's mission. The Center for Environmental Computing (CEC) assists the Agency's program offices and regions to meet staff needs in these areas.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730023424','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730023424"><span>Scene analysis for a breadboard Mars robot functioning in an indoor environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Levine, M. D.</p> <p>1973-01-01</p> <p>The problem is delt with of computer perception in an indoor laboratory environment containing rocks of various sizes. The sensory data processing is required for the NASA/JPL breadboard mobile robot that is a test system for an adaptive variably-autonomous vehicle that will conduct scientific explorations on the surface of Mars. Scene analysis is discussed in terms of object segmentation followed by feature extraction, which results in a representation of the scene in the robot's world model.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29379257','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29379257"><span>Current trends for customized biomedical software tools.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Khan, Haseeb Ahmad</p> <p>2017-01-01</p> <p>In the past, biomedical scientists were solely dependent on expensive commercial software packages for various applications. However, the advent of user-friendly programming languages and open source platforms has revolutionized the development of simple and efficient customized software tools for solving specific biomedical problems. Many of these tools are designed and developed by biomedical scientists independently or with the support of computer experts and often made freely available for the benefit of scientific community. The current trends for customized biomedical software tools are highlighted in this short review.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018QuEle..48..401K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018QuEle..48..401K"><span>Work on the physics of ultracold atoms in Russia</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Kolachevsky, N. N.; Taichenachev, A. V.</p> <p>2018-05-01</p> <p>In December 2017, the regular All-Russian Conference 'Physics of Ultracold Atoms' was held. Several tens of Russian scientists from major scientific centres of the country, as well as a number of leading foreign scientists took part in the Conference. The Conference topics covered a wide range of urgent problems: quantum metrology, quantum gases, waves of matter, spectroscopy, quantum computing, and laser cooling. This issue of Quantum Electronics publishes the papers reported at the conference and selected for the Journal by the Organising committee.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.dtic.mil/docs/citations/ADA134542','DTIC-ST'); return false;" href="http://www.dtic.mil/docs/citations/ADA134542"><span>A Characterization of an Element of Best Simultaneous Approximation.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.dtic.mil/">DTIC Science & Technology</a></p> <p></p> <p>1983-09-01</p> <p>space Work Unit Number 3 - Numerical Analysis and Scientific Computing C . R. Category: G.1.2 * Department of Mathematics, Lewis and Clark College...Portland, OR 97219, USA. Sponsored by the United States Army under Contract No. DAAG29-80- C -0041. - ,1I K SIGNIFICANCE AND EXPLANATION A basic problem of...under Contract No. DAAG29-80- C -0041. others, e.g. Goel et.al. [8], ltilman (14], and Phillips et.al. [15], give characterizations in terms of one or</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1222988','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1222988"><span>Costa - Introduction to 2015 Annual Report</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Costa, James E.</p> <p></p> <p>In parallel with Sandia National Laboratories having two major locations (NM and CA), along with a number of smaller facilities across the nation, so too is the distribution of scientific, engineering and computing resources. As a part of Sandia’s Institutional Computing Program, CA site-based Sandia computer scientists and engineers have been providing mission and research staff with local CA resident expertise on computing options while also focusing on two growing high performance computing research problems. The first is how to increase system resilience to failure, as machines grow larger, more complex and heterogeneous. The second is how to ensure thatmore » computer hardware and configurations are optimized for specialized data analytical mission needs within the overall Sandia computing environment, including the HPC subenvironment. All of these activities support the larger Sandia effort in accelerating development and integration of high performance computing into national security missions. Sandia continues to both promote national R&D objectives, including the recent Presidential Executive Order establishing the National Strategic Computing Initiative and work to ensure that the full range of computing services and capabilities are available for all mission responsibilities, from national security to energy to homeland defense.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002PhPl....9.1856T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002PhPl....9.1856T"><span>Advanced computations in plasma physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, W. M.</p> <p>2002-05-01</p> <p>Scientific simulation in tandem with theory and experiment is an essential tool for understanding complex plasma behavior. In this paper we review recent progress and future directions for advanced simulations in magnetically confined plasmas with illustrative examples chosen from magnetic confinement research areas such as microturbulence, magnetohydrodynamics, magnetic reconnection, and others. Significant recent progress has been made in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics, giving increasingly good agreement between experimental observations and computational modeling. This was made possible by innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning widely disparate temporal and spatial scales together with access to powerful new computational resources. In particular, the fusion energy science community has made excellent progress in developing advanced codes for which computer run-time and problem size scale well with the number of processors on massively parallel machines (MPP's). A good example is the effective usage of the full power of multi-teraflop (multi-trillion floating point computations per second) MPP's to produce three-dimensional, general geometry, nonlinear particle simulations which have accelerated progress in understanding the nature of turbulence self-regulation by zonal flows. It should be emphasized that these calculations, which typically utilized billions of particles for thousands of time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In general, results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. The associated scientific excitement should serve to stimulate improved cross-cutting collaborations with other fields and also to help attract bright young talent to plasma science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017APS..APR.S8002J','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017APS..APR.S8002J"><span>Lattice QCD Calculations in Nuclear Physics towards the Exascale</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Joo, Balint</p> <p>2017-01-01</p> <p>The combination of algorithmic advances and new highly parallel computing architectures are enabling lattice QCD calculations to tackle ever more complex problems in nuclear physics. In this talk I will review some computational challenges that are encountered in large scale cold nuclear physics campaigns such as those in hadron spectroscopy calculations. I will discuss progress in addressing these with algorithmic improvements such as multi-grid solvers and software for recent hardware architectures such as GPUs and Intel Xeon Phi, Knights Landing. Finally, I will highlight some current topics for research and development as we head towards the Exascale era This material is funded by the U.S. Department of Energy, Office Of Science, Offices of Nuclear Physics, High Energy Physics and Advanced Scientific Computing Research, as well as the Office of Nuclear Physics under contract DE-AC05-06OR23177.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_17");'>17</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li class="active"><span>19</span></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_19 --> <div id="page_20" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="381"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5586818','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=5586818"><span>They See a Rat, We Seek a Cure for Diseases: The Current Status of Animal Experimentation in Medical Practice</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Kehinde, Elijah O.</p> <p>2013-01-01</p> <p>The objective of this review article was to examine current and prospective developments in the scientific use of laboratory animals, and to find out whether or not there are still valid scientific benefits of and justification for animal experimentation. The PubMed and Web of Science databases were searched using the following key words: animal models, basic research, pharmaceutical research, toxicity testing, experimental surgery, surgical simulation, ethics, animal welfare, benign, malignant diseases. Important relevant reviews, original articles and references from 1970 to 2012 were reviewed for data on the use of experimental animals in the study of diseases. The use of laboratory animals in scientific research continues to generate intense public debate. Their use can be justified today in the following areas of research: basic scientific research, use of animals as models for human diseases, pharmaceutical research and development, toxicity testing and teaching of new surgical techniques. This is because there are inherent limitations in the use of alternatives such as in vitro studies, human clinical trials or computer simulation. However, there are problems of transferability of results obtained from animal research to humans. Efforts are on-going to find suitable alternatives to animal experimentation like cell and tissue culture and computer simulation. For the foreseeable future, it would appear that to enable scientists to have a more precise understanding of human disease, including its diagnosis, prognosis and therapeutic intervention, there will still be enough grounds to advocate animal experimentation. However, efforts must continue to minimize or eliminate the need for animal testing in scientific research as soon as possible. PMID:24217224</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/24217224','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/24217224"><span>They see a rat, we seek a cure for diseases: the current status of animal experimentation in medical practice.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kehinde, Elijah O</p> <p>2013-01-01</p> <p>The objective of this review article was to examine current and prospective developments in the scientific use of laboratory animals, and to find out whether or not there are still valid scientific benefits of and justification for animal experimentation. The PubMed and Web of Science databases were searched using the following key words: animal models, basic research, pharmaceutical research, toxicity testing, experimental surgery, surgical simulation, ethics, animal welfare, benign, malignant diseases. Important relevant reviews, original articles and references from 1970 to 2012 were reviewed for data on the use of experimental animals in the study of diseases. The use of laboratory animals in scientific research continues to generate intense public debate. Their use can be justified today in the following areas of research: basic scientific research, use of animals as models for human diseases, pharmaceutical research and development, toxicity testing and teaching of new surgical techniques. This is because there are inherent limitations in the use of alternatives such as in vitro studies, human clinical trials or computer simulation. However, there are problems of transferability of results obtained from animal research to humans. Efforts are on-going to find suitable alternatives to animal experimentation like cell and tissue culture and computer simulation. For the foreseeable future, it would appear that to enable scientists to have a more precise understanding of human disease, including its diagnosis, prognosis and therapeutic intervention, there will still be enough grounds to advocate animal experimentation. However, efforts must continue to minimize or eliminate the need for animal testing in scientific research as soon as possible. © 2013 S. Karger AG, Basel.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003EJPh...24..329P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003EJPh...24..329P"><span>BOOK REVIEW: Numerical Recipes in C++: The Art of Scientific Computing (2nd edn)1 Numerical Recipes Example Book (C++) (2nd edn)2 Numerical Recipes Multi-Language Code CD ROM with LINUX or UNIX Single-Screen License Revised Version3Numerical Recipes in C++: The Art of Scientific Computing (2nd edn) Numerical Recipes Example Book (C++) (2nd edn) Numerical Recipes Multi-Language Code CD ROM with LINUX or UNIX Single-Screen License Revised Version</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Press, William H.; Teukolsky, Saul A.; Vettering, William T.; Flannery, Brian P.</p> <p>2003-05-01</p> <p>The two Numerical Recipes books are marvellous. The principal book, The Art of Scientific Computing, contains program listings for almost every conceivable requirement, and it also contains a well written discussion of the algorithms and the numerical methods involved. The Example Book provides a complete driving program, with helpful notes, for nearly all the routines in the principal book. The first edition of Numerical Recipes: The Art of Scientific Computing was published in 1986 in two versions, one with programs in Fortran, the other with programs in Pascal. There were subsequent versions with programs in BASIC and in C. The second, enlarged edition was published in 1992, again in two versions, one with programs in Fortran (NR(F)), the other with programs in C (NR(C)). In 1996 the authors produced Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing as a supplement, called Volume 2, with the original (Fortran) version referred to as Volume 1. Numerical Recipes in C++ (NR(C++)) is another version of the 1992 edition. The numerical recipes are also available on a CD ROM: if you want to use any of the recipes, I would strongly advise you to buy the CD ROM. The CD ROM contains the programs in all the languages. When the first edition was published I bought it, and have also bought copies of the other editions as they have appeared. Anyone involved in scientific computing ought to have a copy of at least one version of Numerical Recipes, and there also ought to be copies in every library. If you already have NR(F), should you buy the NR(C++) and, if not, which version should you buy? In the preface to Volume 2 of NR(F), the authors say 'C and C++ programmers have not been far from our minds as we have written this volume, and we think that you will find that time spent in absorbing its principal lessons will be amply repaid in the future as C and C++ eventually develop standard parallel extensions'. In the preface and introduction to NR(C++), the authors point out some of the problems in the use of C++ in scientific computing. I have not found any mention of parallel computing in NR(C++). Fortran has quite a lot going for it. As someone who has used it in most of its versions from Fortran II, I have seen it develop and leave behind other languages promoted by various enthusiasts: who now uses Algol or Pascal? I think it unlikely that C++ will disappear: it was devised as a systems language, and can also be used for other purposes such as scientific computing. It is possible that Fortran will disappear, but Fortran has the strengths that it can develop, that there are extensive Fortran subroutine libraries, and that it has been developed for parallel computing. To argue with programmers as to which is the best language to use is sterile. If you wish to use C++, then buy NR(C++), but you should also look at volume 2 of NR(F). If you are a Fortran programmer, then make sure you have NR(F), volumes 1 and 2. But whichever language you use, make sure you have one version or the other, and the CD ROM. The Example Book provides listings of complete programs to run nearly all the routines in NR, frequently based on cases where an anlytical solution is available. It is helpful when developing a new program incorporating an unfamiliar routine to see that routine actually working, and this is what the programs in the Example Book achieve. I started teaching computational physics before Numerical Recipes was published. If I were starting again, I would make heavy use of both The Art of Scientific Computing and of the Example Book. Every computational physics teaching laboratory should have both volumes: the programs in the Example Book are included on the CD ROM, but the extra commentary in the book itself is of considerable value. P Borcherds</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010AGUFMIN44A..06S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010AGUFMIN44A..06S"><span>GPU Implementation of High Rayleigh Number Three-Dimensional Mantle Convection</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sanchez, D. A.; Yuen, D. A.; Wright, G. B.; Barnett, G. A.</p> <p>2010-12-01</p> <p>Although we have entered the age of petascale computing, many factors are still prohibiting high-performance computing (HPC) from infiltrating all suitable scientific disciplines. For this reason and others, application of GPU to HPC is gaining traction in the scientific world. With its low price point, high performance potential, and competitive scalability, GPU has been an option well worth considering for the last few years. Moreover with the advent of NVIDIA's Fermi architecture, which brings ECC memory, better double-precision performance, and more RAM to GPU, there is a strong message of corporate support for GPU in HPC. However many doubts linger concerning the practicality of using GPU for scientific computing. In particular, GPU has a reputation for being difficult to program and suitable for only a small subset of problems. Although inroads have been made in addressing these concerns, for many scientists GPU still has hurdles to clear before becoming an acceptable choice. We explore the applicability of GPU to geophysics by implementing a three-dimensional, second-order finite-difference model of Rayleigh-Benard thermal convection on an NVIDIA GPU using C for CUDA. Our code reaches sufficient resolution, on the order of 500x500x250 evenly-spaced finite-difference gridpoints, on a single GPU. We make extensive use of highly optimized CUBLAS routines, allowing us to achieve performance on the order of O( 0.1 ) µs per timestep*gridpoint at this resolution. This performance has allowed us to study high Rayleigh number simulations, on the order of 2x10^7, on a single GPU.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22346639','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22346639"><span>A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>He, Bo; Zhang, Shujing; Yan, Tianhong; Zhang, Tao; Liang, Yan; Zhang, Hongjin</p> <p>2011-01-01</p> <p>Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale simultaneous localization and mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a combined SLAM-an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017APS..DPPY11020F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017APS..DPPY11020F"><span>Multigrid treatment of implicit continuum diffusion</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Francisquez, Manaure; Zhu, Ben; Rogers, Barrett</p> <p>2017-10-01</p> <p>Implicit treatment of diffusive terms of various differential orders common in continuum mechanics modeling, such as computational fluid dynamics, is investigated with spectral and multigrid algorithms in non-periodic 2D domains. In doubly periodic time dependent problems these terms can be efficiently and implicitly handled by spectral methods, but in non-periodic systems solved with distributed memory parallel computing and 2D domain decomposition, this efficiency is lost for large numbers of processors. We built and present here a multigrid algorithm for these types of problems which outperforms a spectral solution that employs the highly optimized FFTW library. This multigrid algorithm is not only suitable for high performance computing but may also be able to efficiently treat implicit diffusion of arbitrary order by introducing auxiliary equations of lower order. We test these solvers for fourth and sixth order diffusion with idealized harmonic test functions as well as a turbulent 2D magnetohydrodynamic simulation. It is also shown that an anisotropic operator without cross-terms can improve model accuracy and speed, and we examine the impact that the various diffusion operators have on the energy, the enstrophy, and the qualitative aspect of a simulation. This work was supported by DOE-SC-0010508. This research used resources of the National Energy Research Scientific Computing Center (NERSC).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3916069','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=3916069"><span>Using the High-Level Based Program Interface to Facilitate the Large Scale Scientific Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Shang, Yizi; Shang, Ling; Gao, Chuanchang; Lu, Guiming; Ye, Yuntao; Jia, Dongdong</p> <p>2014-01-01</p> <p>This paper is to make further research on facilitating the large-scale scientific computing on the grid and the desktop grid platform. The related issues include the programming method, the overhead of the high-level program interface based middleware, and the data anticipate migration. The block based Gauss Jordan algorithm as a real example of large-scale scientific computing is used to evaluate those issues presented above. The results show that the high-level based program interface makes the complex scientific applications on large-scale scientific platform easier, though a little overhead is unavoidable. Also, the data anticipation migration mechanism can improve the efficiency of the platform which needs to process big data based scientific applications. PMID:24574931</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017APS..DPPG11156B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017APS..DPPG11156B"><span>Code Modernization of VPIC</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bird, Robert; Nystrom, David; Albright, Brian</p> <p>2017-10-01</p> <p>The ability of scientific simulations to effectively deliver performant computation is increasingly being challenged by successive generations of high-performance computing architectures. Code development to support efficient computation on these modern architectures is both expensive, and highly complex; if it is approached without due care, it may also not be directly transferable between subsequent hardware generations. Previous works have discussed techniques to support the process of adapting a legacy code for modern hardware generations, but despite the breakthroughs in the areas of mini-app development, portable-performance, and cache oblivious algorithms the problem still remains largely unsolved. In this work we demonstrate how a focus on platform agnostic modern code-development can be applied to Particle-in-Cell (PIC) simulations to facilitate effective scientific delivery. This work builds directly on our previous work optimizing VPIC, in which we replaced intrinsic based vectorisation with compile generated auto-vectorization to improve the performance and portability of VPIC. In this work we present the use of a specialized SIMD queue for processing some particle operations, and also preview a GPU capable OpenMP variant of VPIC. Finally we include a lessons learnt. Work performed under the auspices of the U.S. Dept. of Energy by the Los Alamos National Security, LLC Los Alamos National Laboratory under contract DE-AC52-06NA25396 and supported by the LANL LDRD program.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Science+AND+materials&pg=6&id=EJ1087675','ERIC'); return false;" href="https://eric.ed.gov/?q=Science+AND+materials&pg=6&id=EJ1087675"><span>Defining Computational Thinking for Mathematics and Science Classrooms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri</p> <p>2016-01-01</p> <p>Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include "computational thinking" as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1364635-experimental-evaluation-flexible-architecture-accelerating-workflow-engines-ultrascale-environments','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1364635-experimental-evaluation-flexible-architecture-accelerating-workflow-engines-ultrascale-environments"><span>Experimental evaluation of a flexible I/O architecture for accelerating workflow engines in ultrascale environments</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>Duro, Francisco Rodrigo; Blas, Javier Garcia; Isaila, Florin; ...