Northwest Trajectory Analysis Capability: A Platform for Enhancing Computational Biophysics Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peterson, Elena S.; Stephan, Eric G.; Corrigan, Abigail L.

2008-07-30

As computational resources continue to increase, the ability of computational simulations to effectively complement, and in some cases replace, experimentation in scientific exploration also increases. Today, large-scale simulations are recognized as an effective tool for scientific exploration in many disciplines including chemistry and biology. A natural side effect of this trend has been the need for an increasingly complex analytical environment. In this paper, we describe Northwest Trajectory Analysis Capability (NTRAC), an analytical software suite developed to enhance the efficiency of computational biophysics analyses. Our strategy is to layer higher-level services and introduce improved tools within the user’s familiar environmentmore » without preventing researchers from using traditional tools and methods. Our desire is to share these experiences to serve as an example for effectively analyzing data intensive large scale simulation data.« less

Exascale computing and big data

DOE PAGES

Reed, Daniel A.; Dongarra, Jack

2015-06-25

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates themore » openness of the scientific process.« less

Exascale computing and big data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reed, Daniel A.; Dongarra, Jack

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates themore » openness of the scientific process.« less

Large-Scale Distributed Computational Fluid Dynamics on the Information Power Grid Using Globus

NASA Technical Reports Server (NTRS)

Barnard, Stephen; Biswas, Rupak; Saini, Subhash; VanderWijngaart, Robertus; Yarrow, Maurice; Zechtzer, Lou; Foster, Ian; Larsson, Olle

1999-01-01

This paper describes an experiment in which a large-scale scientific application development for tightly-coupled parallel machines is adapted to the distributed execution environment of the Information Power Grid (IPG). A brief overview of the IPG and a description of the computational fluid dynamics (CFD) algorithm are given. The Globus metacomputing toolkit is used as the enabling device for the geographically-distributed computation. Modifications related to latency hiding and Load balancing were required for an efficient implementation of the CFD application in the IPG environment. Performance results on a pair of SGI Origin 2000 machines indicate that real scientific applications can be effectively implemented on the IPG; however, a significant amount of continued effort is required to make such an environment useful and accessible to scientists and engineers.

Extreme-Scale Computing Project Aims to Advance Precision Oncology | FNLCR Staging

Cancer.gov

Two government agencies and five national laboratories are collaborating to develop extremely high-performance computing capabilities that will analyze mountains of research and clinical data to improve scientific understanding of cancer, predict dru

Data Intensive Scientific Workflows on a Federated Cloud: CRADA Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garzoglio, Gabriele

The Fermilab Scientific Computing Division and the KISTI Global Science Experimental Data Hub Center have built a prototypical large-scale infrastructure to handle scientific workflows of stakeholders to run on multiple cloud resources. The demonstrations have been in the areas of (a) Data-Intensive Scientific Workflows on Federated Clouds, (b) Interoperability and Federation of Cloud Resources, and (c) Virtual Infrastructure Automation to enable On-Demand Services.

Opening Remarks: SciDAC 2007

NASA Astrophysics Data System (ADS)