</p> <p>2016-10-06</p> <p>The increasing volume of scientific data and the limited scalability and performance of storage systems are currently presenting a significant limitation for the productivity of the scientific workflows running on both high-performance computing (HPC) and cloud platforms. Clearly needed is better integration of storage systems and workflow engines to address this problem. This paper presents and evaluates a novel solution that leverages codesign principles for integrating Hercules—an in-memory data store—with a workflow management system. We consider four main aspects: workflow representation, task scheduling, task placement, and task termination. As a result, the experimental evaluation on both cloud and HPC systemsmore » demonstrates significant performance and scalability improvements over existing state-of-the-art approaches.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=learn+AND+russian&pg=4&id=EJ155201','ERIC'); return false;" href="https://eric.ed.gov/?q=learn+AND+russian&pg=4&id=EJ155201"><span>Ermittlung von Wortstaemmen in russischen wissenschaftlichen Fachsprachen mit Hilfe des Computers (Establishing Word Stems in Scientific Russian With the Aid of a Computer)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Halbauer, Siegfried</p> <p>1976-01-01</p> <p>It was considered that students of intensive scientific Russian courses could learn vocabulary more efficiently if they were taught word stems and how to combine them with prefixes and suffixes to form scientific words. The computer programs developed to identify the most important stems is discussed. (Text is in German.) (FB)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.osti.gov/sciencecinema/biblio/1009102','SCIGOVIMAGE-SCICINEMA'); return false;" href="http://www.osti.gov/sciencecinema/biblio/1009102"><span>Scientific Visualization: The Modern Oscilloscope for "Seeing the Unseeable" (LBNL Summer Lecture Series)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/sciencecinema/">ScienceCinema</a></p> <p>Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division and Scientific Visualization Group</p> <p>2018-05-07</p> <p>Summer Lecture Series 2008: Scientific visualization transforms abstract data into readily comprehensible images, provide a vehicle for "seeing the unseeable," and play a central role in both experimental and computational sciences. Wes Bethel, who heads the Scientific Visualization Group in the Computational Research Division, presents an overview of visualization and computer graphics, current research challenges, and future directions for the field.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110008540','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110008540"><span>Engineering and Scientific Applications: Using MatLab(Registered Trademark) for Data Processing and Visualization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Sen, Syamal K.; Shaykhian, Gholam Ali</p> <p>2011-01-01</p> <p>MatLab(R) (MATrix LABoratory) is a numerical computation and simulation tool that is used by thousands Scientists and Engineers in many cou ntries. MatLab does purely numerical calculations, which can be used as a glorified calculator or interpreter programming language; its re al strength is in matrix manipulations. Computer algebra functionalities are achieved within the MatLab environment using "symbolic" toolbo x. This feature is similar to computer algebra programs, provided by Maple or Mathematica to calculate with mathematical equations using s ymbolic operations. MatLab in its interpreter programming language fo rm (command interface) is similar with well known programming languag es such as C/C++, support data structures and cell arrays to define c lasses in object oriented programming. As such, MatLab is equipped with most ofthe essential constructs of a higher programming language. M atLab is packaged with an editor and debugging functionality useful t o perform analysis of large MatLab programs and find errors. We belie ve there are many ways to approach real-world problems; prescribed methods to ensure foregoing solutions are incorporated in design and ana lysis of data processing and visualization can benefit engineers and scientist in gaining wider insight in actual implementation of their perspective experiments. This presentation will focus on data processing and visualizations aspects of engineering and scientific applicati ons. Specifically, it will discuss methods and techniques to perform intermediate-level data processing covering engineering and scientifi c problems. MatLab programming techniques including reading various data files formats to produce customized publication-quality graphics, importing engineering and/or scientific data, organizing data in tabu lar format, exporting data to be used by other software programs such as Microsoft Excel, data presentation and visualization will be discussed. The presentation will emphasize creating practIcal scripts (pro grams) that extend the basic features of MatLab TOPICS mclude (1) Ma trix and vector analysis and manipulations (2) Mathematical functions (3) Symbolic calculations & functions (4) Import/export data files (5) Program lOgic and flow control (6) Writing function and passing parameters (7) Test application programs</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://www.osti.gov/sciencecinema/biblio/987140','SCIGOVIMAGE-SCICINEMA'); return false;" href="http://www.osti.gov/sciencecinema/biblio/987140"><span>Scientific Visualization, Seeing the Unseeable</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/sciencecinema/">ScienceCinema</a></p> <p>LBNL</p> <p>2017-12-09</p> <p>June 24, 2008 Berkeley Lab lecture: Scientific visualization transforms abstract data into readily comprehensible images, provide a vehicle for "seeing the unseeable," and play a central role in bo... June 24, 2008 Berkeley Lab lecture: Scientific visualization transforms abstract data into readily comprehensible images, provide a vehicle for "seeing the unseeable," and play a central role in both experimental and computational sciences. Wes Bethel, who heads the Scientific Visualization Group in the Computational Research Division, presents an overview of visualization and computer graphics, current research challenges, and future directions for the field.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940022862','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940022862"><span>Sculpting in cyberspace: Parallel processing the development of new software</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Fisher, Rob</p> <p>1993-01-01</p> <p>Stimulating creativity in problem solving, particularly where software development is involved, is applicable to many disciplines. Metaphorical thinking keeps the problem in focus but in a different light, jarring people out of their mental ruts and sparking fresh insights. It forces the mind to stretch to find patterns between dissimilar concepts, in the hope of discovering unusual ideas in odd associations (Technology Review January 1993, p. 37). With a background in Engineering and Visual Design from MIT, I have for the past 30 years pursued a career as a sculptor of interdisciplinary monumental artworks that bridge the fields of science, engineering and art. Since 1979, I have pioneered the application of computer simulation to solve the complex problems associated with these projects. A recent project for the roof of the Carnegie Science Center in Pittsburgh made particular use of the metaphoric creativity technique described above. The problem-solving process led to the creation of hybrid software combining scientific, architectural and engineering visualization techniques. David Steich, a Doctoral Candidate in Electrical Engineering at Penn State, was commissioned to develop special software that enabled me to create innovative free-form sculpture. This paper explores the process of inventing the software through a detailed analysis of the interaction between an artist and a computer programmer.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2002PhDT.......148G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2002PhDT.......148G"><span>Testing the effectiveness of problem-based learning with learning-disabled students in biology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Guerrera, Claudia Patrizia</p> <p></p> <p>The purpose of the present study was to investigate the effects of problem-based learning (PBL) with learning-disabled (LD) students. Twenty-four students (12 dyads) classified as LD and attending a school for the learning-disabled participated in the study. Students engaged in either a computer-based environment involving BioWorld, a hospital simulation designed to teach biology students problem-solving skills, or a paper-and-pencil version based on the computer program. A hybrid model of learning was adopted whereby students were provided with direct instruction on the digestive system prior to participating in a problem-solving activity. Students worked in dyads and solved three problems involving the digestive system in either a computerized or a paper-and-pencil condition. The experimenter acted as a coach to assist students throughout the problem-solving process. A follow-up study was conducted, one month later, to measure the long-term learning gains. Quantitative and qualitative methods were used to analyze three types of data: process data, outcome data, and follow-up data. Results from the process data showed that all students engaged in effective collaboration and became more systematic in their problem solving over time. Findings from the outcome and follow-up data showed that students in both treatment conditions, made both learning and motivational gains and that these benefits were still evident one month later. Overall, results demonstrated that the computer facilitated students' problem solving and scientific reasoning skills. Some differences were noted in students' collaboration and the amount of assistance required from the coach in both conditions. Thus, PBL is an effective learning approach with LD students in science, regardless of the type of learning environment. These results have implications for teaching science to LD students, as well as for future designs of educational software for this population.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JSTEd..26..263B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JSTEd..26..263B"><span>A Blended Professional Development Program to Help a Teacher Learn to Provide One-to-One Scaffolding</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Belland, Brian R.; Burdo, Ryan; Gu, Jiangyue</p> <p>2015-04-01</p> <p>Argumentation is central to instruction centered on socio-scientific issues (Sadler & Donnelly in International Journal of Science Education, 28(12), 1463-1488, 2006. doi: 10.1080/09500690600708717). Teachers can play a big role in helping students engage in argumentation and solve authentic scientific problems. To do so, they need to learn one-to-one scaffolding—dynamic support to help students accomplish tasks that they could not complete unaided. This study explores a middle school science teacher's provision of one-to-one scaffolding during a problem-based learning unit, in which students argued about how to optimize the water quality of their local river. The blended professional development program incorporated three 1.5-h seminars, one 8-h workshop, and 4 weeks of online education activities. Data sources were video of three small groups per period, and what students typed in response to prompts from computer-based argumentation scaffolds. Results indicated that the teacher provided one-to-one scaffolding on a par with inquiry-oriented teachers described in the literature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29551955','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29551955"><span>Trajectory Optimization for Missions to Small Bodies with a Focus on Scientific Merit.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Englander, Jacob A; Vavrina, Matthew A; Lim, Lucy F; McFadden, Lucy A; Rhoden, Alyssa R; Noll, Keith S</p> <p>2017-01-01</p> <p>Trajectory design for missions to small bodies is tightly coupled both with the selection of targets for a mission and with the choice of spacecraft power, propulsion, and other hardware. Traditional methods of trajectory optimization have focused on finding the optimal trajectory for an a priori selection of destinations and spacecraft parameters. Recent research has expanded the field of trajectory optimization to multidisciplinary systems optimization that includes spacecraft parameters. The logical next step is to extend the optimization process to include target selection based not only on engineering figures of merit but also scientific value. This paper presents a new technique to solve the multidisciplinary mission optimization problem for small-bodies missions, including classical trajectory design, the choice of spacecraft power and propulsion systems, and also the scientific value of the targets. This technique, when combined with modern parallel computers, enables a holistic view of the small body mission design process that previously required iteration among several different design processes.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890017052','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890017052"><span>Solving large sparse eigenvalue problems on supercomputers</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Philippe, Bernard; Saad, Youcef</p> <p>1988-01-01</p> <p>An important problem in scientific computing consists in finding a few eigenvalues and corresponding eigenvectors of a very large and sparse matrix. The most popular methods to solve these problems are based on projection techniques on appropriate subspaces. The main attraction of these methods is that they only require the use of the matrix in the form of matrix by vector multiplications. The implementations on supercomputers of two such methods for symmetric matrices, namely Lanczos' method and Davidson's method are compared. Since one of the most important operations in these two methods is the multiplication of vectors by the sparse matrix, methods of performing this operation efficiently are discussed. The advantages and the disadvantages of each method are compared and implementation aspects are discussed. Numerical experiments on a one processor CRAY 2 and CRAY X-MP are reported. Possible parallel implementations are also discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008CoPhC.178..596B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008CoPhC.178..596B"><span>Resolution of singularities for multi-loop integrals</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bogner, Christian; Weinzierl, Stefan</p> <p>2008-04-01</p> <p>We report on a program for the numerical evaluation of divergent multi-loop integrals. The program is based on iterated sector decomposition. We improve the original algorithm of Binoth and Heinrich such that the program is guaranteed to terminate. The program can be used to compute numerically the Laurent expansion of divergent multi-loop integrals regulated by dimensional regularisation. The symbolic and the numerical steps of the algorithm are combined into one program. Program summaryProgram title: sector_decomposition Catalogue identifier: AEAG_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAG_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 47 506 No. of bytes in distributed program, including test data, etc.: 328 485 Distribution format: tar.gz Programming language: C++ Computer: all Operating system: Unix RAM: Depending on the complexity of the problem Classification: 4.4 External routines: GiNaC, available from http://www.ginac.de, GNU scientific library, available from http://www.gnu.org/software/gsl Nature of problem: Computation of divergent multi-loop integrals. Solution method: Sector decomposition. Restrictions: Only limited by the available memory and CPU time. Running time: Depending on the complexity of the problem.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_18");'>18</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li class="active"><span>20</span></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_20 --> <div id="page_21" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="401"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19225577','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19225577"><span>OMPC: an Open-Source MATLAB-to-Python Compiler.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Jurica, Peter; van Leeuwen, Cees</p> <p>2009-01-01</p> <p>Free access to scientific information facilitates scientific progress. Open-access scientific journals are a first step in this direction; a further step is to make auxiliary and supplementary materials that accompany scientific publications, such as methodological procedures and data-analysis tools, open and accessible to the scientific community. To this purpose it is instrumental to establish a software base, which will grow toward a comprehensive free and open-source language of technical and scientific computing. Endeavors in this direction are met with an important obstacle. MATLAB((R)), the predominant computation tool in many fields of research, is a closed-source commercial product. To facilitate the transition to an open computation platform, we propose Open-source MATLAB((R))-to-Python Compiler (OMPC), a platform that uses syntax adaptation and emulation to allow transparent import of existing MATLAB((R)) functions into Python programs. The imported MATLAB((R)) modules will run independently of MATLAB((R)), relying on Python's numerical and scientific libraries. Python offers a stable and mature open source platform that, in many respects, surpasses commonly used, expensive commercial closed source packages. The proposed software will therefore facilitate the transparent transition towards a free and general open-source lingua franca for scientific computation, while enabling access to the existing methods and algorithms of technical computing already available in MATLAB((R)). OMPC is available at http://ompc.juricap.com.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009CoPhC.180.2513Z','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009CoPhC.180.2513Z"><span>Virtualizing access to scientific applications with the Application Hosting Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Zasada, S. J.; Coveney, P. V.</p> <p>2009-12-01</p> <p>The growing power and number of high performance computing resources made available through computational grids present major opportunities as well as a number of challenges to the user. At issue is how these resources can be accessed and how their power can be effectively exploited. In this paper we first present our views on the usability of contemporary high-performance computational resources. We introduce the concept of grid application virtualization as a solution to some of the problems with grid-based HPC usability. We then describe a middleware tool that we have developed to realize the virtualization of grid applications, the Application Hosting Environment (AHE), and describe the features of the new release, AHE 2.0, which provides access to a common platform of federated computational grid resources in standard and non-standard ways. Finally, we describe a case study showing how AHE supports clinical use of whole brain blood flow modelling in a routine and automated fashion. Program summaryProgram title: Application Hosting Environment 2.0 Catalogue identifier: AEEJ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEEJ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU Public Licence, Version 2 No. of lines in distributed program, including test data, etc.: not applicable No. of bytes in distributed program, including test data, etc.: 1 685 603 766 Distribution format: tar.gz Programming language: Perl (server), Java (Client) Computer: x86 Operating system: Linux (Server), Linux/Windows/MacOS (Client) RAM: 134 217 728 (server), 67 108 864 (client) bytes Classification: 6.5 External routines: VirtualBox (server), Java (client) Nature of problem: The middleware that makes grid computing possible has been found by many users to be too unwieldy, and presents an obstacle to use rather than providing assistance [1,2]. Such problems are compounded when one attempts to harness the power of a grid, or a federation of different grids, rather than just a single resource on the grid. Solution method: To address the above problem, we have developed AHE, a lightweight interface, designed to simplify the process of running scientific codes on a grid of HPC and local resources. AHE does this by introducing a layer of middleware between the user and the grid, which encapsulates much of the complexity associated with launching grid applications. Unusual features: The server is distributed as a VirtualBox virtual machine. VirtualBox ( http://www.virtualbox.org) must be downloaded and installed in order to run the AHE server virtual machine. Details of how to do this are given in the AHE 2.0 Quick Start Guide. Running time: Not applicable References:J. Chin, P.V. Coveney, Towards tractable toolkits for the grid: A plea for lightweight, useable middleware, NeSC Technical Report, 2004, http://nesc.ac.uk/technical_papers/UKeS-2004-01.pdf. P.V. Coveney, R.S. Saksena, S.J. Zasada, M. McKeown, S. Pickles, The Application Hosting Environment: Lightweight middleware for grid-based computational science, Computer Physics Communications 176 (2007) 406-418.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015PhDT........56S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015PhDT........56S"><span>Problem Based Learning and the scientific process</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Schuchardt, Daniel Shaner</p> <p></p> <p>This research project was developed to inspire students to constructively use problem based learning and the scientific process to learn middle school science content. The student population in this study consisted of male and female seventh grade students. Students were presented with authentic problems that are connected to physical and chemical properties of matter. The intent of the study was to have students use the scientific process of looking at existing knowledge, generating learning issues or questions about the problems, and then developing a course of action to research and design experiments to model resolutions to the authentic problems. It was expected that students would improve their ability to actively engage with others in a problem solving process to achieve a deeper understanding of Michigan's 7th Grade Level Content Expectations, the Next Generation Science Standards, and a scientific process. Problem based learning was statistically effective in students' learning of the scientific process. Students statistically showed improvement on pre to posttest scores. The teaching method of Problem Based Learning was effective for seventh grade science students at Dowagiac Middle School.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/5070891','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/5070891"><span>Parallel computing works</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Not Available</p> <p></p> <p>An account of the Caltech Concurrent Computation Program (C{sup 3}P), a five year project that focused on answering the question: Can parallel computers be used to do large-scale scientific computations '' As the title indicates, the question is answered in the affirmative, by implementing numerous scientific applications on real parallel computers and doing computations that produced new scientific results. In the process of doing so, C{sup 3}P helped design and build several new computers, designed and implemented basic system software, developed algorithms for frequently used mathematical computations on massively parallel machines, devised performance models and measured the performance of manymore » computers, and created a high performance computing facility based exclusively on parallel computers. While the initial focus of C{sup 3}P was the hypercube architecture developed by C. Seitz, many of the methods developed and lessons learned have been applied successfully on other massively parallel architectures.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=scientific+AND+method&pg=7&id=EJ658133','ERIC'); return false;" href="https://eric.ed.gov/?q=scientific+AND+method&pg=7&id=EJ658133"><span>The Dogma of "The" Scientific Method.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Wivagg, Dan; Allchin, Douglas</p> <p>2002-01-01</p> <p>Points out major problems with the scientific method as a model for learning about methodology in science and suggests teaching about the scientists' toolbox to remedy problems with the conventional scientific method. (KHR)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1111221-exploring-cloud-computing-large-scale-scientific-applications','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1111221-exploring-cloud-computing-large-scale-scientific-applications"><span>Exploring Cloud Computing for Large-scale Scientific Applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Lin, Guang; Han, Binh; Yin, Jian</p> <p></p> <p>This paper explores cloud computing for large-scale data-intensive scientific applications. Cloud computing is attractive because it provides hardware and software resources on-demand, which relieves the burden of acquiring and maintaining a huge amount of resources that may be used only once by a scientific application. However, unlike typical commercial applications that often just requires a moderate amount of ordinary resources, large-scale scientific applications often need to process enormous amount of data in the terabyte or even petabyte range and require special high performance hardware with low latency connections to complete computation in a reasonable amount of time. To address thesemore » challenges, we build an infrastructure that can dynamically select high performance computing hardware across institutions and dynamically adapt the computation to the selected resources to achieve high performance. We have also demonstrated the effectiveness of our infrastructure by building a system biology application and an uncertainty quantification application for carbon sequestration, which can efficiently utilize data and computation resources across several institutions.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/800079','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/800079"><span>Terascale Computing in Accelerator Science and Technology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Ko, Kwok</p> <p>2002-08-21</p> <p>We have entered the age of ''terascale'' scientific computing. Processors and system architecture both continue to evolve; hundred-teraFLOP computers are expected in the next few years, and petaFLOP computers toward the end of this decade are conceivable. This ever-increasing power to solve previously intractable numerical problems benefits almost every field of science and engineering and is revolutionizing some of them, notably including accelerator physics and technology. At existing accelerators, it will help us optimize performance, expand operational parameter envelopes, and increase reliability. Design decisions for next-generation machines will be informed by unprecedented comprehensive and accurate modeling, as well as computer-aidedmore » engineering; all this will increase the likelihood that even their most advanced subsystems can be commissioned on time, within budget, and up to specifications. Advanced computing is also vital to developing new means of acceleration and exploring the behavior of beams under extreme conditions. With continued progress it will someday become reasonable to speak of a complete numerical model of all phenomena important to a particular accelerator.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110013052','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110013052"><span>Loci-STREAM Version 0.9</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wright, Jeffrey; Thakur, Siddharth</p> <p>2006-01-01</p> <p>Loci-STREAM is an evolving computational fluid dynamics (CFD) software tool for simulating possibly chemically reacting, possibly unsteady flows in diverse settings, including rocket engines, turbomachines, oil refineries, etc. Loci-STREAM implements a pressure- based flow-solving algorithm that utilizes unstructured grids. (The benefit of low memory usage by pressure-based algorithms is well recognized by experts in the field.) The algorithm is robust for flows at all speeds from zero to hypersonic. The flexibility of arbitrary polyhedral grids enables accurate, efficient simulation of flows in complex geometries, including those of plume-impingement problems. The present version - Loci-STREAM version 0.9 - includes an interface with the Portable, Extensible Toolkit for Scientific Computation (PETSc) library for access to enhanced linear-equation-solving programs therein that accelerate convergence toward a solution. The name "Loci" reflects the creation of this software within the Loci computational framework, which was developed at Mississippi State University for the primary purpose of simplifying the writing of complex multidisciplinary application programs to run in distributed-memory computing environments including clusters of personal computers. Loci has been designed to relieve application programmers of the details of programming for distributed-memory computers.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4528661','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4528661"><span>Probabilistic numerics and uncertainty in computations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Hennig, Philipp; Osborne, Michael A.; Girolami, Mark</p> <p>2015-01-01</p> <p>We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations. PMID:26346321</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/26346321','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/26346321"><span>Probabilistic numerics and uncertainty in computations.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Hennig, Philipp; Osborne, Michael A; Girolami, Mark</p> <p>2015-07-08</p> <p>We deliver a call to arms for probabilistic numerical methods : algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014AGUFMIN11A3598R','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014AGUFMIN11A3598R"><span>Handling the Diversity in the Coming Flood of InSAR Data with the InSAR Scientific Computing Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Rosen, P. A.; Gurrola, E. M.; Sacco, G. F.; Agram, P. S.; Lavalle, M.; Zebker, H. A.</p> <p>2014-12-01</p> <p>The NASA ESTO-developed InSAR Scientific Computing Environment (ISCE) provides acomputing framework for geodetic image processing for InSAR sensors that ismodular, flexible, and extensible, enabling scientists to reduce measurementsdirectly from a diverse array of radar satellites and aircraft to newgeophysical products. ISCE can serve as the core of a centralized processingcenter to bring Level-0 raw radar data up to Level-3 data products, but isadaptable to alternative processing approaches for science users interested innew and different ways to exploit mission data. This is accomplished throughrigorous componentization of processing codes, abstraction and generalization ofdata models, and a xml-based input interface with multi-level prioritizedcontrol of the component configurations depending on the science processingcontext. The proposed NASA-ISRO SAR (NISAR) Mission would deliver data ofunprecedented quantity and quality, making possible global-scale studies inclimate research, natural hazards, and Earth's ecosystems. ISCE is planned tobecome a key element in processing projected NISAR data into higher level dataproducts, enabling a new class of analyses that take greater advantage of thelong time and large spatial scales of these new data than current approaches.NISAR would be but one mission in a constellation of radar satellites in thefuture delivering such data. ISCE has been incorporated into two prototypecloud-based systems that have demonstrated its elasticity to addressing largerdata processing problems in a "production" context and its ability to becontrolled by individual science users on the cloud for large data problems.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012DPS....4411103D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012DPS....4411103D"><span>Asteroids@Home</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Durech, Josef; Hanus, J.; Vanco, R.</p> <p>2012-10-01</p> <p>We present a new project called Asteroids@home (http://asteroidsathome.net/boinc). It is a volunteer-computing project that uses an open-source BOINC (Berkeley Open Infrastructure for Network Computing) software to distribute tasks to volunteers, who provide their computing resources. The project was created at the Astronomical Institute, Charles University in Prague, in cooperation with the Czech National Team. The scientific aim of the project is to solve a time-consuming inverse problem of shape reconstruction of asteroids from sparse-in-time photometry. The time-demanding nature of the problem comes from the fact that with sparse-in-time photometry the rotation period of an asteroid is not apriori known and a huge parameter space must be densely scanned for the best solution. The nature of the problem makes it an ideal task to be solved by distributed computing - the period parameter space can be divided into small bins that can be scanned separately and then joined together to give the globally best solution. In the framework of the the project, we process asteroid photometric data from surveys together with asteroid lightcurves and we derive asteroid shapes and spin states. The algorithm is based on the lightcurve inversion method developed by Kaasalainen et al. (Icarus 153, 37, 2001). The enormous potential of distributed computing will enable us to effectively process also the data from future surveys (Large Synoptic Survey Telescope, Gaia mission, etc.). We also plan to process data of a synthetic asteroid population to reveal biases of the method. In our presentation, we will describe the project, show the first results (new models of asteroids), and discuss the possibilities of its further development. This work has been supported by the grant GACR P209/10/0537 of the Czech Science Foundation and by the Research Program MSM0021620860 of the Ministry of Education of the Czech Republic.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1362144','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1362144"><span>Final Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Marzouk, Youssef; Conrad, Patrick; Bigoni, Daniele</p> <p></p> <p>QUEST (\\url{www.quest-scidac.org}) is a SciDAC Institute that is focused on uncertainty quantification (UQ) in large-scale scientific computations. Our goals are to (1) advance the state of the art in UQ mathematics, algorithms, and software; and (2) provide modeling, algorithmic, and general UQ expertise, together with software tools, to other SciDAC projects, thereby enabling and guiding a broad range of UQ activities in their respective contexts. QUEST is a collaboration among six institutions (Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University) with a historymore » of joint UQ research. Our vision encompasses all aspects of UQ in leadership-class computing. This includes the well-founded setup of UQ problems; characterization of the input space given available data/information; local and global sensitivity analysis; adaptive dimensionality and order reduction; forward and inverse propagation of uncertainty; handling of application code failures, missing data, and hardware/software fault tolerance; and model inadequacy, comparison, validation, selection, and averaging. The nature of the UQ problem requires the seamless combination of data, models, and information across this landscape in a manner that provides a self-consistent quantification of requisite uncertainties in predictions from computational models. Accordingly, our UQ methods and tools span an interdisciplinary space across applied math, information theory, and statistics. The MIT QUEST effort centers on statistical inference and methods for surrogate or reduced-order modeling. MIT personnel have been responsible for the development of adaptive sampling methods, methods for approximating computationally intensive models, and software for both forward uncertainty propagation and statistical inverse problems. A key software product of the MIT QUEST effort is the MIT Uncertainty Quantification library, called MUQ (\\url{muq.mit.edu}).« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1421620-ensemble-grouping-strategies-embedded-stochastic-collocation-methods-applied-anisotropic-diffusion-problems','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1421620-ensemble-grouping-strategies-embedded-stochastic-collocation-methods-applied-anisotropic-diffusion-problems"><span>Ensemble Grouping Strategies for Embedded Stochastic Collocation Methods Applied to Anisotropic Diffusion Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>D'Elia, M.; Edwards, H. C.; Hu, J.</p> <p></p> <p>Previous work has demonstrated that propagating groups of samples, called ensembles, together through forward simulations can dramatically reduce the aggregate cost of sampling-based uncertainty propagation methods [E. Phipps, M. D'Elia, H. C. Edwards, M. Hoemmen, J. Hu, and S. Rajamanickam, SIAM J. Sci. Comput., 39 (2017), pp. C162--C193]. However, critical to the success of this approach when applied to challenging problems of scientific interest is the grouping of samples into ensembles to minimize the total computational work. For example, the total number of linear solver iterations for ensemble systems may be strongly influenced by which samples form the ensemble whenmore » applying iterative linear solvers to parameterized and stochastic linear systems. In this paper we explore sample grouping strategies for local adaptive stochastic collocation methods applied to PDEs with uncertain input data, in particular canonical anisotropic diffusion problems where the diffusion coefficient is modeled by truncated Karhunen--Loève expansions. Finally, we demonstrate that a measure of the total anisotropy of the diffusion coefficient is a good surrogate for the number of linear solver iterations for each sample and therefore provides a simple and effective metric for grouping samples.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/pages/biblio/1421620-ensemble-grouping-strategies-embedded-stochastic-collocation-methods-applied-anisotropic-diffusion-problems','SCIGOV-DOEP'); return false;" href="https://www.osti.gov/pages/biblio/1421620-ensemble-grouping-strategies-embedded-stochastic-collocation-methods-applied-anisotropic-diffusion-problems"><span>Ensemble Grouping Strategies for Embedded Stochastic Collocation Methods Applied to Anisotropic Diffusion Problems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/pages">DOE PAGES</a></p> <p>D'Elia, M.; Edwards, H. C.; Hu, J.; ...</p> <p>2018-01-18</p> <p>Previous work has demonstrated that propagating groups of samples, called ensembles, together through forward simulations can dramatically reduce the aggregate cost of sampling-based uncertainty propagation methods [E. Phipps, M. D'Elia, H. C. Edwards, M. Hoemmen, J. Hu, and S. Rajamanickam, SIAM J. Sci. Comput., 39 (2017), pp. C162--C193]. However, critical to the success of this approach when applied to challenging problems of scientific interest is the grouping of samples into ensembles to minimize the total computational work. For example, the total number of linear solver iterations for ensemble systems may be strongly influenced by which samples form the ensemble whenmore » applying iterative linear solvers to parameterized and stochastic linear systems. In this paper we explore sample grouping strategies for local adaptive stochastic collocation methods applied to PDEs with uncertain input data, in particular canonical anisotropic diffusion problems where the diffusion coefficient is modeled by truncated Karhunen--Loève expansions. Finally, we demonstrate that a measure of the total anisotropy of the diffusion coefficient is a good surrogate for the number of linear solver iterations for each sample and therefore provides a simple and effective metric for grouping samples.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/885226','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/885226"><span>Parallel Scaling Characteristics of Selected NERSC User ProjectCodes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Skinner, David; Verdier, Francesca; Anand, Harsh</p> <p></p> <p>This report documents parallel scaling characteristics of NERSC user project codes between Fiscal Year 2003 and the first half of Fiscal Year 2004 (Oct 2002-March 2004). The codes analyzed cover 60% of all the CPU hours delivered during that time frame on seaborg, a 6080 CPU IBM SP and the largest parallel computer at NERSC. The scale in terms of concurrency and problem size of the workload is analyzed. Drawing on batch queue logs, performance data and feedback from researchers we detail the motivations, benefits, and challenges of implementing highly parallel scientific codes on current NERSC High Performance Computing systems.more » An evaluation and outlook of the NERSC workload for Allocation Year 2005 is presented.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017JCoPh.338..477E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017JCoPh.338..477E"><span>Seafloor identification in sonar imagery via simulations of Helmholtz equations and discrete optimization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Engquist, Björn; Frederick, Christina; Huynh, Quyen; Zhou, Haomin</p> <p>2017-06-01</p> <p>We present a multiscale approach for identifying features in ocean beds by solving inverse problems in high frequency seafloor acoustics. The setting is based on Sound Navigation And Ranging (SONAR) imaging used in scientific, commercial, and military applications. The forward model incorporates multiscale simulations, by coupling Helmholtz equations and geometrical optics for a wide range of spatial scales in the seafloor geometry. This allows for detailed recovery of seafloor parameters including material type. Simulated backscattered data is generated using numerical microlocal analysis techniques. In order to lower the computational cost of the large-scale simulations in the inversion process, we take advantage of a pre-computed library of representative acoustic responses from various seafloor parameterizations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19730022508','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19730022508"><span>Numerical studies of unsteady two dimensional subsonic flows using the ICE method. Ph.D. Thesis - Toledo Univ.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Wieber, P. R.</p> <p>1973-01-01</p> <p>A numerical program was developed to compute transient compressible and incompressible laminar flows in two dimensions with multicomponent mixing and chemical reaction. The algorithm used the Los Alamos Scientific Laboratory ICE (Implicit Continuous-Fluid Eulerian) method as its base. The program can compute both high and low speed compressible flows. The numerical program incorporating the stabilization techniques was quite successful in treating both old and new problems. Detailed calculations of coaxial flow very close to the entry plane were possible. The program treated complex flows such as the formation and downstream growth of a recirculation cell. An implicit solution of the species equation predicted mixing and reaction rates which compared favorably with the literature.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015EPSC...10..211A','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015EPSC...10..211A"><span>iSPHERE - A New Approach to Collaborative Research and Cloud Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Al-Ubaidi, T.; Khodachenko, M. L.; Kallio, E. J.; Harry, A.; Alexeev, I. I.; Vázquez-Poletti, J. L.; Enke, H.; Magin, T.; Mair, M.; Scherf, M.; Poedts, S.; De Causmaecker, P.; Heynderickx, D.; Congedo, P.; Manolescu, I.; Esser, B.; Webb, S.; Ruja, C.</p> <p>2015-10-01</p> <p>The project iSPHERE (integrated Scientific Platform for HEterogeneous Research and Engineering) that has been proposed for Horizon 2020 (EINFRA-9- 2015, [1]) aims at creating a next generation Virtual Research Environment (VRE) that embraces existing and emerging technologies and standards in order to provide a versatile platform for scientific investigations and collaboration. The presentation will introduce the large project consortium, provide a comprehensive overview of iSPHERE's basic concepts and approaches and outline general user requirements that the VRE will strive to satisfy. An overview of the envisioned architecture will be given, focusing on the adapted Service Bus concept, i.e. the "Scientific Service Bus" as it is called in iSPHERE. The bus will act as a central hub for all communication and user access, and will be implemented in the course of the project. The agile approach [2] that has been chosen for detailed elaboration and documentation of user requirements, as well as for the actual implementation of the system, will be outlined and its motivation and basic structure will be discussed. The presentation will show which user communities will benefit and which concrete problems, scientific investigations are facing today, will be tackled by the system. Another focus of the presentation is iSPHERE's seamless integration of cloud computing resources and how these will benefit scientific modeling teams by providing a reliable and web based environment for cloud based model execution, storage of results, and comparison with measurements, including fully web based tools for data mining, analysis and visualization. Also the envisioned creation of a dedicated data model for experimental plasma physics will be discussed. It will be shown why the Scientific Service Bus provides an ideal basis to integrate a number of data models and communication protocols and to provide mechanisms for data exchange across multiple and even multidisciplinary platforms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012ASSL..395....9L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012ASSL..395....9L"><span>Georges Lemaître: The Priest Who Invented the Big Bang</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lambert, Dominique</p> <p></p> <p>This contribution gives a concise survey of Georges Lemaître works and life, shedding some light on less-known aspects. Lemaître is a Belgian catholic priest who gave for the first time in 1927 the explanation of the Hubble law and who proposed in 1931 the "Primeval Atom Hypothesis", considered as the first step towards the Big Bang cosmology. But the scientific work of Lemaître goes far beyond Physical Cosmology. Indeed, he contributed also to the theory of Cosmis Rays, to the Spinor theory, to Analytical mechanics (regularization of 3- Bodies problem), to Numerical Analysis (Fast Fourier Transform), to Computer Science (he introduced and programmed the first computer of Louvain),… Lemaître took part to the "Science and Faith" debate. He defended a position that has some analogy with the NOMA principle, making a sharp distinction between what he called the "two paths to Truth" (a scientific one and a theological one). In particular, he never made a confusion between the theological concept of "creation" and the scientific notion of "natural beginning" (initial singularity). Lemaître was deeply rooted in his faith and sacerdotal vocation. Remaining a secular priest, he belonged to a community of priests called "The Friends of Jesus", characterized by a deep spirituality and special vows (for example the vow of poverty). He had also an apostolic activity amongst Chinese students.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_19");'>19</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li class="active"><span>21</span></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_21 --> <div id="page_22" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="421"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19499141','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19499141"><span>Continued multidisciplinary project-based learning - implementation in health informatics.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Wessel, C; Spreckelsen, C</p> <p>2009-01-01</p> <p>Problem- and project-based learning are approved methods to train students, graduates and post-graduates in scientific and other professional skills. The students are trained on realistic scenarios in a broader context. For students specializing in health informatics we introduced continued multidisciplinary project-based learning (CM-PBL) at a department of medical informatics. The training approach addresses both students of medicine and students of computer science. The students are full members of an ongoing research project and develop a project-related application or module, or explore or evaluate a sub-project. Two teachers guide and review the students' work. The training on scientific work follows a workflow with defined milestones. The team acts as peer group. By participating in the research team's work the students are trained on professional skills. A research project on a web-based information system on hospitals built the scenario for the realistic context. The research team consisted of up to 14 active members at a time, who were scientists and students of computer science and medicine. The well communicated educational approach and team policy fostered the participation of the students. Formative assessment and evaluation showed a considerable improvement of the students' skills and a high participant satisfaction. Alternative education approaches such as project-based learning empower students to acquire scientific knowledge and professional skills, especially the ability of life-long learning, multidisciplinary team work and social responsibility.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4909110','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=4909110"><span>ISMB 2016 offers outstanding science, networking, and celebration</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Fogg, Christiana</p> <p>2016-01-01</p> <p>The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community. ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas. PMID:27347392</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/27347392','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/27347392"><span>ISMB 2016 offers outstanding science, networking, and celebration.