Strayer, Michael

2007-09-01

Good morning. Welcome to Boston, the home of the Red Sox, Celtics and Bruins, baked beans, tea parties, Robert Parker, and SciDAC 2007. A year ago I stood before you to share the legacy of the first SciDAC program and identify the challenges that we must address on the road to petascale computing—a road E E Cummins described as `. . . never traveled, gladly beyond any experience.' Today, I want to explore the preparations for the rapidly approaching extreme scale (X-scale) generation. These preparations are the first step propelling us along the road of burgeoning scientific discovery enabled by the application of X- scale computing. We look to petascale computing and beyond to open up a world of discovery that cuts across scientific fields and leads us to a greater understanding of not only our world, but our universe. As part of the President's America Competitiveness Initiative, the ASCR Office has been preparing a ten year vision for computing. As part of this planning the LBNL together with ORNL and ANL hosted three town hall meetings on Simulation and Modeling at the Exascale for Energy, Ecological Sustainability and Global Security (E3). The proposed E3 initiative is organized around four programmatic themes: Engaging our top scientists, engineers, computer scientists and applied mathematicians; investing in pioneering large-scale science; developing scalable analysis algorithms, and storage architectures to accelerate discovery; and accelerating the build-out and future development of the DOE open computing facilities. It is clear that we have only just started down the path to extreme scale computing. Plan to attend Thursday's session on the out-briefing and discussion of these meetings. The road to the petascale has been at best rocky. In FY07, the continuing resolution provided 12% less money for Advanced Scientific Computing than either the President, the Senate, or the House. As a consequence, many of you had to absorb a no cost extension for your SciDAC work. I am pleased that the President's FY08 budget restores the funding for SciDAC. Quoting from Advanced Scientific Computing Research description in the House Energy and Water Development Appropriations Bill for FY08, "Perhaps no other area of research at the Department is so critical to sustaining U.S. leadership in science and technology, revolutionizing the way science is done and improving research productivity." As a society we need to revolutionize our approaches to energy, environmental and global security challenges. As we go forward along the road to the X-scale generation, the use of computation will continue to be a critical tool along with theory and experiment in understanding the behavior of the fundamental components of nature as well as for fundamental discovery and exploration of the behavior of complex systems. The foundation to overcome these societal challenges will build from the experiences and knowledge gained as you, members of our SciDAC research teams, work together to attack problems at the tera- and peta- scale. If SciDAC is viewed as an experiment for revolutionizing scientific methodology, then a strategic goal of ASCR program must be to broaden the intellectual base prepared to address the challenges of the new X-scale generation of computing. We must focus our computational science experiences gained over the past five years on the opportunities introduced with extreme scale computing. Our facilities are on a path to provide the resources needed to undertake the first part of our journey. Using the newly upgraded 119 teraflop Cray XT system at the Leadership Computing Facility, SciDAC research teams have in three days performed a 100-year study of the time evolution of the atmospheric CO2 concentration originating from the land surface. The simulation of the El Nino/Southern Oscillation which was part of this study has been characterized as `the most impressive new result in ten years' gained new insight into the behavior of superheated ionic gas in the ITER reactor as a result of an AORSA run on 22,500 processors that achieved over 87 trillion calculations per second (87 teraflops) which is 74% of the system's theoretical peak. Tomorrow, Argonne and IBM will announce that the first IBM Blue Gene/P, a 100 teraflop system, will be shipped to the Argonne Leadership Computing Facility later this fiscal year. By the end of FY2007 ASCR high performance and leadership computing resources will include the 114 teraflop IBM Blue Gene/P; a 102 teraflop Cray XT4 at NERSC and a 119 teraflop Cray XT system at Oak Ridge. Before ringing in the New Year, Oak Ridge will upgrade to 250 teraflops with the replacement of the dual core processors with quad core processors and Argonne will upgrade to between 250-500 teraflops, and next year, a petascale Cray Baker system is scheduled for delivery at Oak Ridge. The multidisciplinary teams in our SciDAC Centers for Enabling Technologies and our SciDAC Institutes must continue to work with our Scientific Application teams to overcome the barriers that prevent effective use of these new systems. These challenges include: the need for new algorithms as well as operating system and runtime software and tools which scale to parallel systems composed of hundreds of thousands processors; program development environments and tools which scale effectively and provide ease of use for developers and scientific end users; and visualization and data management systems that support moving, storing, analyzing, manipulating and visualizing multi-petabytes of scientific data and objects. The SciDAC Centers, located primarily at our DOE national laboratories will take the lead in ensuring that critical computer science and applied mathematics issues are addressed in a timely and comprehensive fashion and to address issues associated with research software lifecycle. In contrast, the SciDAC Institutes, which are university-led centers of excellence, will have more flexibility to pursue new research topics through a range of research collaborations. The Institutes will also work to broaden the intellectual and researcher base—conducting short courses and summer schools to take advantage of new high performance computing capabilities. The SciDAC Outreach Center at Lawrence Berkeley National Laboratory complements the outreach efforts of the SciDAC Institutes. The Outreach Center is our clearinghouse for SciDAC activities and resources and will communicate with the high performance computing community in part to understand their needs for workshops, summer schools and institutes. SciDAC is not ASCR's only effort to broaden the computational science community needed to meet the challenges of the new X-scale generation. I hope that you were able to attend the Computational Science Graduate Fellowship poster session last night. ASCR developed the fellowship in 1991 to meet the nation's growing need for scientists and technology professionals with advanced computer skills. CSGF, now jointly funded between ASCR and NNSA, is more than a traditional academic fellowship. It has provided more than 200 of the best and brightest graduate students with guidance, support and community in preparing them as computational scientists. Today CSGF alumni are bringing their diverse top-level skills and knowledge to research teams at DOE laboratories and in industries such as Proctor and Gamble, Lockheed Martin and Intel. At universities they are working to train the next generation of computational scientists. To build on this success, we intend to develop a wholly new Early Career Principal Investigator's (ECPI) program. Our objective is to stimulate academic research in scientific areas within ASCR's purview especially among faculty in early stages of their academic careers. Last February, we lost Ken Kennedy, one of the leading lights of our community. As we move forward into the extreme computing generation, his vision and insight will be greatly missed. In memorial to Ken Kennedy, we shall designate the ECPI grants to beginning faculty in Computer Science as the Ken Kennedy Fellowship. Watch the ASCR website for more information about ECPI and other early career programs in the computational sciences. We look to you, our scientists, researchers, and visionaries to take X-scale computing and use it to explode scientific discovery in your fields. We at SciDAC will work to ensure that this tool is the sharpest and most precise and efficient instrument to carve away the unknown and reveal the most exciting secrets and stimulating scientific discoveries of our time. The partnership between research and computing is the marriage that will spur greater discovery, and as Spencer said to Susan in Robert Parker's novel, `Sudden Mischief', `We stick together long enough, and we may get as smart as hell'. Michael Strayer