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Fogg, Christiana</p> <p>2016-01-01</p> <p>The annual international conference on Intelligent Systems for Molecular Biology (ISMB) is the major meeting of the International Society for Computational Biology (ISCB). Over the past 23 years the ISMB conference has grown to become the world's largest bioinformatics/computational biology conference. ISMB 2016 will be the year's most important computational biology event globally. The conferences provide a multidisciplinary forum for disseminating the latest developments in bioinformatics/computational biology. ISMB brings together scientists from computer science, molecular biology, mathematics, statistics and related fields. Its principal focus is on the development and application of advanced computational methods for biological problems. ISMB 2016 offers the strongest scientific program and the broadest scope of any international bioinformatics/computational biology conference. Building on past successes, the conference is designed to cater to variety of disciplines within the bioinformatics/computational biology community. ISMB 2016 takes place July 8 - 12 at the Swan and Dolphin Hotel in Orlando, Florida, United States. For two days preceding the conference, additional opportunities including Satellite Meetings, Student Council Symposium, and a selection of Special Interest Group Meetings and Applied Knowledge Exchange Sessions (AKES) are all offered to enable registered participants to learn more on the latest methods and tools within specialty research areas.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EGUGA..19.8931E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EGUGA..19.8931E"><span>Understanding the seismic wave propagation inside and around an underground cavity from a 3D numerical survey</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Esterhazy, Sofi; Schneider, Felix; Perugia, Ilaria; Bokelmann, Götz</p> <p>2017-04-01</p> <p>Motivated by the need to detect an underground cavity within the procedure of an On-Site-Inspection (OSI) of the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO), which might be caused by a nuclear explosion/weapon testing, we aim to provide a basic numerical study of the wave propagation around and inside such an underground cavity. One method to investigate the geophysical properties of an underground cavity allowed by the Comprehensive Nuclear-test Ban Treaty is referred to as "resonance seismometry" - a resonance method that uses passive or active seismic techniques, relying on seismic cavity vibrations. This method is in fact not yet entirely determined by the Treaty and so far, there are only very few experimental examples that have been suitably documented to build a proper scientific groundwork. This motivates to investigate this problem on a purely numerical level and to simulate these events based on recent advances in numerical modeling of wave propagation problems. Our numerical study includes the full elastic wave field in three dimensions. We consider the effects from an incoming plane wave as well as point source located in the surrounding of the cavity at the surface. While the former can be considered as passive source like a tele-seismic earthquake, the latter represents a man-made explosion or a viborseis as used for/in active seismic techniques. Further we want to demonstrate the specific characteristics of the scattered wave field from a P-waves and S-wave separately. For our simulations in 3D we use the discontinuous Galerkin Spectral Element Code SPEED developed by MOX (The Laboratory for Modeling and Scientific Computing, Department of Mathematics) and DICA (Department of Civil and Environmental Engineering) at the Politecnico di Milano. The computations are carried out on the Vienna Scientific Cluster (VSC). The accurate numerical modeling can facilitate the development of proper analysis techniques to detect the remnants of an underground nuclear test, help to set a rigorous scientific base of OSI and contribute to bringing the Treaty into force.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=artificial+AND+intelligence+AND+computer+AND+games&pg=5&id=ED071369','ERIC'); return false;" href="https://eric.ed.gov/?q=artificial+AND+intelligence+AND+computer+AND+games&pg=5&id=ED071369"><span>Computers and Computation. Readings from Scientific American.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Fenichel, Robert R.; Weizenbaum, Joseph</p> <p></p> <p>A collection of articles from "Scientific American" magazine has been put together at this time because the current period in computer science is one of consolidation rather than innovation. A few years ago, computer science was moving so swiftly that even the professional journals were more archival than informative; but today it is…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005JPhCS..16.....M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005JPhCS..16.....M"><span>Preface: SciDAC 2005</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Mezzacappa, Anthony</p> <p>2005-01-01</p> <p>On 26-30 June 2005 at the Grand Hyatt on Union Square in San Francisco several hundred computational scientists from around the world came together for what can certainly be described as a celebration of computational science. Scientists from the SciDAC Program and scientists from other agencies and nations were joined by applied mathematicians and computer scientists to highlight the many successes in the past year where computation has led to scientific discovery in a variety of fields: lattice quantum chromodynamics, accelerator modeling, chemistry, biology, materials science, Earth and climate science, astrophysics, and combustion and fusion energy science. Also highlighted were the advances in numerical methods and computer science, and the multidisciplinary collaboration cutting across science, mathematics, and computer science that enabled these discoveries. The SciDAC Program was conceived and funded by the US Department of Energy Office of Science. It is the Office of Science's premier computational science program founded on what is arguably the perfect formula: the priority and focus is science and scientific discovery, with the understanding that the full arsenal of `enabling technologies' in applied mathematics and computer science must be brought to bear if we are to have any hope of attacking and ultimately solving today's computational Grand Challenge problems. The SciDAC Program has been in existence for four years, and many of the computational scientists funded by this program will tell you that the program has given them the hope of addressing their scientific problems in full realism for the very first time. Many of these scientists will also tell you that SciDAC has also fundamentally changed the way they do computational science. We begin this volume with one of DOE's great traditions, and core missions: energy research. As we will see, computation has been seminal to the critical advances that have been made in this arena. Of course, to understand our world, whether it is to understand its very nature or to understand it so as to control it for practical application, will require explorations on all of its scales. Computational science has been no less an important tool in this arena than it has been in the arena of energy research. From explorations of quantum chromodynamics, the fundamental theory that describes how quarks make up the protons and neutrons of which we are composed, to explorations of the complex biomolecules that are the building blocks of life, to explorations of some of the most violent phenomena in our universe and of the Universe itself, computation has provided not only significant insight, but often the only means by which we have been able to explore these complex, multicomponent systems and by which we have been able to achieve scientific discovery and understanding. While our ultimate target remains scientific discovery, it certainly can be said that at a fundamental level the world is mathematical. Equations ultimately govern the evolution of the systems of interest to us, be they physical, chemical, or biological systems. The development and choice of discretizations of these underlying equations is often a critical deciding factor in whether or not one is able to model such systems stably, faithfully, and practically, and in turn, the algorithms to solve the resultant discrete equations are the complementary, critical ingredient in the recipe to model the natural world. The use of parallel computing platforms, especially at the TeraScale, and the trend toward even larger numbers of processors, continue to present significant challenges in the development and implementation of these algorithms. Computational scientists often speak of their `workflows'. A workflow, as the name suggests, is the sum total of all complex and interlocking tasks, from simulation set up, execution, and I/O, to visualization and scientific discovery, through which the advancement in our understanding of the natural world is realized. For the computational scientist, enabling such workflows presents myriad, signiflcant challenges, and it is computer scientists that are called upon at such times to address these challenges. Simulations are currently generating data at the staggering rate of tens of TeraBytes per simulation, over the course of days. In the next few years, these data generation rates are expected to climb exponentially to hundreds of TeraBytes per simulation, performed over the course of months. The output, management, movement, analysis, and visualization of these data will be our key to unlocking the scientific discoveries buried within the data. And there is no hope of generating such data to begin with, or of scientific discovery, without stable computing platforms and a sufficiently high and sustained performance of scientific applications codes on them. Thus, scientific discovery in the realm of computational science at the TeraScale and beyond will occur at the intersection of science, applied mathematics, and computer science. The SciDAC Program was constructed to mirror this reality, and the pages that follow are a testament to the efficacy of such an approach. We would like to acknowledge the individuals on whose talents and efforts the success of SciDAC 2005 was based. Special thanks go to Betsy Riley for her work on the SciDAC 2005 Web site and meeting agenda, for lining up our corporate sponsors, for coordinating all media communications, and for her efforts in processing the proceedings contributions, to Sherry Hempfling for coordinating the overall SciDAC 2005 meeting planning, for handling a significant share of its associated communications, and for coordinating with the ORNL Conference Center and Grand Hyatt, to Angela Harris for producing many of the documents and records on which our meeting planning was based and for her efforts in coordinating with ORNL Graphics Services, to Angie Beach of the ORNL Conference Center for her efforts in procurement and setting up and executing the contracts with the hotel, and to John Bui and John Smith for their superb wireless networking and A/V set up and support. We are grateful for the relentless efforts of all of these individuals, their remarkable talents, and for the joy of working with them during this past year. They were the cornerstones of SciDAC 2005. Thanks also go to Kymba A'Hearn and Patty Boyd for on-site registration, Brittany Hagen for administrative support, Bruce Johnston for netcast support, Tim Jones for help with the proceedings and Web site, Sherry Lamb for housing and registration, Cindy Lathum for Web site design, Carolyn Peters for on-site registration, and Dami Rich for graphic design. And we would like to express our appreciation to the Oak Ridge National Laboratory, especially Jeff Nichols, the Argonne National Laboratory, the Lawrence Berkeley National Laboratory, and to our corporate sponsors, Cray, IBM, Intel, and SGI, for their support. We would like to extend special thanks also to our plenary speakers, technical speakers, poster presenters, and panelists for all of their efforts on behalf of SciDAC 2005 and for their remarkable achievements and contributions. We would like to express our deep appreciation to Lali Chatterjee, Graham Douglas and Margaret Smith of Institute of Physics Publishing, who worked tirelessly in order to provide us with this finished volume within two months, which is nothing short of miraculous. Finally, we wish to express our heartfelt thanks to Michael Strayer, SciDAC Director, whose vision it was to focus SciDAC 2005 on scientific discovery, around which all of the excitement we experienced revolved, and to our DOE SciDAC program managers, especially Fred Johnson, for their support, input, and help throughout.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN51B1693C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN51B1693C"><span>The Virtual Geophysics Laboratory (VGL): Scientific Workflows Operating Across Organizations and Across Infrastructures</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cox, S. J.; Wyborn, L. A.; Fraser, R.; Rankine, T.; Woodcock, R.; Vote, J.; Evans, B.</p> <p>2012-12-01</p> <p>The Virtual Geophysics Laboratory (VGL) is web portal that provides geoscientists with an integrated online environment that: seamlessly accesses geophysical and geoscience data services from the AuScope national geoscience information infrastructure; loosely couples these data to a variety of gesocience software tools; and provides large scale processing facilities via cloud computing. VGL is a collaboration between CSIRO, Geoscience Australia, National Computational Infrastructure, Monash University, Australian National University and the University of Queensland. The VGL provides a distributed system whereby a user can enter an online virtual laboratory to seamlessly connect to OGC web services for geoscience data. The data is supplied in open standards formats using international standards like GeoSciML. A VGL user uses a web mapping interface to discover and filter the data sources using spatial and attribute filters to define a subset. Once the data is selected the user is not required to download the data. VGL collates the service query information for later in the processing workflow where it will be staged directly to the computing facilities. The combination of deferring data download and access to Cloud computing enables VGL users to access their data at higher resolutions and to undertake larger scale inversions, more complex models and simulations than their own local computing facilities might allow. Inside the Virtual Geophysics Laboratory, the user has access to a library of existing models, complete with exemplar workflows for specific scientific problems based on those models. For example, the user can load a geological model published by Geoscience Australia, apply a basic deformation workflow provided by a CSIRO scientist, and have it run in a scientific code from Monash. Finally the user can publish these results to share with a colleague or cite in a paper. This opens new opportunities for access and collaboration as all the resources (models, code, data, processing) are shared in the one virtual laboratory. VGL provides end users with access to an intuitive, user-centered interface that leverages cloud storage and cloud and cluster processing from both the research communities and commercial suppliers (e.g. Amazon). As the underlying data and information services are agnostic of the scientific domain, they can support many other data types. This fundamental characteristic results in a highly reusable virtual laboratory infrastructure that could also be used for example natural hazards, satellite processing, soil geochemistry, climate modeling, agriculture crop modeling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMED11A0757W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMED11A0757W"><span>Mini-Journals: Incorporating Inquiry, Quantitative Skills and Writing into Homework Assignments for Geochemistry and Planetary Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Whittington, A. G.; Speck, A.; Witzig, S.</p> <p>2011-12-01</p> <p>As part of an NSF-funded project, "CUES: Connecting Undergraduates to the Enterprise of Science," new inquiry-based homework materials were developed for two upper-level classes at the University of Missouri: Geochemistry (required for Geology majors, fulfills the computing requirement by having 50% of the grade come from five spreadsheet-based homework assignments), and Solar System Science (open to seniors and graduate students, co-taught and cross-listed between Geology and Physics & Astronomy). Inquiry involves activities where the learner engages in scientifically oriented questions, gives priority to evidence in responding to questions, formulates explanations from evidence, connects explanations to scientific knowledge, and communicates and justifies explanations. We engage students in inquiry-based learning by presenting homework exercises as "mini-journal" articles that follow the format of a scientific journal article, including a title, authors, abstract, introduction, methods, results, discussion and citations to peer-reviewed literature. The mini-journal provides a scaffold and serves as a springboard for students to develop and carry out their own follow-up investigation. They then present their findings in the form of their own mini-journal. Mini-journals replace traditional homework problem sets with a format that more directly reflects and encourages scientific practice. Students are engaged in inquiry-based homework which encompass doing, thinking, and communicating, while the mini-journal allows the instructor to contain lines of inquiry within the limits posed by available resources. In the examples we present, research is conducted via spreadsheet modeling, where the students develop their own spreadsheets. Example assignments from Geochemistry include "Trace Element Partitioning During Mantle Melting and MORB Crystallization" and "Isotopic Investigations of Crustal Evolution in the Midcontinent US". The key differences between the old and new formats include (i) active participation of the students in defining the question/problem that they will pursue, within well-defined boundaries, (ii) open-ended nature of the inquiry, so that students need to recognize when they have enough information to answer their question, (iii) extensive spreadsheet manipulation and presentation of results in graphical and tabular formats, and (iv) a written discussion of their findings. Grading is weighted more towards how the problem was addressed, and how findings are presented and interpreted, and less on actual numerical answers. Survey responses from students indicate that they experience discomfort on being presented with an open-ended assignment, but like the freedom to define their own problem. Students also recognize that reading, writing and critical thinking skills employed in the minijournal format increase their understanding of content. The combination of calculation and writing components make these assignments particularly useful for classes designated as "computer-based", and/or "writing intensive" (or similar designations).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003AGUFMGP11A0250T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003AGUFMGP11A0250T"><span>Modular Approaches to Earth Science Scientific Computing: 3D Electromagnetic Induction Modeling as an Example</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tandon, K.; Egbert, G.; Siripunvaraporn, W.</p> <p>2003-12-01</p> <p>We are developing a modular system for three-dimensional inversion of electromagnetic (EM) induction data, using an object oriented programming approach. This approach allows us to modify the individual components of the inversion scheme proposed, and also reuse the components for variety of problems in earth science computing howsoever diverse they might be. In particular, the modularity allows us to (a) change modeling codes independently of inversion algorithm details; (b) experiment with new inversion algorithms; and (c) modify the way prior information is imposed in the inversion to test competing hypothesis and techniques required to solve an earth science problem. Our initial code development is for EM induction equations on a staggered grid, using iterative solution techniques in 3D. An example illustrated here is an experiment with the sensitivity of 3D magnetotelluric inversion to uncertainties in the boundary conditions required for regional induction problems. These boundary conditions should reflect the large-scale geoelectric structure of the study area, which is usually poorly constrained. In general for inversion of MT data, one fixes boundary conditions at the edge of the model domain, and adjusts the earth?s conductivity structure within the modeling domain. Allowing for errors in specification of the open boundary values is simple in principle, but no existing inversion codes that we are aware of have this feature. Adding a feature such as this is straightforward within the context of the modular approach. More generally, a modular approach provides an efficient methodology for setting up earth science computing problems to test various ideas. As a concrete illustration relevant to EM induction problems, we investigate the sensitivity of MT data near San Andreas Fault at Parkfield (California) to uncertainties in the regional geoelectric structure.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005PPCF...47....1T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005PPCF...47....1T"><span>TOPICAL REVIEW: Advances and challenges in computational plasma science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, W. M.; Chan, V. S.</p> <p>2005-02-01</p> <p>Scientific simulation, which provides a natural bridge between theory and experiment, is an essential tool for understanding complex plasma behaviour. Recent advances in simulations of magnetically confined plasmas are reviewed in this paper, with illustrative examples, chosen from associated research areas such as microturbulence, magnetohydrodynamics and other topics. Progress has been stimulated, in particular, by the exponential growth of computer speed along with significant improvements in computer technology. The advances in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics have produced increasingly good agreement between experimental observations and computational modelling. This was enabled by two key factors: (a) innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning widely disparate temporal and spatial scales and (b) access to powerful new computational resources. Excellent progress has been made in developing codes for which computer run-time and problem-size scale well with the number of processors on massively parallel processors (MPPs). Examples include the effective usage of the full power of multi-teraflop (multi-trillion floating point computations per second) MPPs to produce three-dimensional, general geometry, nonlinear particle simulations that have accelerated advances in understanding the nature of turbulence self-regulation by zonal flows. These calculations, which typically utilized billions of particles for thousands of time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In looking towards the future, the current results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. This should produce the scientific excitement which will help to (a) stimulate enhanced cross-cutting collaborations with other fields and (b) attract the bright young talent needed for the future health of the field of plasma science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2005PPCF...47R...1T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2005PPCF...47R...1T"><span>Advances and challenges in computational plasma science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, W. M.</p> <p>2005-02-01</p> <p>Scientific simulation, which provides a natural bridge between theory and experiment, is an essential tool for understanding complex plasma behaviour. Recent advances in simulations of magnetically confined plasmas are reviewed in this paper, with illustrative examples, chosen from associated research areas such as microturbulence, magnetohydrodynamics and other topics. Progress has been stimulated, in particular, by the exponential growth of computer speed along with significant improvements in computer technology. The advances in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics have produced increasingly good agreement between experimental observations and computational modelling. This was enabled by two key factors: (a) innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning widely disparate temporal and spatial scales and (b) access to powerful new computational resources. Excellent progress has been made in developing codes for which computer run-time and problem-size scale well with the number of processors on massively parallel processors (MPPs). Examples include the effective usage of the full power of multi-teraflop (multi-trillion floating point computations per second) MPPs to produce three-dimensional, general geometry, nonlinear particle simulations that have accelerated advances in understanding the nature of turbulence self-regulation by zonal flows. These calculations, which typically utilized billions of particles for thousands of time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In looking towards the future, the current results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. This should produce the scientific excitement which will help to (a) stimulate enhanced cross-cutting collaborations with other fields and (b) attract the bright young talent needed for the future health of the field of plasma science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001APS..DPPTR1001T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001APS..DPPTR1001T"><span>Advanced Computation in Plasma Physics</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tang, William</p> <p>2001-10-01</p> <p>Scientific simulation in tandem with theory and experiment is an essential tool for understanding complex plasma behavior. This talk will review recent progress and future directions for advanced simulations in magnetically-confined plasmas with illustrative examples chosen from areas such as microturbulence, magnetohydrodynamics, magnetic reconnection, and others. Significant recent progress has been made in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics, giving increasingly good agreement between experimental observations and computational modeling. This was made possible by innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning widely disparate temporal and spatial scales together with access to powerful new computational resources. In particular, the fusion energy science community has made excellent progress in developing advanced codes for which computer run-time and problem size scale well with the number of processors on massively parallel machines (MPP's). A good example is the effective usage of the full power of multi-teraflop MPP's to produce 3-dimensional, general geometry, nonlinear particle simulations which have accelerated progress in understanding the nature of turbulence self-regulation by zonal flows. It should be emphasized that these calculations, which typically utilized billions of particles for tens of thousands time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In general, results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. The associated scientific excitement should serve to stimulate improved cross-cutting collaborations with other fields and also to help attract bright young talent to plasma science.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2018JPhCS1006a2029W','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2018JPhCS1006a2029W"><span>Problem-based learning: effects on student’s scientific reasoning skills in science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Wulandari, F. E.; Shofiyah, N.</p> <p>2018-04-01</p> <p>This research aimed to develop instructional package of problem-based learning to enhance student’s scientific reasoning from concrete to formal reasoning skills level. The instructional package was developed using the Dick and Carey Model. Subject of this study was instructional package of problem-based learning which was consisting of lesson plan, handout, student’s worksheet, and scientific reasoning test. The instructional package was tried out on 4th semester science education students of Universitas Muhammadiyah Sidoarjo by using the one-group pre-test post-test design. The data of scientific reasoning skills was collected by making use of the test. The findings showed that the developed instructional package reflecting problem-based learning was feasible to be implemented in classroom. Furthermore, through applying the problem-based learning, students could dominate formal scientific reasoning skills in terms of functionality and proportional reasoning, control variables, and theoretical reasoning.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020052594','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020052594"><span>Design of Unstructured Adaptive (UA) NAS Parallel Benchmark Featuring Irregular, Dynamic Memory Accesses</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Feng, Hui-Yu; VanderWijngaart, Rob; Biswas, Rupak; Biegel, Bryan (Technical Monitor)</p> <p>2001-01-01</p> <p>We describe the design of a new method for the measurement of the performance of modern computer systems when solving scientific problems featuring irregular, dynamic memory accesses. The method involves the solution of a stylized heat transfer problem on an unstructured, adaptive grid. A Spectral Element Method (SEM) with an adaptive, nonconforming mesh is selected to discretize the transport equation. The relatively high order of the SEM lowers the fraction of wall clock time spent on inter-processor communication, which eases the load balancing task and allows us to concentrate on the memory accesses. The benchmark is designed to be three-dimensional. Parallelization and load balance issues of a reference implementation will be described in detail in future reports.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20030012606','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20030012606"><span>Scientific Visualization & Modeling for Earth Systems Science Education</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Chaudhury, S. Raj; Rodriguez, Waldo J.</p> <p>2003-01-01</p> <p>Providing research experiences for undergraduate students in Earth Systems Science (ESS) poses several challenges at smaller academic institutions that might lack dedicated resources for this area of study. This paper describes the development of an innovative model that involves students with majors in diverse scientific disciplines in authentic ESS research. In studying global climate change, experts typically use scientific visualization techniques applied to remote sensing data collected by satellites. In particular, many problems related to environmental phenomena can be quantitatively addressed by investigations based on datasets related to the scientific endeavours such as the Earth Radiation Budget Experiment (ERBE). Working with data products stored at NASA's Distributed Active Archive Centers, visualization software specifically designed for students and an advanced, immersive Virtual Reality (VR) environment, students engage in guided research projects during a structured 6-week summer program. Over the 5-year span, this program has afforded the opportunity for students majoring in biology, chemistry, mathematics, computer science, physics, engineering and science education to work collaboratively in teams on research projects that emphasize the use of scientific visualization in studying the environment. Recently, a hands-on component has been added through science student partnerships with school-teachers in data collection and reporting for the GLOBE Program (GLobal Observations to Benefit the Environment).</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19960049754','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19960049754"><span>Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Biswas, Rupak; Oliker, Leonid; Sohn, Andrew</p> <p>1996-01-01</p> <p>Dynamic mesh adaption on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load imbalance among processors on a parallel machine. This paper describes the parallel implementation of a tetrahedral mesh adaption scheme and a new global load balancing method. A heuristic remapping algorithm is presented that assigns partitions to processors such that the redistribution cost is minimized. Results indicate that the parallel performance of the mesh adaption code depends on the nature of the adaption region and show a 35.5X speedup on 64 processors of an SP2 when 35% of the mesh is randomly adapted. For large-scale scientific computations, our load balancing strategy gives almost a sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remapper yields processor assignments that are less than 3% off the optimal solutions but requires only 1% of the computational time.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/29650670','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/29650670"><span>A blueprint for demonstrating quantum supremacy with superconducting qubits.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Neill, C; Roushan, P; Kechedzhi, K; Boixo, S; Isakov, S V; Smelyanskiy, V; Megrant, A; Chiaro, B; Dunsworth, A; Arya, K; Barends, R; Burkett, B; Chen, Y; Chen, Z; Fowler, A; Foxen, B; Giustina, M; Graff, R; Jeffrey, E; Huang, T; Kelly, J; Klimov, P; Lucero, E; Mutus, J; Neeley, M; Quintana, C; Sank, D; Vainsencher, A; Wenner, J; White, T C; Neven, H; Martinis, J M</p> <p>2018-04-13</p> <p>A key step toward demonstrating a quantum system that can address difficult problems in physics and chemistry will be performing a computation beyond the capabilities of any classical computer, thus achieving so-called quantum supremacy. In this study, we used nine superconducting qubits to demonstrate a promising path toward quantum supremacy. By individually tuning the qubit parameters, we were able to generate thousands of distinct Hamiltonian evolutions and probe the output probabilities. The measured probabilities obey a universal distribution, consistent with uniformly sampling the full Hilbert space. As the number of qubits increases, the system continues to explore the exponentially growing number of states. Extending these results to a system of 50 qubits has the potential to address scientific questions that are beyond the capabilities of any classical computer. Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/19556205','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/19556205"><span>Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Kelemen, Arpad; Vasilakos, Athanasios V; Liang, Yulan</p> <p>2009-09-01</p> <p>Comprehensive evaluation of common genetic variations through association of single-nucleotide polymorphism (SNP) structure with common complex disease in the genome-wide scale is currently a hot area in human genome research due to the recent development of the Human Genome Project and HapMap Project. Computational science, which includes computational intelligence (CI), has recently become the third method of scientific enquiry besides theory and experimentation. There have been fast growing interests in developing and applying CI in disease mapping using SNP and haplotype data. Some of the recent studies have demonstrated the promise and importance of CI for common complex diseases in genomic association study using SNP/haplotype data, especially for tackling challenges, such as gene-gene and gene-environment interactions, and the notorious "curse of dimensionality" problem. This review provides coverage of recent developments of CI approaches for complex diseases in genetic association study with SNP/haplotype data.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017E3SWC..2200030C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017E3SWC..2200030C"><span>Use of CFD modelling for analysing air parameters in auditorium halls</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Cichowicz, Robert</p> <p>2017-11-01</p> <p>Modelling with the use of numerical methods is currently the most popular method of solving scientific as well as engineering problems. Thanks to the use of computer methods it is possible for example to comprehensively describe the conditions in a given room and to determine thermal comfort, which is a complex issue including subjective sensations of the persons in a given room. The article presents the results of measurements and numerical computing that enabled carrying out the assessment of environment parameters, taking into consideration microclimate, temperature comfort, speeds in the zone of human presence and dustiness in auditory halls. For this purpose measurements of temperature, relative humidity and dustiness were made with the use of a digital microclimate meter and a laser dust particles counter. Thanks to the above by using the application DesignBuilder numerical computing was performed and the obtained results enabled determining PMV comfort indicator in selected rooms.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2008ASPC..389..211C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2008ASPC..389..211C"><span>Astronomy Village: Innovative Uses of Planetary Astronomy Images and Data</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Croft, S. K.; Pompea, S. M.</p> <p>2008-06-01</p> <p>Teaching and learning science is best done by hands-on experience with real scientific data and real scientific problems. Getting such experiences into public and home-schooling classrooms is a challenge. Here we describe two award-winning multimedia products that embody one successful solution to the problem: Astronomy Village: Investigating the Universe, and Astronomy Village: Investigating the Solar System. Each Village provides a virtual environment for inquiry-based scientific exploration of ten planetary and astronomical problems such as ``Mission to Pluto'' and ``Search for a Supernova.'' Both Villages are standards-based and classroom tested. Investigating the Solar System is designed for middle and early high school students, while Investigating the Universe is at the high school and introductory college level. The objective of both Villages is to engage students in scientific inquiry by having them acquire, explore, and analyze real scientific data and images drawn from real scientific problems.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_20");'>20</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li class="active"><span>22</span></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_22 --> <div id="page_23" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="441"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016APS..DPPPO7011V','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016APS..DPPPO7011V"><span>A Computational Framework for Efficient Low Temperature Plasma Simulations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Verma, Abhishek Kumar; Venkattraman, Ayyaswamy</p> <p>2016-10-01</p> <p>Over the past years, scientific computing has emerged as an essential tool for the investigation and prediction of low temperature plasmas (LTP) applications which includes electronics, nanomaterial synthesis, metamaterials etc. To further explore the LTP behavior with greater fidelity, we present a computational toolbox developed to perform LTP simulations. This framework will allow us to enhance our understanding of multiscale plasma phenomenon using high performance computing tools mainly based on OpenFOAM FVM distribution. Although aimed at microplasma simulations, the modular framework is able to perform multiscale, multiphysics simulations of physical systems comprises of LTP. Some salient introductory features are capability to perform parallel, 3D simulations of LTP applications on unstructured meshes. Performance of the solver is tested based on numerical results assessing accuracy and efficiency of benchmarks for problems in microdischarge devices. Numerical simulation of microplasma reactor at atmospheric pressure with hemispherical dielectric coated electrodes will be discussed and hence, provide an overview of applicability and future scope of this framework.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000APS..DFD.JM001C','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000APS..DFD.JM001C"><span>Computational analysis of drop formation before and after the first singularity: the fate of free and satellite drops during simple dripping and DOD drop formation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Chen, Alvin U.; Basaran, Osman A.</p> <p>2000-11-01</p> <p>Drop formation from a capillary --- dripping mode --- or an ink jet nozzle --- drop-on-demand (DOD) mode --- falls into a class of scientifically challenging yet practically useful free surface flows that exhibit a finite time singularity, i.e. the breakup of an initially single liquid mass into two or more fragments. While computational tools to model such problems have been developed recently, they lack the accuracy needed to quantitatively predict all the dynamics observed in experiments. Here we present a new finite element method (FEM) based on a robust algorithm for elliptic mesh generation and remeshing to handle extremely large interface deformations. The new algorithm allows continuation of computations beyond the first singularity to track fates of both primary and any satellite drops. The accuracy of the computations is demonstrated by comparison of simulations with experimental measurements made possible with an ultra high-speed digital imager capable of recording 100 million frames per second.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2013AGUFM.T31C2525Q','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2013AGUFM.T31C2525Q"><span>Underworld results as a triple (shopping list, posterior, priors)</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Quenette, S. M.; Moresi, L. N.; Abramson, D.</p> <p>2013-12-01</p> <p>When studying long-term lithosphere deformation and other such large-scale, spatially distinct and behaviour rich problems, there is a natural trade-off between the meaning of a model, the observations used to validate the model and the ability to compute over this space. For example, many models of varying lithologies, rheological properties and underlying physics may reasonably match (or not match) observables. To compound this problem, each realisation is computationally intensive, requiring high resolution, algorithm tuning and code tuning to contemporary computer hardware. It is often intractable to use sampling based assimilation methods, but with better optimisation, the window of tractability becomes wider. The ultimate goal is to find a sweet-spot where a formal assimilation method is used, and where a model affines to observations. Its natural to think of this as an inverse problem, in which the underlying physics may be fixed and the rheological properties and possibly the lithologies themselves are unknown. What happens when we push this approach and treat some portion of the underlying physics as an unknown? At its extreme this is an intractable problem. However, there is an analogy here with how we develop software for these scientific problems. What happens when we treat the changing part of a largely complete code as an unknown, where the changes are working towards this sweet-spot? When posed as a Bayesian inverse problem the result is a triple - the model changes, the real priors and the real posterior. Not only does this give meaning to the process by which a code changes, it forms a mathematical bridge from an inverse problem to compiler optimisations given such changes. As a stepping stone example we show a regional scale heat flow model with constraining observations, and the inverse process including increasingly complexity in the software. The implementation uses Underworld-GT (Underworld plus research extras to import geology and export geothermic measures, etc). Underworld uses StGermain an early (partial) implementation of the theories described here.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014ISPAr.XL2..151X','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014ISPAr.XL2..151X"><span>An automated and integrated framework for dust storm detection based on ogc web processing services</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Xiao, F.; Shea, G. Y. K.; Wong, M. S.; Campbell, J.</p> <p>2014-11-01</p> <p>Dust storms are known to have adverse effects on public health. Atmospheric dust loading is also one of the major uncertainties in global climatic modelling as it is known to have a significant impact on the radiation budget and atmospheric stability. The complexity of building scientific dust storm models is coupled with the scientific computation advancement, ongoing computing platform development, and the development of heterogeneous Earth Observation (EO) networks. It is a challenging task to develop an integrated and automated scheme for dust storm detection that combines Geo-Processing frameworks, scientific models and EO data together to enable the dust storm detection and tracking processes in a dynamic and timely manner. This study develops an automated and integrated framework for dust storm detection and tracking based on the Web Processing Services (WPS) initiated by Open Geospatial Consortium (OGC). The presented WPS framework consists of EO data retrieval components, dust storm detecting and tracking component, and service chain orchestration engine. The EO data processing component is implemented based on OPeNDAP standard. The dust storm detecting and tracking component combines three earth scientific models, which are SBDART model (for computing aerosol optical depth (AOT) of dust particles), WRF model (for simulating meteorological parameters) and HYSPLIT model (for simulating the dust storm transport processes). The service chain orchestration engine is implemented based on Business Process Execution Language for Web Service (BPEL4WS) using open-source software. The output results, including horizontal and vertical AOT distribution of dust particles as well as their transport paths, were represented using KML/XML and displayed in Google Earth. A serious dust storm, which occurred over East Asia from 26 to 28 Apr 2012, is used to test the applicability of the proposed WPS framework. Our aim here is to solve a specific instance of a complex EO data and scientific model integration problem by using a framework and scientific workflow approach together. The experimental result shows that this newly automated and integrated framework can be used to give advance near real-time warning of dust storms, for both environmental authorities and public. The methods presented in this paper might be also generalized to other types of Earth system models, leading to improved ease of use and flexibility.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20090040773','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20090040773"><span>Integrated Hardware and Software for No-Loss Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>James, Mark</p> <p>2007-01-01</p> <p>When an algorithm is distributed across multiple threads executing on many distinct processors, a loss of one of those threads or processors can potentially result in the total loss of all the incremental results up to that point. When implementation is massively hardware distributed, then the probability of a hardware failure during the course of a long execution is potentially high. Traditionally, this problem has been addressed by establishing checkpoints where the current state of some or part of the execution is saved. Then in the event of a failure, this state information can be used to recompute that point in the execution and resume the computation from that point. A serious problem arises when one distributes a problem across multiple threads and physical processors is that one increases the likelihood of the algorithm failing due to no fault of the scientist but as a result of hardware faults coupled with operating system problems. With good reason, scientists expect their computing tools to serve them and not the other way around. What is novel here is a unique combination of hardware and software that reformulates an application into monolithic structure that can be monitored in real-time and dynamically reconfigured in the event of a failure. This unique reformulation of hardware and software will provide advanced aeronautical technologies to meet the challenges of next-generation systems in aviation, for civilian and scientific purposes, in our atmosphere and in atmospheres of other worlds. In particular, with respect to NASA s manned flight to Mars, this technology addresses the critical requirements for improving safety and increasing reliability of manned spacecraft.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015JSEdT..24..696B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015JSEdT..24..696B"><span>Scientific Inquiry Self-Efficacy and Computer Game Self-Efficacy as Predictors and Outcomes of Middle School Boys' and Girls' Performance in a Science Assessment in a Virtual Environment</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Bergey, Bradley W.; Ketelhut, Diane Jass; Liang, Senfeng; Natarajan, Uma; Karakus, Melissa</p> <p>2015-10-01</p> <p>The primary aim of the study was to examine whether performance on a science assessment in an immersive virtual environment was associated with changes in scientific inquiry self-efficacy. A secondary aim of the study was to examine whether performance on the science assessment was equitable for students with different levels of computer game self-efficacy, including whether gender differences were observed. We examined 407 middle school students' scientific inquiry self-efficacy and computer game self-efficacy before and after completing a computer game-like assessment about a science mystery. Results from path analyses indicated that prior scientific inquiry self-efficacy predicted achievement on end-of-module questions, which in turn predicted change in scientific inquiry self-efficacy. By contrast, computer game self-efficacy was neither predictive of nor predicted by performance on the science assessment. While boys had higher computer game self-efficacy compared to girls, multi-group analyses suggested only minor gender differences in how efficacy beliefs related to performance. Implications for assessments with virtual environments and future design and research are discussed.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/752226','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/752226"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hules, John</p> <p></p> <p>This 1998 annual report from the National Scientific Energy Research Computing Center (NERSC) presents the year in review of the following categories: Computational Science; Computer Science and Applied Mathematics; and Systems and Services. Also presented are science highlights in the following categories: Basic Energy Sciences; Biological and Environmental Research; Fusion Energy Sciences; High Energy and Nuclear Physics; and Advanced Scientific Computing Research and Other Projects.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=kodak&pg=4&id=ED190057','ERIC'); return false;" href="https://eric.ed.gov/?q=kodak&pg=4&id=ED190057"><span>The Versatile Terminal.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Evans, C. D.