Extreme-Scale Computing Project Aims to Advance Precision Oncology | Frederick National Laboratory for Cancer Research

Cancer.gov

Two government agencies and five national laboratories are collaborating to develop extremely high-performance computing capabilities that will analyze mountains of research and clinical data to improve scientific understanding of cancer, predict dru

Extreme-Scale Computing Project Aims to Advance Precision Oncology | Poster

Cancer.gov

Two government agencies and five national laboratories are collaborating to develop extremely high-performance computing capabilities that will analyze mountains of research and clinical data to improve scientific understanding of cancer, predict drug response, and improve treatments for patients.

GISpark: A Geospatial Distributed Computing Platform for Spatiotemporal Big Data

NASA Astrophysics Data System (ADS)

Wang, S.; Zhong, E.; Wang, E.; Zhong, Y.; Cai, W.; Li, S.; Gao, S.

2016-12-01

Geospatial data are growing exponentially because of the proliferation of cost effective and ubiquitous positioning technologies such as global remote-sensing satellites and location-based devices. Analyzing large amounts of geospatial data can provide great value for both industrial and scientific applications. Data- and compute- intensive characteristics inherent in geospatial big data increasingly pose great challenges to technologies of data storing, computing and analyzing. Such challenges require a scalable and efficient architecture that can store, query, analyze, and visualize large-scale spatiotemporal data. Therefore, we developed GISpark - a geospatial distributed computing platform for processing large-scale vector, raster and stream data. GISpark is constructed based on the latest virtualized computing infrastructures and distributed computing architecture. OpenStack and Docker are used to build multi-user hosting cloud computing infrastructure for GISpark. The virtual storage systems such as HDFS, Ceph, MongoDB are combined and adopted for spatiotemporal data storage management. Spark-based algorithm framework is developed for efficient parallel computing. Within this framework, SuperMap GIScript and various open-source GIS libraries can be integrated into GISpark. GISpark can also integrated with scientific computing environment (e.g., Anaconda), interactive computing web applications (e.g., Jupyter notebook), and machine learning tools (e.g., TensorFlow/Orange). The associated geospatial facilities of GISpark in conjunction with the scientific computing environment, exploratory spatial data analysis tools, temporal data management and analysis systems make up a powerful geospatial computing tool. GISpark not only provides spatiotemporal big data processing capacity in the geospatial field, but also provides spatiotemporal computational model and advanced geospatial visualization tools that deals with other domains related with spatial property. We tested the performance of the platform based on taxi trajectory analysis. Results suggested that GISpark achieves excellent run time performance in spatiotemporal big data applications.