</p> <p></p> <p>This paper describes the experiences of the industrial research laboratory of Kodak Ltd. in finding and providing a computer terminal most suited to its very varied requirements. These requirements include bibliographic and scientific data searching and access to a number of worldwide computing services for scientific computing work. The provision…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2015IAUGA..2255422F','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2015IAUGA..2255422F"><span>Accelerating Astronomy & Astrophysics in the New Era of Parallel Computing: GPUs, Phi and Cloud Computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ford, Eric B.; Dindar, Saleh; Peters, Jorg</p> <p>2015-08-01</p> <p>The realism of astrophysical simulations and statistical analyses of astronomical data are set by the available computational resources. Thus, astronomers and astrophysicists are constantly pushing the limits of computational capabilities. For decades, astronomers benefited from massive improvements in computational power that were driven primarily by increasing clock speeds and required relatively little attention to details of the computational hardware. For nearly a decade, increases in computational capabilities have come primarily from increasing the degree of parallelism, rather than increasing clock speeds. Further increases in computational capabilities will likely be led by many-core architectures such as Graphical Processing Units (GPUs) and Intel Xeon Phi. Successfully harnessing these new architectures, requires significantly more understanding of the hardware architecture, cache hierarchy, compiler capabilities and network network characteristics.I will provide an astronomer's overview of the opportunities and challenges provided by modern many-core architectures and elastic cloud computing. The primary goal is to help an astronomical audience understand what types of problems are likely to yield more than order of magnitude speed-ups and which problems are unlikely to parallelize sufficiently efficiently to be worth the development time and/or costs.I will draw on my experience leading a team in developing the Swarm-NG library for parallel integration of large ensembles of small n-body systems on GPUs, as well as several smaller software projects. I will share lessons learned from collaborating with computer scientists, including both technical and soft skills. Finally, I will discuss the challenges of training the next generation of astronomers to be proficient in this new era of high-performance computing, drawing on experience teaching a graduate class on High-Performance Scientific Computing for Astrophysics and organizing a 2014 advanced summer school on Bayesian Computing for Astronomical Data Analysis with support of the Penn State Center for Astrostatistics and Institute for CyberScience.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/1243296-amplify-scientific-discovery-artificial-intelligence','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/1243296-amplify-scientific-discovery-artificial-intelligence"><span>Amplify scientific discovery with artificial intelligence</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Gil, Yolanda; Greaves, Mark T.; Hendler, James</p> <p></p> <p>Computing innovations have fundamentally changed many aspects of scientific inquiry. For example, advances in robotics, high-end computing, networking, and databases now underlie much of what we do in science such as gene sequencing, general number crunching, sharing information between scientists, and analyzing large amounts of data. As computing has evolved at a rapid pace, so too has its impact in science, with the most recent computing innovations repeatedly being brought to bear to facilitate new forms of inquiry. Recently, advances in Artificial Intelligence (AI) have deeply penetrated many consumer sectors, including for example Apple’s Siri™ speech recognition system, real-time automatedmore » language translation services, and a new generation of self-driving cars and self-navigating drones. However, AI has yet to achieve comparable levels of penetration in scientific inquiry, despite its tremendous potential in aiding computers to help scientists tackle tasks that require scientific reasoning. We contend that advances in AI will transform the practice of science as we are increasingly able to effectively and jointly harness human and machine intelligence in the pursuit of major scientific challenges.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/505354','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/505354"><span>A distributed computing environment with support for constraint-based task scheduling and scientific experimentation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Ahrens, J.P.; Shapiro, L.G.; Tanimoto, S.L.</p> <p>1997-04-01</p> <p>This paper describes a computing environment which supports computer-based scientific research work. Key features include support for automatic distributed scheduling and execution and computer-based scientific experimentation. A new flexible and extensible scheduling technique that is responsive to a user`s scheduling constraints, such as the ordering of program results and the specification of task assignments and processor utilization levels, is presented. An easy-to-use constraint language for specifying scheduling constraints, based on the relational database query language SQL, is described along with a search-based algorithm for fulfilling these constraints. A set of performance studies show that the environment can schedule and executemore » program graphs on a network of workstations as the user requests. A method for automatically generating computer-based scientific experiments is described. Experiments provide a concise method of specifying a large collection of parameterized program executions. The environment achieved significant speedups when executing experiments; for a large collection of scientific experiments an average speedup of 3.4 on an average of 5.5 scheduled processors was obtained.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://ntrs.nasa.gov/search.jsp?R=20120008477&hterms=dataset&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddataset','NASA-TRS'); return false;" href="https://ntrs.nasa.gov/search.jsp?R=20120008477&hterms=dataset&qs=Ntx%3Dmode%2Bmatchall%26Ntk%3DAll%26N%3D0%26No%3D30%26Ntt%3Ddataset"><span>Provenance Challenges for Earth Science Dataset Publication</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tilmes, Curt</p> <p>2011-01-01</p> <p>Modern science is increasingly dependent on computational analysis of very large data sets. Organizing, referencing, publishing those data has become a complex problem. Published research that depends on such data often fails to cite the data in sufficient detail to allow an independent scientist to reproduce the original experiments and analyses. This paper explores some of the challenges related to data identification, equivalence and reproducibility in the domain of data intensive scientific processing. It will use the example of Earth Science satellite data, but the challenges also apply to other domains.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20020083264','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20020083264"><span>Mirror Measurement Device</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p></p> <p>1992-01-01</p> <p>A Small Business Innovation Research (SBIR) contract led to a commercially available instrument used to measure the shape profile of mirror surfaces in scientific instruments. Bauer Associates, Inc.'s Bauer Model 200 Profilometer is based upon a different measurement concept. The local curvature of the mirror's surface is measured at many points, and the collection of data is computer processed to yield the desired shape profile. (Earlier profilometers are based on the principle of interferometry.) The system is accurate and immune to problems like vibration and turbulence. Two profilometers are currently marketed, and a third will soon be commercialized.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1311528','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1311528"><span>Modeling Primary Atomization of Liquid Fuels using a Multiphase DNS/LES Approach</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Arienti, Marco; Oefelein, Joe; Doisneau, Francois</p> <p>2016-08-01</p> <p>As part of a Laboratory Directed Research and Development project, we are developing a modeling-and-simulation capability to study fuel direct injection in automotive engines. Predicting mixing and combustion at realistic conditions remains a challenging objective of energy science. And it is a research priority in Sandia’s mission-critical area of energy security, being also relevant to many flows in defense and climate. High-performance computing applied to this non-linear multi-scale problem is key to engine calculations with increased scientific reliability.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017EPSC...11..281G','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017EPSC...11..281G"><span>Lobachevsky Year at Kazan University: Center of Science, Education, Intellectual-Cognitive Tourism "Kazan - GeoNa - 2020+" and "Kazan-Moon-2020+" projects</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Gusev, A.; Trudkova, N.</p> <p>2017-09-01</p> <p>Center "GeoNa" will enable scientists and teachers of the Russian universities to join to advanced achievements of a science, information technologies; to establish scientific communications with foreign colleagues in sphere of the high technology, educational projects and Intellectual-Cognitive Tourism. The Project "Kazan - Moon - 2020+" is directed on the decision of fundamental problems of celestial mechanics, selenodesy and geophysics of the Moon(s) connected to carrying out of complex theoretical researches and computer modelling.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940007333','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940007333"><span>International disaster research</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Silverstein, Martin Elliot</p> <p>1991-01-01</p> <p>No existing telecommunications system can be expected to provide strategy and tactics appropriate to the complex, many faceted problem of disaster. Despite the exciting capabilities of space, communications, remote sensing, and the miracles of modern medicine, complete turnkey transfers to the disaster problem do not make the fit, and cannot be expected to do so. In 1980, a Presidential team assigned the mission of exploring disaster response within the U.S. Federal Government encountered an unanticipated obstacle: disaster was essentially undefined. In the absence of a scientifically based paradigm of disaster, there can be no measure of cost effectiveness, optimum design of manpower structure, or precise application of any technology. These problems spawned a 10-year, multidisciplinary study designed to define the origins, anatomy, and necessary management techniques for catastrophes. The design of the study necessarily reflects interests and expertise in disaster medicine, emergency medicine, telecommunications, computer communications, and forencsic sciences. This study is described.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Python&id=EJ1142013','ERIC'); return false;" href="https://eric.ed.gov/?q=Python&id=EJ1142013"><span>Scientific Computing for Chemists: An Undergraduate Course in Simulations, Data Processing, and Visualization</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Weiss, Charles J.</p> <p>2017-01-01</p> <p>The Scientific Computing for Chemists course taught at Wabash College teaches chemistry students to use the Python programming language, Jupyter notebooks, and a number of common Python scientific libraries to process, analyze, and visualize data. Assuming no prior programming experience, the course introduces students to basic programming and…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.ncbi.nlm.nih.gov/pubmed/22083841','PUBMED'); return false;" href="https://www.ncbi.nlm.nih.gov/pubmed/22083841"><span>Computational chemistry in pharmaceutical research: at the crossroads.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="https://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pubmed">PubMed</a></p> <p>Bajorath, Jürgen</p> <p>2012-01-01</p> <p>Computational approaches are an integral part of pharmaceutical research. However, there are many of unsolved key questions that limit the scientific progress in the still evolving computational field and its impact on drug discovery. Importantly, a number of these questions are not new but date back many years. Hence, it might be difficult to conclusively answer them in the foreseeable future. Moreover, the computational field as a whole is characterized by a high degree of heterogeneity and so is, unfortunately, the quality of its scientific output. In light of this situation, it is proposed that changes in scientific standards and culture should be seriously considered now in order to lay a foundation for future progress in computational research.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2014JPhCS.513f2039P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2014JPhCS.513f2039P"><span>Scholarly literature and the press: scientific impact and social perception of physics computing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Pia, M. G.; Basaglia, T.; Bell, Z. W.; Dressendorfer, P. V.</p> <p>2014-06-01</p> <p>The broad coverage of the search for the Higgs boson in the mainstream media is a relative novelty for high energy physics (HEP) research, whose achievements have traditionally been limited to scholarly literature. This paper illustrates the results of a scientometric analysis of HEP computing in scientific literature, institutional media and the press, and a comparative overview of similar metrics concerning representative particle physics measurements. The picture emerging from these scientometric data documents the relationship between the scientific impact and the social perception of HEP physics research versus that of HEP computing. The results of this analysis suggest that improved communication of the scientific and social role of HEP computing via press releases from the major HEP laboratories would be beneficial to the high energy physics community.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20110015282','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20110015282"><span>Software Reuse Methods to Improve Technological Infrastructure for e-Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Marshall, James J.; Downs, Robert R.; Mattmann, Chris A.</p> <p>2011-01-01</p> <p>Social computing has the potential to contribute to scientific research. Ongoing developments in information and communications technology improve capabilities for enabling scientific research, including research fostered by social computing capabilities. The recent emergence of e-Science practices has demonstrated the benefits from improvements in the technological infrastructure, or cyber-infrastructure, that has been developed to support science. Cloud computing is one example of this e-Science trend. Our own work in the area of software reuse offers methods that can be used to improve new technological development, including cloud computing capabilities, to support scientific research practices. In this paper, we focus on software reuse and its potential to contribute to the development and evaluation of information systems and related services designed to support new capabilities for conducting scientific research.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li class="active"><span>23</span></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_23 --> <div id="page_24" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="461"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1250537','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1250537"><span>Computer Modeling and Simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Pronskikh, V. S.</p> <p>2014-05-09</p> <p>Verification and validation of computer codes and models used in simulation are two aspects of the scientific practice of high importance and have recently been discussed by philosophers of science. While verification is predominantly associated with the correctness of the way a model is represented by a computer code or algorithm, validation more often refers to model’s relation to the real world and its intended use. It has been argued that because complex simulations are generally not transparent to a practitioner, the Duhem problem can arise for verification and validation due to their entanglement; such an entanglement makes it impossiblemore » to distinguish whether a coding error or model’s general inadequacy to its target should be blamed in the case of the model failure. I argue that in order to disentangle verification and validation, a clear distinction between computer modeling (construction of mathematical computer models of elementary processes) and simulation (construction of models of composite objects and processes by means of numerical experimenting with them) needs to be made. Holding on to that distinction, I propose to relate verification (based on theoretical strategies such as inferences) to modeling and validation, which shares the common epistemology with experimentation, to simulation. To explain reasons of their intermittent entanglement I propose a weberian ideal-typical model of modeling and simulation as roles in practice. I suggest an approach to alleviate the Duhem problem for verification and validation generally applicable in practice and based on differences in epistemic strategies and scopes« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2001APS..DPPUP1056E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2001APS..DPPUP1056E"><span>Gyrokinetic micro-turbulence simulations on the NERSC 16-way SMP IBM SP computer: experiences and performance results</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Ethier, Stephane; Lin, Zhihong</p> <p>2001-10-01</p> <p>Earlier this year, the National Energy Research Scientific Computing center (NERSC) took delivery of the second most powerful computer in the world. With its 2,528 processors running at a peak performance of 1.5 GFlops, this IBM SP machine has a theoretical performance of almost 3.8 TFlops. To efficiently harness such computing power in one single code is not an easy task and requires a good knowledge of the computer's architecture. Here we present the steps that we followed to improve our gyrokinetic micro-turbulence code GTC in order to take advantage of the new 16-way shared memory nodes of the NERSC IBM SP. Performance results are shown as well as details about the improved mixed-mode MPI-OpenMP model that we use. The enhancements to the code allowed us to tackle much bigger problem sizes, getting closer to our goal of simulating an ITER-size tokamak with both kinetic ions and electrons.(This work is supported by DOE Contract No. DE-AC02-76CH03073 (PPPL), and in part by the DOE Fusion SciDAC Project.)</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/465696','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/465696"><span>The Nimrod computational workbench: a case study in desktop metacomputing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Abramson, D.; Sosic, R.; Foster, I.</p> <p></p> <p>The coordinated use of geographically distributed computers, or metacomputing, can in principle provide more accessible and cost- effective supercomputing than conventional high-performance systems. However, we lack evidence that metacomputing systems can be made easily usable, or that there exist large numbers of applications able to exploit metacomputing resources. In this paper, we present work that addresses both these concerns. The basis for this work is a system called Nimrod that provides a desktop problem-solving environment for parametric experiments. We describe how Nimrod has been extended to support the scheduling of computational resources located in a wide-area environment, and report onmore » an experiment in which Nimrod was used to schedule a large parametric study across the Australian Internet. The experiment provided both new scientific results and insights into Nimrod capabilities. We relate the results of this experiment to lessons learned from the I-WAY distributed computing experiment, and draw conclusions as to how Nimrod and I-WAY- like computing environments should be developed to support desktop metacomputing.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2009sdmk.book...91P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2009sdmk.book...91P"><span>Concept Formation in Scientific Knowledge Discovery from a Constructivist View</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Wei; Gero, John S.</p> <p></p> <p>The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called “first-person” knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer’s first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010sdma.book...91P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010sdma.book...91P"><span>Concept Formation in Scientific Knowledge Discovery from a Constructivist View</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Peng, Wei; Gero, John S.</p> <p></p> <p>The central goal of scientific knowledge discovery is to learn cause-effect relationships among natural phenomena presented as variables and the consequences their interactions. Scientific knowledge is normally expressed as scientific taxonomies and qualitative and quantitative laws [1]. This type of knowledge represents intrinsic regularities of the observed phenomena that can be used to explain and predict behaviors of the phenomena. It is a generalization that is abstracted and externalized from a set of contexts and applicable to a broader scope. Scientific knowledge is a type of third-person knowledge, i.e., knowledge that independent of a specific enquirer. Artificial intelligence approaches, particularly data mining algorithms that are used to identify meaningful patterns from large data sets, are approaches that aim to facilitate the knowledge discovery process [2]. A broad spectrum of algorithms has been developed in addressing classification, associative learning, and clustering problems. However, their linkages to people who use them have not been adequately explored. Issues in relation to supporting the interpretation of the patterns, the application of prior knowledge to the data mining process and addressing user interactions remain challenges for building knowledge discovery tools [3]. As a consequence, scientists rely on their experience to formulate problems, evaluate hypotheses, reason about untraceable factors and derive new problems. This type of knowledge which they have developed during their career is called "first-person" knowledge. The formation of scientific knowledge (third-person knowledge) is highly influenced by the enquirer's first-person knowledge construct, which is a result of his or her interactions with the environment. There have been attempts to craft automatic knowledge discovery tools but these systems are limited in their capabilities to handle the dynamics of personal experience. There are now trends in developing approaches to assist scientists applying their expertise to model formation, simulation, and prediction in various domains [4], [5]. On the other hand, first-person knowledge becomes third-person theory only if it proves general by evidence and is acknowledged by a scientific community. Researchers start to focus on building interactive cooperation platforms [1] to accommodate different views into the knowledge discovery process. There are some fundamental questions in relation to scientific knowledge development. What aremajor components for knowledge construction and how do people construct their knowledge? How is this personal construct assimilated and accommodated into a scientific paradigm? How can one design a computational system to facilitate these processes? This chapter does not attempt to answer all these questions but serves as a basis to foster thinking along this line. A brief literature review about how people develop their knowledge is carried out through a constructivist view. A hydrological modeling scenario is presented to elucidate the approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011AGUFMIN41A1388P','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011AGUFMIN41A1388P"><span>Software engineering and automatic continuous verification of scientific software</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Piggott, M. D.; Hill, J.; Farrell, P. E.; Kramer, S. C.; Wilson, C. R.; Ham, D.; Gorman, G. J.; Bond, T.</p> <p>2011-12-01</p> <p>Software engineering of scientific code is challenging for a number of reasons including pressure to publish and a lack of awareness of the pitfalls of software engineering by scientists. The Applied Modelling and Computation Group at Imperial College is a diverse group of researchers that employ best practice software engineering methods whilst developing open source scientific software. Our main code is Fluidity - a multi-purpose computational fluid dynamics (CFD) code that can be used for a wide range of scientific applications from earth-scale mantle convection, through basin-scale ocean dynamics, to laboratory-scale classic CFD problems, and is coupled to a number of other codes including nuclear radiation and solid modelling. Our software development infrastructure consists of a number of free tools that could be employed by any group that develops scientific code and has been developed over a number of years with many lessons learnt. A single code base is developed by over 30 people for which we use bazaar for revision control, making good use of the strong branching and merging capabilities. Using features of Canonical's Launchpad platform, such as code review, blueprints for designing features and bug reporting gives the group, partners and other Fluidity uers an easy-to-use platform to collaborate and allows the induction of new members of the group into an environment where software development forms a central part of their work. The code repositoriy are coupled to an automated test and verification system which performs over 20,000 tests, including unit tests, short regression tests, code verification and large parallel tests. Included in these tests are build tests on HPC systems, including local and UK National HPC services. The testing of code in this manner leads to a continuous verification process; not a discrete event performed once development has ceased. Much of the code verification is done via the "gold standard" of comparisons to analytical solutions via the method of manufactured solutions. By developing and verifying code in tandem we avoid a number of pitfalls in scientific software development and advocate similar procedures for other scientific code applications.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2010hocc.book..475M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2010hocc.book..475M"><span>Multidimensional Environmental Data Resource Brokering on Computational Grids and Scientific Clouds</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Montella, Raffaele; Giunta, Giulio; Laccetti, Giuliano</p> <p></p> <p>Grid computing has widely evolved over the past years, and its capabilities have found their way even into business products and are no longer relegated to scientific applications. Today, grid computing technology is not restricted to a set of specific grid open source or industrial products, but rather it is comprised of a set of capabilities virtually within any kind of software to create shared and highly collaborative production environments. These environments are focused on computational (workload) capabilities and the integration of information (data) into those computational capabilities. An active grid computing application field is the fully virtualization of scientific instruments in order to increase their availability and decrease operational and maintaining costs. Computational and information grids allow to manage real-world objects in a service-oriented way using industrial world-spread standards.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.gpo.gov/fdsys/pkg/FR-2013-01-29/pdf/2013-01838.pdf','FEDREG'); return false;" href="https://www.gpo.gov/fdsys/pkg/FR-2013-01-29/pdf/2013-01838.pdf"><span>78 FR 6087 - Advanced Scientific Computing Advisory Committee</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.gpo.gov/fdsys/browse/collection.action?collectionCode=FR">Federal Register 2010, 2011, 2012, 2013, 2014</a></p> <p></p> <p>2013-01-29</p> <p>... INFORMATION CONTACT: Melea Baker, Office of Advanced Scientific Computing Research; SC-21/Germantown Building... Theory and Experiment (INCITE) Public Comment (10-minute rule) Public Participation: The meeting is open...</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2952966','PMC'); return false;" href="https://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2952966"><span>Efficient Controls for Finitely Convergent Sequential Algorithms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?DB=pmc">PubMed Central</a></p> <p>Chen, Wei; Herman, Gabor T.</p> <p>2010-01-01</p> <p>Finding a feasible point that satisfies a set of constraints is a common task in scientific computing: examples are the linear feasibility problem and the convex feasibility problem. Finitely convergent sequential algorithms can be used for solving such problems; an example of such an algorithm is ART3, which is defined in such a way that its control is cyclic in the sense that during its execution it repeatedly cycles through the given constraints. Previously we found a variant of ART3 whose control is no longer cyclic, but which is still finitely convergent and in practice it usually converges faster than ART3 does. In this paper we propose a general methodology for automatic transformation of finitely convergent sequential algorithms in such a way that (i) finite convergence is retained and (ii) the speed of convergence is improved. The first of these two properties is proven by mathematical theorems, the second is illustrated by applying the algorithms to a practical problem. PMID:20953327</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1236181','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1236181"><span>ASCR Cybersecurity for Scientific Computing Integrity - Research Pathways and Ideas Workshop</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Peisert, Sean; Potok, Thomas E.; Jones, Todd</p> <p></p> <p>At the request of the U.S. Department of Energy's (DOE) Office of Science (SC) Advanced Scientific Computing Research (ASCR) program office, a workshop was held June 2-3, 2015, in Gaithersburg, MD, to identify potential long term (10 to +20 year) cybersecurity fundamental basic research and development challenges, strategies and roadmap facing future high performance computing (HPC), networks, data centers, and extreme-scale scientific user facilities. This workshop was a follow-on to the workshop held January 7-9, 2015, in Rockville, MD, that examined higher level ideas about scientific computing integrity specific to the mission of the DOE Office of Science. Issues includedmore » research computation and simulation that takes place on ASCR computing facilities and networks, as well as network-connected scientific instruments, such as those run by various DOE Office of Science programs. Workshop participants included researchers and operational staff from DOE national laboratories, as well as academic researchers and industry experts. Participants were selected based on the submission of abstracts relating to the topics discussed in the previous workshop report [1] and also from other ASCR reports, including "Abstract Machine Models and Proxy Architectures for Exascale Computing" [27], the DOE "Preliminary Conceptual Design for an Exascale Computing Initiative" [28], and the January 2015 machine learning workshop [29]. The workshop was also attended by several observers from DOE and other government agencies. The workshop was divided into three topic areas: (1) Trustworthy Supercomputing, (2) Extreme-Scale Data, Knowledge, and Analytics for Understanding and Improving Cybersecurity, and (3) Trust within High-end Networking and Data Centers. Participants were divided into three corresponding teams based on the category of their abstracts. The workshop began with a series of talks from the program manager and workshop chair, followed by the leaders for each of the three topics and a representative of each of the four major DOE Office of Science Advanced Scientific Computing Research Facilities: the Argonne Leadership Computing Facility (ALCF), the Energy Sciences Network (ESnet), the National Energy Research Scientific Computing Center (NERSC), and the Oak Ridge Leadership Computing Facility (OLCF). The rest of the workshop consisted of topical breakout discussions and focused writing periods that produced much of this report.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=RNA+AND+RNA&pg=6&id=EJ1087759','ERIC'); return false;" href="https://eric.ed.gov/?q=RNA+AND+RNA&pg=6&id=EJ1087759"><span>Building Cognition: The Construction of Computational Representations for Scientific Discovery</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Chandrasekharan, Sanjay; Nersessian, Nancy J.</p> <p>2015-01-01</p> <p>Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1222717','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1222717"><span>DOE Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee Report on Scientific and Technical Information</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Hey, Tony; Agarwal, Deborah; Borgman, Christine</p> <p></p> <p>The Advanced Scientific Computing Advisory Committee (ASCAC) was charged to form a standing subcommittee to review the Department of Energy’s Office of Scientific and Technical Information (OSTI) and to begin by assessing the quality and effectiveness of OSTI’s recent and current products and services and to comment on its mission and future directions in the rapidly changing environment for scientific publication and data. The Committee met with OSTI staff and reviewed available products, services and other materials. This report summaries their initial findings and recommendations.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1209196','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1209196"><span></span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Perumalla, Kalyan S.; Yoginath, Srikanth B.</p> <p></p> <p>Problems such as fault tolerance and scalable synchronization can be efficiently solved using reversibility of applications. Making applications reversible by relying on computation rather than on memory is ideal for large scale parallel computing, especially for the next generation of supercomputers in which memory is expensive in terms of latency, energy, and price. In this direction, a case study is presented here in reversing a computational core, namely, Basic Linear Algebra Subprograms, which is widely used in scientific applications. A new Reversible BLAS (RBLAS) library interface has been designed, and a prototype has been implemented with two modes: (1) amore » memory-mode in which reversibility is obtained by checkpointing to memory in forward and restoring from memory in reverse, and (2) a computational-mode in which nothing is saved in the forward, but restoration is done entirely via inverse computation in reverse. The article is focused on detailed performance benchmarking to evaluate the runtime dynamics and performance effects, comparing reversible computation with checkpointing on both traditional CPU platforms and recent GPU accelerator platforms. For BLAS Level-1 subprograms, data indicates over an order of magnitude better speed of reversible computation compared to checkpointing. For BLAS Level-2 and Level-3, a more complex tradeoff is observed between reversible computation and checkpointing, depending on computational and memory complexities of the subprograms.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19940011121','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19940011121"><span>Computer networking at SLR stations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Novotny, Antonin</p> <p>1993-01-01</p> <p>There are several existing communication methods to deliver data from the satellite laser ranging (SLR) station to the SLR data center and back: telephonmodem, telex, and computer networks. The SLR scientific community has been exploiting mainly INTERNET, BITNET/EARN, and SPAN. The total of 56 countries are connected to INTERNET and the number of nodes is exponentially growing. The computer networks mentioned above and others are connected through E-mail protocol. The scientific progress of SLR requires the increase of communication speed and the amount of the transmitted data. The TOPEX/POSEIDON test campaign required to deliver Quick Look data (1.7 kB/pass) from a SLR site to SLR data center within 8 hours and full rate data (up to 500 kB/pass) within 24 hours. We developed networking for the remote SLR station in Helwan, Egypt. The reliable scheme for data delivery consists of: compression of MERIT2 format (up to 89 percent), encoding to ASCII Me (files); and e-mail sending from SLR station--e-mail receiving, decoding, and decompression at the center. We do propose to use the ZIP method for compression/decompression and the UUCODE method for ASCII encoding/decoding. This method will be useful for stations connected via telephonemodems or commercial networks. The electronics delivery could solve the problem of the too late receiving of the FR data by SLR data center.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1993lri..work.....N','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1993lri..work.....N"><span>Computer networking at SLR stations</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Novotny, Antonin</p> <p>1993-06-01</p> <p>There are several existing communication methods to deliver data from the satellite laser ranging (SLR) station to the SLR data center and back: telephonmodem, telex, and computer networks. The SLR scientific community has been exploiting mainly INTERNET, BITNET/EARN, and SPAN. The total of 56 countries are connected to INTERNET and the number of nodes is exponentially growing. The computer networks mentioned above and others are connected through E-mail protocol. The scientific progress of SLR requires the increase of communication speed and the amount of the transmitted data. The TOPEX/POSEIDON test campaign required to deliver Quick Look data (1.7 kB/pass) from a SLR site to SLR data center within 8 hours and full rate data (up to 500 kB/pass) within 24 hours. We developed networking for the remote SLR station in Helwan, Egypt. The reliable scheme for data delivery consists of: compression of MERIT2 format (up to 89 percent), encoding to ASCII Me (files); and e-mail sending from SLR station--e-mail receiving, decoding, and decompression at the center. We do propose to use the ZIP method for compression/decompression and the UUCODE method for ASCII encoding/decoding. This method will be useful for stations connected via telephonemodems or commercial networks. The electronics delivery could solve the problem of the too late receiving of the FR data by SLR data center.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/20040081296','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/20040081296"><span>A Survey of Collectives</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Tumer, Kagan; Wolpert, David</p> <p>2004-01-01</p> <p>Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a well-defined set of system-level performance criteria, are called collectives. The fundamental problem in analyzing/designing such systems is in determining how the combined actions of self-interested agents leads to 'coordinated' behavior on a iarge scale. Examples of artificial systems which exhibit such behavior include packet routing across a data network, control of an array of communication satellites, coordination of multiple deployables, and dynamic job scheduling across a distributed computer grid. Examples of natural systems include ecosystems, economies, and the organelles within a living cell. No current scientific discipline provides a thorough understanding of the relation between the structure of collectives and how well they meet their overall performance criteria. Although still very young, research on collectives has resulted in successes both in understanding and designing such systems. It is eqected that as it matures and draws upon other disciplines related to collectives, this field will greatly expand the range of computationally addressable tasks. Moreover, in addition to drawing on them, such a fully developed field of collective intelligence may provide insight into already established scientific fields, such as mechanism design, economics, game theory, and population biology. This chapter provides a survey to the emerging science of collectives.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011JHyd..403..186L','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011JHyd..403..186L"><span>Grid computing technology for hydrological applications</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Lecca, G.; Petitdidier, M.; Hluchy, L.; Ivanovic, M.; Kussul, N.; Ray, N.; Thieron, V.</p> <p>2011-06-01</p> <p>SummaryAdvances in e-Infrastructure promise to revolutionize sensing systems and the way in which data are collected and assimilated, and complex water systems are simulated and visualized. According to the EU Infrastructure 2010 work-programme, data and compute infrastructures and their underlying technologies, either oriented to tackle scientific challenges or complex problem solving in engineering, are expected to converge together into the so-called knowledge infrastructures, leading to a more effective research, education and innovation in the next decade and beyond. Grid technology is recognized as a fundamental component of e-Infrastructures. Nevertheless, this emerging paradigm highlights several topics, including data management, algorithm optimization, security, performance (speed, throughput, bandwidth, etc.), and scientific cooperation and collaboration issues that require further examination to fully exploit it and to better inform future research policies. The paper illustrates the results of six different surface and subsurface hydrology applications that have been deployed on the Grid. All the applications aim to answer to strong requirements from the Civil Society at large, relatively to natural and anthropogenic risks. Grid technology has been successfully tested to improve flood prediction, groundwater resources management and Black Sea hydrological survey, by providing large computing resources. It is also shown that Grid technology facilitates e-cooperation among partners by means of services for authentication and authorization, seamless access to distributed data sources, data protection and access right, and standardization.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2011ExA....31..243O','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2011ExA....31..243O"><span>Using Java for distributed computing in the Gaia satellite data processing</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>O'Mullane, William; Luri, Xavier; Parsons, Paul; Lammers, Uwe; Hoar, John; Hernandez, Jose</p> <p>2011-10-01</p> <p>In recent years Java has matured to a stable easy-to-use language with the flexibility of an interpreter (for reflection etc.) but the performance and type checking of a compiled language. When we started using Java for astronomical applications around 1999 they were the first of their kind in astronomy. Now a great deal of astronomy software is written in Java as are many business applications. We discuss the current environment and trends concerning the language and present an actual example of scientific use of Java for high-performance distributed computing: ESA's mission Gaia. The Gaia scanning satellite will perform a galactic census of about 1,000 million objects in our galaxy. The Gaia community has chosen to write its processing software in Java. We explore the manifold reasons for choosing Java for this large science collaboration. Gaia processing is numerically complex but highly distributable, some parts being embarrassingly parallel. We describe the Gaia processing architecture and its realisation in Java. We delve into the astrometric solution which is the most advanced and most complex part of the processing. The Gaia simulator is also written in Java and is the most mature code in the system. This has been successfully running since about 2005 on the supercomputer "Marenostrum" in Barcelona. We relate experiences of using Java on a large shared machine. Finally we discuss Java, including some of its problems, for scientific computing.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012AGUFMIN11B1463T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012AGUFMIN11B1463T"><span>Advancing Cyberinfrastructure to support high resolution water resources modeling</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tarboton, D. G.; Ogden, F. L.; Jones, N.; Horsburgh, J. S.</p> <p>2012-12-01</p> <p>Addressing the problem of how the availability and quality of water resources at large scales are sensitive to climate variability, watershed alterations and management activities requires computational resources that combine data from multiple sources and support integrated modeling. Related cyberinfrastructure challenges include: 1) how can we best structure data and computer models to address this scientific problem through the use of high-performance and data-intensive computing, and 2) how can we do this in a way that discipline scientists without extensive computational and algorithmic knowledge and experience can take advantage of advances in cyberinfrastructure? This presentation will describe a new system called CI-WATER that is being developed to address these challenges and advance high resolution water resources modeling in the Western U.S. We are building on existing tools that enable collaboration to develop model and data interfaces that link integrated system models running within an HPC environment to multiple data sources. Our goal is to enhance the use of computational simulation and data-intensive modeling to better understand water resources. Addressing water resource problems in the Western U.S. requires simulation of natural and engineered systems, as well as representation of legal (water rights) and institutional constraints alongside the representation of physical processes. We are establishing data services to represent the engineered infrastructure and legal and institutional systems in a way that they can be used with high resolution multi-physics watershed modeling at high spatial resolution. These services will enable incorporation of location-specific information on water management infrastructure and systems into the assessment of regional water availability in the face of growing demands, uncertain future meteorological forcings, and existing prior-appropriations water rights. This presentation will discuss the informatics challenges involved with data management and easy-to-use access to high performance computing being tackled in this project.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=computational&pg=7&id=EJ1122099','ERIC'); return false;" href="https://eric.ed.gov/?q=computational&pg=7&id=EJ1122099"><span>Computational Scientific Inquiry with Virtual Worlds and Agent-Based Models: New Ways of Doing Science to Learn Science</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Jacobson, Michael J.; Taylor, Charlotte E.; Richards, Deborah</p> <p>2016-01-01</p> <p>In this paper, we propose computational scientific inquiry (CSI) as an innovative model for learning important scientific knowledge and new practices for "doing" science. This approach involves the use of a "game-like" virtual world for students to experience virtual biological fieldwork in conjunction with using an agent-based…</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li class="active"><span>24</span></li> <li><a href="#" onclick='return showDiv("page_25");'>25</a></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_24 --> <div id="page_25" class="hiddenDiv"> <div class="row"> <div class="col-sm-12"> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div> </div> <div class="row"> <div class="col-sm-12"> <ol class="result-class" start="481"> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=hulshof&id=EJ739458','ERIC'); return false;" href="https://eric.ed.gov/?q=hulshof&id=EJ739458"><span>Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hulshof, Casper D.; de Jong, Ton</p> <p>2006-01-01</p> <p>Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016EPJWC.10802033K','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016EPJWC.10802033K"><span>The JINR Tier1 Site Simulation for Research and Development Purposes</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Korenkov, V.; Nechaevskiy, A.; Ososkov, G.; Pryahina, D.; Trofimov, V.; Uzhinskiy, A.; Voytishin, N.</p> <p>2016-02-01</p> <p>Distributed complex computing systems for data storage and processing are in common use in the majority of modern scientific centers. The design of such systems is usually based on recommendations obtained via a preliminary simulated model used and executed only once. However big experiments last for years and decades, and the development of their computing system is going on, not only quantitatively but also qualitatively. Even with the substantial efforts invested in the design phase to understand the systems configuration, it would be hard enough to develop a system without additional research of its future evolution. The developers and operators face the problem of the system behaviour predicting after the planned modifications. A system for grid and cloud services simulation is developed at LIT (JINR, Dubna). This simulation system is focused on improving the effciency of the grid/cloud structures development by using the work quality indicators of some real system. The development of such kind of software is very important for making a new grid/cloud infrastructure for such big scientific experiments like the JINR Tier1 site for WLCG. The simulation of some processes of the Tier1 site is considered as an example of our application approach.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/22570201-explicit-integration-gpu-acceleration-large-kinetic-networks','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/22570201-explicit-integration-gpu-acceleration-large-kinetic-networks"><span>Explicit integration with GPU acceleration for large kinetic networks</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Brock, Benjamin; Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830; Belt, Andrew</p> <p>2015-12-01</p> <p>We demonstrate the first implementation of recently-developed fast explicit kinetic integration algorithms on modern graphics processing unit (GPU) accelerators. Taking as a generic test case a Type Ia supernova explosion with an extremely stiff thermonuclear network having 150 isotopic species and 1604 reactions coupled to hydrodynamics using operator splitting, we demonstrate the capability to solve of order 100 realistic kinetic networks in parallel in the same time that standard implicit methods can solve a single such network on a CPU. This orders-of-magnitude decrease in computation time for solving systems of realistic kinetic networks implies that important coupled, multiphysics problems inmore » various scientific and technical fields that were intractable, or could be simulated only with highly schematic kinetic networks, are now computationally feasible.