A Rich Metadata Filesystem for Scientific Data

ERIC Educational Resources Information Center

Bui, Hoang

2012-01-01

As scientific research becomes more data intensive, there is an increasing need for scalable, reliable, and high performance storage systems. Such data repositories must provide both data archival services and rich metadata, and cleanly integrate with large scale computing resources. ROARS is a hybrid approach to distributed storage that provides…

The scaling issue: scientific opportunities

NASA Astrophysics Data System (ADS)

Orbach, Raymond L.

2009-07-01

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.

Mathematical and Computational Challenges in Population Biology and Ecosystems Science

NASA Technical Reports Server (NTRS)

Levin, Simon A.; Grenfell, Bryan; Hastings, Alan; Perelson, Alan S.

1997-01-01

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.

Software Engineering for Scientific Computer Simulations

NASA Astrophysics Data System (ADS)

Post, Douglass E.; Henderson, Dale B.; Kendall, Richard P.; Whitney, Earl M.

2004-11-01

Computer simulation is becoming a very powerful tool for analyzing and predicting the performance of fusion experiments. Simulation efforts are evolving from including only a few effects to many effects, from small teams with a few people to large teams, and from workstations and small processor count parallel computers to massively parallel platforms. Successfully making this transition requires attention to software engineering issues. We report on the conclusions drawn from a number of case studies of large scale scientific computing projects within DOE, academia and the DoD. The major lessons learned include attention to sound project management including setting reasonable and achievable requirements, building a good code team, enforcing customer focus, carrying out verification and validation and selecting the optimum computational mathematics approaches.

Recent Enhancements to the Community Multiscale Air Quality Modeling System (CMAQ)

EPA Science Inventory

EPA’s Office of Research and Development, Computational Exposure Division held a webinar on January 31, 2017 to present the recent scientific and computational updates made by EPA to the Community Multi-Scale Air Quality Model (CMAQ). Topics covered included: (1) Improveme...

Information Science Research: The Search for the Nature of Information.

ERIC Educational Resources Information Center

Kochen, Manfred

1984-01-01

High-level scientific research in the information sciences is illustrated by sampling of recent discoveries involving adaptive information processing strategies, computer and information systems, centroid scaling, economic growth of computer and communication industries, and information flow in biological systems. Relationship of information…

Scaling Watershed Models: Modern Approaches to Science Computation with MapReduce, Parallelization, and Cloud Optimization

EPA Science Inventory

Environmental models are products of the computer architecture and software tools available at the time of development. Scientifically sound algorithms may persist in their original state even as system architectures and software development approaches evolve and progress. Dating...

Parallel Computation of the Regional Ocean Modeling System (ROMS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, P; Song, Y T; Chao, Y

2005-04-05

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

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heroux, Michael A.; Bartlett, Roscoe; Demeshko, Irina

Here, extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly. The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, SuperLU, Trilinos, and Alquimia) and provides a foundationmore » for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.« less

xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit

DOE PAGES

Heroux, Michael A.; Bartlett, Roscoe; Demeshko, Irina; ...

2017-03-01

Here, extreme-scale computational science increasingly demands multiscale and multiphysics formulations. Combining software developed by independent groups is imperative: no single team has resources for all predictive science and decision support capabilities. Scientific libraries provide high-quality, reusable software components for constructing applications with improved robustness and portability. However, without coordination, many libraries cannot be easily composed. Namespace collisions, inconsistent arguments, lack of third-party software versioning, and additional difficulties make composition costly. The Extreme-scale Scientific Software Development Kit (xSDK) defines community policies to improve code quality and compatibility across independently developed packages (hypre, PETSc, SuperLU, Trilinos, and Alquimia) and provides a foundationmore » for addressing broader issues in software interoperability, performance portability, and sustainability. The xSDK provides turnkey installation of member software and seamless combination of aggregate capabilities, and it marks first steps toward extreme-scale scientific software ecosystems from which future applications can be composed rapidly with assured quality and scalability.« less