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1163896','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1163896"><span>Final Report for DOE Award ER25756</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Kesselman, Carl</p> <p>2014-11-17</p> <p>The SciDAC-funded Center for Enabling Distributed Petascale Science (CEDPS) was established to address technical challenges that arise due to the frequent geographic distribution of data producers (in particular, supercomputers and scientific instruments) and data consumers (people and computers) within the DOE laboratory system. Its goal is to produce technical innovations that meet DOE end-user needs for (a) rapid and dependable placement of large quantities of data within a distributed high-performance environment, and (b) the convenient construction of scalable science services that provide for the reliable and high-performance processing of computation and data analysis requests from many remote clients. The Centermore » is also addressing (c) the important problem of troubleshooting these and other related ultra-high-performance distributed activities from the perspective of both performance and functionality« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2006PhDT.......316S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2006PhDT.......316S"><span>Unsteady flow simulations around complex geometries using stationary or rotating unstructured grids</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Sezer-Uzol, Nilay</p> <p></p> <p>In this research, the computational analysis of three-dimensional, unsteady, separated, vortical flows around complex geometries is studied by using stationary or moving unstructured grids. Two main engineering problems are investigated. The first problem is the unsteady simulation of a ship airwake, where helicopter operations become even more challenging, by using stationary unstructured grids. The second problem is the unsteady simulation of wind turbine rotor flow fields by using moving unstructured grids which are rotating with the whole three-dimensional rigid rotor geometry. The three dimensional, unsteady, parallel, unstructured, finite volume flow solver, PUMA2, is used for the computational fluid dynamics (CFD) simulations considered in this research. The code is modified to have a moving grid capability to perform three-dimensional, time-dependent rotor simulations. An instantaneous log-law wall model for Large Eddy Simulations is also implemented in PUMA2 to investigate the very large Reynolds number flow fields of rotating blades. To verify the code modifications, several sample test cases are also considered. In addition, interdisciplinary studies, which are aiming to provide new tools and insights to the aerospace and wind energy scientific communities, are done during this research by focusing on the coupling of ship airwake CFD simulations with the helicopter flight dynamics and control analysis, the coupling of wind turbine rotor CFD simulations with the aeroacoustic analysis, and the analysis of these time-dependent and large-scale CFD simulations with the help of a computational monitoring, steering and visualization tool, POSSE.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2012HCSR....7....1B','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2012HCSR....7....1B"><span>New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Brescia, Massimo</p> <p>2012-11-01</p> <p>Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19910001527','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19910001527"><span>Computational Fluid Dynamics Program at NASA Ames Research Center</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Holst, Terry L.</p> <p>1989-01-01</p> <p>The Computational Fluid Dynamics (CFD) Program at NASA Ames Research Center is reviewed and discussed. The technical elements of the CFD Program are listed and briefly discussed. These elements include algorithm research, research and pilot code development, scientific visualization, advanced surface representation, volume grid generation, and numerical optimization. Next, the discipline of CFD is briefly discussed and related to other areas of research at NASA Ames including experimental fluid dynamics, computer science research, computational chemistry, and numerical aerodynamic simulation. These areas combine with CFD to form a larger area of research, which might collectively be called computational technology. The ultimate goal of computational technology research at NASA Ames is to increase the physical understanding of the world in which we live, solve problems of national importance, and increase the technical capabilities of the aerospace community. Next, the major programs at NASA Ames that either use CFD technology or perform research in CFD are listed and discussed. Briefly, this list includes turbulent/transition physics and modeling, high-speed real gas flows, interdisciplinary research, turbomachinery demonstration computations, complete aircraft aerodynamics, rotorcraft applications, powered lift flows, high alpha flows, multiple body aerodynamics, and incompressible flow applications. Some of the individual problems actively being worked in each of these areas is listed to help define the breadth or extent of CFD involvement in each of these major programs. State-of-the-art examples of various CFD applications are presented to highlight most of these areas. The main emphasis of this portion of the presentation is on examples which will not otherwise be treated at this conference by the individual presentations. Finally, a list of principal current limitations and expected future directions is given.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=circulation+AND+Computer+AND+Science&id=EJ996923','ERIC'); return false;" href="https://eric.ed.gov/?q=circulation+AND+Computer+AND+Science&id=EJ996923"><span>An Interdisciplinary Guided Inquiry on Estuarine Transport Using a Computer Model in High School Classrooms</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Chan, Kit Yu Karen; Yang, Sylvia; Maliska, Max E.; Grunbaum, Daniel</p> <p>2012-01-01</p> <p>The National Science Education Standards have highlighted the importance of active learning and reflection for contemporary scientific methods in K-12 classrooms, including the use of models. Computer modeling and visualization are tools that researchers employ in their scientific inquiry process, and often computer models are used in…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=novel+AND+computation&pg=2&id=ED575224','ERIC'); return false;" href="https://eric.ed.gov/?q=novel+AND+computation&pg=2&id=ED575224"><span>Architectural Principles and Experimentation of Distributed High Performance Virtual Clusters</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Younge, Andrew J.</p> <p>2016-01-01</p> <p>With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their scientific computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities, and the many novel computing paradigms available for…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Scientific+AND+Research&id=EJ1008271','ERIC'); return false;" href="https://eric.ed.gov/?q=Scientific+AND+Research&id=EJ1008271"><span>An Analysis on the Effect of Computer Self-Efficacy over Scientific Research Self-Efficacy and Information Literacy Self-Efficacy</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Tuncer, Murat</p> <p>2013-01-01</p> <p>Present research investigates reciprocal relations amidst computer self-efficacy, scientific research and information literacy self-efficacy. Research findings have demonstrated that according to standardized regression coefficients, computer self-efficacy has a positive effect on information literacy self-efficacy. Likewise it has been detected…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://eric.ed.gov/?q=Solar+AND+system+AND+facts&pg=2&id=EJ682119','ERIC'); return false;" href="https://eric.ed.gov/?q=Solar+AND+system+AND+facts&pg=2&id=EJ682119"><span>The Impact of Three-Dimensional Computational Modeling on Student Understanding of Astronomical Concepts: A Quantitative Analysis</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.eric.ed.gov/ERICWebPortal/search/extended.jsp?_pageLabel=advanced">ERIC Educational Resources Information Center</a></p> <p>Hansen, John; Barnett, Michael; MaKinster, James; Keating, Thomas</p> <p>2004-01-01</p> <p>The increased availability of computational modeling software has created opportunities for students to engage in scientific inquiry through constructing computer-based models of scientific phenomena. However, despite the growing trend of integrating technology into science curricula, educators need to understand what aspects of these technologies…</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/1999PhDT.......341M','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/1999PhDT.......341M"><span>Introducing social cues in multimedia learning: The role of pedagogic agents' image and language in a scientific lesson</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Moreno, Roxana Arleen</p> <p></p> <p>The present dissertation tested the hypothesis that software pedagogical agents can promote constructivist learning in a discovery-based multimedia environment. In a preliminary study, students who received a computer-based lesson on environmental science performed better on subsequent tests of problem solving and motivation when they learned with the mediation of a fictional agent compared to when they learned the same material from text. In order to investigate further the basis for this personal agent effect, I varied whether the agent's words were presented as speech or on-screen text and whether or not the agent's image appeared on the screen. Both with a fictional agent (Experiment 1) and a video of a human face (Experiment 2), students performed better on tests of retention, problem-solving transfer, and program ratings when words were presented as speech rather than on-screen text (producing a modality effect) but visual presence of the agent did not affect test performance (producing no image effect). Next, I varied whether or not the agent's words were presented in conversational style (i.e., as dialogue) or formal style (i.e., as monologue) both using speech (Experiment 3) and on-screen text (Experiment 4). In both experiments, there was a dialogue effect in which conversational-style produced better retention and transfer performance. Students who learned with conversational-style text rated the program more favorably than those who learned with monologue-style text. The results support cognitive principles of multimedia learning which underlie the understanding of a computer lesson about a complex scientific system.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/373868','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/373868"><span>Hidden Markov models and other machine learning approaches in computational molecular biology</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Baldi, P.</p> <p>1995-12-31</p> <p>This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In thismore » tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://hdl.handle.net/2060/19890015789','NASA-TRS'); return false;" href="http://hdl.handle.net/2060/19890015789"><span>Demonstration of decomposition and optimization in the design of experimental space systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://ntrs.nasa.gov/search.jsp">NASA Technical Reports Server (NTRS)</a></p> <p>Padula, Sharon; Sandridge, Chris A.; Haftka, Raphael T.; Walsh, Joanne L.</p> <p>1989-01-01</p> <p>Effective design strategies for a class of systems which may be termed Experimental Space Systems (ESS) are needed. These systems, which include large space antenna and observatories, space platforms, earth satellites and deep space explorers, have special characteristics which make them particularly difficult to design. It is argued here that these same characteristics encourage the use of advanced computer-aided optimization and planning techniques. The broad goal of this research is to develop optimization strategies for the design of ESS. These strategics would account for the possibly conflicting requirements of mission life, safety, scientific payoffs, initial system cost, launch limitations and maintenance costs. The strategies must also preserve the coupling between disciplines or between subsystems. Here, the specific purpose is to describe a computer-aided planning and scheduling technique. This technique provides the designer with a way to map the flow of data between multidisciplinary analyses. The technique is important because it enables the designer to decompose the system design problem into a number of smaller subproblems. The planning and scheduling technique is demonstrated by its application to a specific preliminary design problem.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2000SPIE.4132...83S','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2000SPIE.4132...83S"><span>Advanced processing for high-bandwidth sensor systems</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Szymanski, John J.; Blain, Phil C.; Bloch, Jeffrey J.; Brislawn, Christopher M.; Brumby, Steven P.; Cafferty, Maureen M.; Dunham, Mark E.; Frigo, Janette R.; Gokhale, Maya; Harvey, Neal R.; Kenyon, Garrett; Kim, Won-Ha; Layne, J.; Lavenier, Dominique D.; McCabe, Kevin P.; Mitchell, Melanie; Moore, Kurt R.; Perkins, Simon J.; Porter, Reid B.; Robinson, S.; Salazar, Alfonso; Theiler, James P.; Young, Aaron C.</p> <p>2000-11-01</p> <p>Compute performance and algorithm design are key problems of image processing and scientific computing in general. For example, imaging spectrometers are capable of producing data in hundreds of spectral bands with millions of pixels. These data sets show great promise for remote sensing applications, but require new and computationally intensive processing. The goal of the Deployable Adaptive Processing Systems (DAPS) project at Los Alamos National Laboratory is to develop advanced processing hardware and algorithms for high-bandwidth sensor applications. The project has produced electronics for processing multi- and hyper-spectral sensor data, as well as LIDAR data, while employing processing elements using a variety of technologies. The project team is currently working on reconfigurable computing technology and advanced feature extraction techniques, with an emphasis on their application to image and RF signal processing. This paper presents reconfigurable computing technology and advanced feature extraction algorithm work and their application to multi- and hyperspectral image processing. Related projects on genetic algorithms as applied to image processing will be introduced, as will the collaboration between the DAPS project and the DARPA Adaptive Computing Systems program. Further details are presented in other talks during this conference and in other conferences taking place during this symposium.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/biblio/927730-interoperability-gadu-using-heterogeneous-grid-resources-bioinformatics-applications','SCIGOV-STC'); return false;" href="https://www.osti.gov/biblio/927730-interoperability-gadu-using-heterogeneous-grid-resources-bioinformatics-applications"><span>Interoperability of GADU in using heterogeneous Grid resources for bioinformatics applications.</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Sulakhe, D.; Rodriguez, A.; Wilde, M.</p> <p>2008-03-01</p> <p>Bioinformatics tools used for efficient and computationally intensive analysis of genetic sequences require large-scale computational resources to accommodate the growing data. Grid computational resources such as the Open Science Grid and TeraGrid have proved useful for scientific discovery. The genome analysis and database update system (GADU) is a high-throughput computational system developed to automate the steps involved in accessing the Grid resources for running bioinformatics applications. This paper describes the requirements for building an automated scalable system such as GADU that can run jobs on different Grids. The paper describes the resource-independent configuration of GADU using the Pegasus-based virtual datamore » system that makes high-throughput computational tools interoperable on heterogeneous Grid resources. The paper also highlights the features implemented to make GADU a gateway to computationally intensive bioinformatics applications on the Grid. The paper will not go into the details of problems involved or the lessons learned in using individual Grid resources as it has already been published in our paper on genome analysis research environment (GNARE) and will focus primarily on the architecture that makes GADU resource independent and interoperable across heterogeneous Grid resources.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('https://www.osti.gov/servlets/purl/1375720','SCIGOV-STC'); return false;" href="https://www.osti.gov/servlets/purl/1375720"><span>Biological and Environmental Research Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Biological and Environmental Research, March 28-31, 2016, Rockville, Maryland</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://www.osti.gov/search">DOE Office of Scientific and Technical Information (OSTI.GOV)</a></p> <p>Arkin, Adam; Bader, David C.; Coffey, Richard</p> <p></p> <p>Understanding the fundamentals of genomic systems or the processes governing impactful weather patterns are examples of the types of simulation and modeling performed on the most advanced computing resources in America. High-performance computing and computational science together provide a necessary platform for the mission science conducted by the Biological and Environmental Research (BER) office at the U.S. Department of Energy (DOE). This report reviews BER’s computing needs and their importance for solving some of the toughest problems in BER’s portfolio. BER’s impact on science has been transformative. Mapping the human genome, including the U.S.-supported international Human Genome Project that DOEmore » began in 1987, initiated the era of modern biotechnology and genomics-based systems biology. And since the 1950s, BER has been a core contributor to atmospheric, environmental, and climate science research, beginning with atmospheric circulation studies that were the forerunners of modern Earth system models (ESMs) and by pioneering the implementation of climate codes onto high-performance computers. See http://exascaleage.org/ber/ for more information.« less</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2003copm.book.....T','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2003copm.book.....T"><span>Colour and Optical Properties of Materials: An Exploration of the Relationship Between Light, the Optical Properties of Materials and Colour</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Tilley, Richard J. D.</p> <p>2003-05-01</p> <p>Colour is an important and integral part of everyday life, and an understanding and knowledge of the scientific principles behind colour, with its many applications and uses, is becoming increasingly important to a wide range of academic disciplines, from physical, medical and biological sciences through to the arts. Colour and the Optical Properties of Materials carefully introduces the science behind the subject, along with many modern and cutting-edge applications, chose to appeal to today's students. For science students, it provides a broad introduction to the subject and the many applications of colour. To more applied students, such as engineering and arts students, it provides the essential scientific background to colour and the many applications. Features: * Introduces the science behind the subject whilst closely connecting it to modern applications, such as colour displays, optical amplifiers and colour centre lasers * Richly illustrated with full-colour plates * Includes many worked examples, along with problems and exercises at the end of each chapter and selected answers at the back of the book * A Web site, including additional problems and full solutions to all the problems, which may be accessed at: www.cardiff.ac.uk/uwcc/engin/staff/rdjt/colour Written for students taking an introductory course in colour in a wide range of disciplines such as physics, chemistry, engineering, materials science, computer science, design, photography, architecture and textiles.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2016CoPhC.202..326E','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2016CoPhC.202..326E"><span>High performance computing aspects of a dimension independent semi-Lagrangian discontinuous Galerkin code</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Einkemmer, Lukas</p> <p>2016-05-01</p> <p>The recently developed semi-Lagrangian discontinuous Galerkin approach is used to discretize hyperbolic partial differential equations (usually first order equations). Since these methods are conservative, local in space, and able to limit numerical diffusion, they are considered a promising alternative to more traditional semi-Lagrangian schemes (which are usually based on polynomial or spline interpolation). In this paper, we consider a parallel implementation of a semi-Lagrangian discontinuous Galerkin method for distributed memory systems (so-called clusters). Both strong and weak scaling studies are performed on the Vienna Scientific Cluster 2 (VSC-2). In the case of weak scaling we observe a parallel efficiency above 0.8 for both two and four dimensional problems and up to 8192 cores. Strong scaling results show good scalability to at least 512 cores (we consider problems that can be run on a single processor in reasonable time). In addition, we study the scaling of a two dimensional Vlasov-Poisson solver that is implemented using the framework provided. All of the simulations are conducted in the context of worst case communication overhead; i.e., in a setting where the CFL (Courant-Friedrichs-Lewy) number increases linearly with the problem size. The framework introduced in this paper facilitates a dimension independent implementation of scientific codes (based on C++ templates) using both an MPI and a hybrid approach to parallelization. We describe the essential ingredients of our implementation.</p> </li> <li> <p><a target="_blank" rel="noopener noreferrer" onclick="trackOutboundLink('http://adsabs.harvard.edu/abs/2017Sc%26Ed..26.1001D','NASAADS'); return false;" href="http://adsabs.harvard.edu/abs/2017Sc%26Ed..26.1001D"><span>Using Computer Simulations for Promoting Model-based Reasoning. Epistemological and Educational Dimensions</span></a></p> <p><a target="_blank" rel="noopener noreferrer" href="http://adsabs.harvard.edu/abstract_service.html">NASA Astrophysics Data System (ADS)</a></p> <p>Develaki, Maria</p> <p>2017-11-01</p> <p>Scientific reasoning is particularly pertinent to science education since it is closely related to the content and methodologies of science and contributes to scientific literacy. Much of the research in science education investigates the appropriate framework and teaching methods and tools needed to promote students' ability to reason and evaluate in a scientific way. This paper aims (a) to contribute to an extended understanding of the nature and pedagogical importance of model-based reasoning and (b) to exemplify how using computer simulations can support students' model-based reasoning. We provide first a background for both scientific reasoning and computer simulations, based on the relevant philosophical views and the related educational discussion. This background suggests that the model-based framework provides an epistemologically valid and pedagogically appropriate basis for teaching scientific reasoning and for helping students develop sounder reasoning and decision-taking abilities and explains how using computer simulations can foster these abilities. We then provide some examples illustrating the use of computer simulations to support model-based reasoning and evaluation activities in the classroom. The examples reflect the procedure and criteria for evaluating models in science and demonstrate the educational advantages of their application in classroom reasoning activities.</p> </li> </ol> <div class="pull-right"> <ul class="pagination"> <li><a href="#" onclick='return showDiv("page_1");'>«</a></li> <li><a href="#" onclick='return showDiv("page_21");'>21</a></li> <li><a href="#" onclick='return showDiv("page_22");'>22</a></li> <li><a href="#" onclick='return showDiv("page_23");'>23</a></li> <li><a href="#" onclick='return showDiv("page_24");'>24</a></li> <li class="active"><span>25</span></li> <li><a href="#" onclick='return showDiv("page_25");'>»</a></li> </ul> </div> </div><!-- col-sm-12 --> </div><!-- row --> </div><!-- page_25 --> <div class="footer-extlink text-muted" style="margin-bottom:1rem; text-align:center;">Some links on this page may take you to non-federal websites. 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