DOE High Performance Computing Operational Review (HPCOR): Enabling Data-Driven Scientific Discovery at HPC Facilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Allcock, William; Beggio, Chris

2014-10-17

U.S. Department of Energy (DOE) High Performance Computing (HPC) facilities are on the verge of a paradigm shift in the way they deliver systems and services to science and engineering teams. Research projects are producing a wide variety of data at unprecedented scale and level of complexity, with community-specific services that are part of the data collection and analysis workflow. On June 18-19, 2014 representatives from six DOE HPC centers met in Oakland, CA at the DOE High Performance Operational Review (HPCOR) to discuss how they can best provide facilities and services to enable large-scale data-driven scientific discovery at themore » DOE national laboratories. The report contains findings from that review.« less

SciSpark's SRDD : A Scientific Resilient Distributed Dataset for Multidimensional Data

NASA Astrophysics Data System (ADS)

Palamuttam, R. S.; Wilson, B. D.; Mogrovejo, R. M.; Whitehall, K. D.; Mattmann, C. A.; McGibbney, L. J.; Ramirez, P.

2015-12-01

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.

Multiscale Computation. Needs and Opportunities for BER Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Scheibe, Timothy D.; Smith, Jeremy C.

2015-01-01

The Environmental Molecular Sciences Laboratory (EMSL), a scientific user facility managed by Pacific Northwest National Laboratory for the U.S. Department of Energy, Office of Biological and Environmental Research (BER), conducted a one-day workshop on August 26, 2014 on the topic of “Multiscale Computation: Needs and Opportunities for BER Science.” Twenty invited participants, from various computational disciplines within the BER program research areas, were charged with the following objectives; Identify BER-relevant models and their potential cross-scale linkages that could be exploited to better connect molecular-scale research to BER research at larger scales and; Identify critical science directions that will motivate EMSLmore » decisions regarding future computational (hardware and software) architectures.« less

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

DOE PAGES

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; ...

2018-03-22

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore howmore » different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.« less

Emerging Nanophotonic Applications Explored with Advanced Scientific Parallel Computing

NASA Astrophysics Data System (ADS)

Meng, Xiang

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).

The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications

NASA Technical Reports Server (NTRS)

Johnston, William E.

2002-01-01

With the advent of Grids - infrastructure for using and managing widely distributed computing and data resources in the science environment - there is now an opportunity to provide a standard, large-scale, computing, data, instrument, and collaboration environment for science that spans many different projects and provides the required infrastructure and services in a relatively uniform and supportable way. Grid technology has evolved over the past several years to provide the services and infrastructure needed for building 'virtual' systems and organizations. We argue that Grid technology provides an excellent basis for the creation of the integrated environments that can combine the resources needed to support the large- scale science projects located at multiple laboratories and universities. We present some science case studies that indicate that a paradigm shift in the process of science will come about as a result of Grids providing transparent and secure access to advanced and integrated information and technologies infrastructure: powerful computing systems, large-scale data archives, scientific instruments, and collaboration tools. These changes will be in the form of services that can be integrated with the user's work environment, and that enable uniform and highly capable access to these computers, data, and instruments, regardless of the location or exact nature of these resources. These services will integrate transient-use resources like computing systems, scientific instruments, and data caches (e.g., as they are needed to perform a simulation or analyze data from a single experiment); persistent-use resources. such as databases, data catalogues, and archives, and; collaborators, whose involvement will continue for the lifetime of a project or longer. While we largely address large-scale science in this paper, Grids, particularly when combined with Web Services, will address a broad spectrum of science scenarios. both large and small scale.

Java Performance for Scientific Applications on LLNL Computer Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kapfer, C; Wissink, A

2002-05-10

Languages in use for high performance computing at the laboratory--Fortran (f77 and f90), C, and C++--have many years of development behind them and are generally considered the fastest available. However, Fortran and C do not readily extend to object-oriented programming models, limiting their capability for very complex simulation software. C++ facilitates object-oriented programming but is a very complex and error-prone language. Java offers a number of capabilities that these other languages do not. For instance it implements cleaner (i.e., easier to use and less prone to errors) object-oriented models than C++. It also offers networking and security as part ofmore » the language standard, and cross-platform executables that make it architecture neutral, to name a few. These features have made Java very popular for industrial computing applications. The aim of this paper is to explain the trade-offs in using Java for large-scale scientific applications at LLNL. Despite its advantages, the computational science community has been reluctant to write large-scale computationally intensive applications in Java due to concerns over its poor performance. However, considerable progress has been made over the last several years. The Java Grande Forum [1] has been promoting the use of Java for large-scale computing. Members have introduced efficient array libraries, developed fast just-in-time (JIT) compilers, and built links to existing packages used in high performance parallel computing.« less

DOE Advanced Scientific Computing Advisory Subcommittee (ASCAC) Report: Top Ten Exascale Research Challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lucas, Robert; Ang, James; Bergman, Keren

2014-02-10

Exascale computing systems are essential for the scientific fields that will transform the 21st century global economy, including energy, biotechnology, nanotechnology, and materials science. Progress in these fields is predicated on the ability to perform advanced scientific and engineering simulations, and analyze the deluge of data. On July 29, 2013, ASCAC was charged by Patricia Dehmer, the Acting Director of the Office of Science, to assemble a subcommittee to provide advice on exascale computing. This subcommittee was directed to return a list of no more than ten technical approaches (hardware and software) that will enable the development of a systemmore » that achieves the Department's goals for exascale computing. Numerous reports over the past few years have documented the technical challenges and the non¬-viability of simply scaling existing computer designs to reach exascale. The technical challenges revolve around energy consumption, memory performance, resilience, extreme concurrency, and big data. Drawing from these reports and more recent experience, this ASCAC subcommittee has identified the top ten computing technology advancements that are critical to making a capable, economically viable, exascale system.« less

Advanced computations in plasma physics

NASA Astrophysics Data System (ADS)

Tang, W. M.

2002-05-01

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard; Hack, James; Riley, Katherine

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

Improving Data Mobility & Management for International Cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borrill, Julian; Dart, Eli; Gore, Brooklin

In February 2015 the third workshop in the CrossConnects series, with a focus on Improving Data Mobility & Management for International Cosmology, was held at Lawrence Berkeley National Laboratory. Scientists from fields including astrophysics, cosmology, and astronomy collaborated with experts in computing and networking to outline strategic opportunities for enhancing scientific productivity and effectively managing the ever-increasing scale of scientific data. While each field has unique details which depend on the instruments employed, the type and scale of the data, and the structure of scientific collaborations, several important themes emerged from the workshop discussions. Findings, as well as a setmore » of recommendations, are contained in their respective sections in this report.« less

PETSc Users Manual Revision 3.7

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balay, Satish; Abhyankar, S.; Adams, M.

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.

The Influence of Large-Scale Computing on Aircraft Structural Design.

DTIC Science & Technology

1986-04-01

the customer in the most cost- effective manner. Computer facility organizations became computer resource power brokers. A good data processing...capabilities generated on other processors can be easily used. This approach is easily implementable and provides a good strategy for using existing...assistance to member nations for the purpose of increasing their scientific and technical potential; - Recommending effective ways for the member nations to

PETSc Users Manual Revision 3.8

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balay, S.; Abhyankar, S.; Adams, M.

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.

Improving Data Mobility & Management for International Cosmology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Borrill, Julian; Dart, Eli; Gore, Brooklin

In February 2015 the third workshop in the CrossConnects series, with a focus on Improving Data Mobility & Management for International Cosmology, was held at Lawrence Berkeley National Laboratory. Scientists from fields including astrophysics, cosmology, and astronomy collaborated with experts in computing and networking to outline strategic opportunities for enhancing scientific productivity and effectively managing the ever-increasing scale of scientific data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lingerfelt, Eric J; Endeve, Eirik; Hui, Yawei

Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now--with the rise of multimodal acquisition systems and the associated processing capability--the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalablemore » data analysis and simulation and manage uploaded data files via an intuitive, cross-platform client user interface. This framework delivers authenticated, "push-button" execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing compute-and-data cloud infrastructures and HPC environments like Titan at the Oak Ridge Leadershp Computing Facility (OLCF).« less

A uniform approach for programming distributed heterogeneous computing systems

PubMed Central

Grasso, Ivan; Pellegrini, Simone; Cosenza, Biagio; Fahringer, Thomas

2014-01-01

Large-scale compute clusters of heterogeneous nodes equipped with multi-core CPUs and GPUs are getting increasingly popular in the scientific community. However, such systems require a combination of different programming paradigms making application development very challenging. In this article we introduce libWater, a library-based extension of the OpenCL programming model that simplifies the development of heterogeneous distributed applications. libWater consists of a simple interface, which is a transparent abstraction of the underlying distributed architecture, offering advanced features such as inter-context and inter-node device synchronization. It provides a runtime system which tracks dependency information enforced by event synchronization to dynamically build a DAG of commands, on which we automatically apply two optimizations: collective communication pattern detection and device-host-device copy removal. We assess libWater’s performance in three compute clusters available from the Vienna Scientific Cluster, the Barcelona Supercomputing Center and the University of Innsbruck, demonstrating improved performance and scaling with different test applications and configurations. PMID:25844015

A uniform approach for programming distributed heterogeneous computing systems.

PubMed

Grasso, Ivan; Pellegrini, Simone; Cosenza, Biagio; Fahringer, Thomas

2014-12-01

Large-scale compute clusters of heterogeneous nodes equipped with multi-core CPUs and GPUs are getting increasingly popular in the scientific community. However, such systems require a combination of different programming paradigms making application development very challenging. In this article we introduce libWater, a library-based extension of the OpenCL programming model that simplifies the development of heterogeneous distributed applications. libWater consists of a simple interface, which is a transparent abstraction of the underlying distributed architecture, offering advanced features such as inter-context and inter-node device synchronization. It provides a runtime system which tracks dependency information enforced by event synchronization to dynamically build a DAG of commands, on which we automatically apply two optimizations: collective communication pattern detection and device-host-device copy removal. We assess libWater's performance in three compute clusters available from the Vienna Scientific Cluster, the Barcelona Supercomputing Center and the University of Innsbruck, demonstrating improved performance and scaling with different test applications and configurations.

TOPICAL REVIEW: Advances and challenges in computational plasma science

NASA Astrophysics Data System (ADS)

Tang, W. M.; Chan, V. S.

2005-02-01

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.

Advances and challenges in computational plasma science

NASA Astrophysics Data System (ADS)

Tang, W. M.

2005-02-01

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.

Information Power Grid Posters

NASA Technical Reports Server (NTRS)

Vaziri, Arsi

2003-01-01

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.

High performance computing and communications: Advancing the frontiers of information technology

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1997-12-31

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

Final Report for "Implimentation and Evaluation of Multigrid Linear Solvers into Extended Magnetohydrodynamic Codes for Petascale Computing"

DOE Office of Scientific and Technical Information (OSTI.GOV)

Srinath Vadlamani; Scott Kruger; Travis Austin

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

Advanced Computation in Plasma Physics

NASA Astrophysics Data System (ADS)

Tang, William

2001-10-01

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.

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE PAGES

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin; ...

2015-02-19

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Understanding the Performance and Potential of Cloud Computing for Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sadooghi, Iman; Martin, Jesus Hernandez; Li, Tonglin

In this paper, commercial clouds bring a great opportunity to the scientific computing area. Scientific applications usually require significant resources, however not all scientists have access to sufficient high-end computing systems, may of which can be found in the Top500 list. Cloud Computing has gained the attention of scientists as a competitive resource to run HPC applications at a potentially lower cost. But as a different infrastructure, it is unclear whether clouds are capable of running scientific applications with a reasonable performance per money spent. This work studies the performance of public clouds and places this performance in context tomore » price. We evaluate the raw performance of different services of AWS cloud in terms of the basic resources, such as compute, memory, network and I/O. We also evaluate the performance of the scientific applications running in the cloud. This paper aims to assess the ability of the cloud to perform well, as well as to evaluate the cost of the cloud running scientific applications. We developed a full set of metrics and conducted a comprehensive performance evlauation over the Amazon cloud. We evaluated EC2, S3, EBS and DynamoDB among the many Amazon AWS services. We evaluated the memory sub-system performance with CacheBench, the network performance with iperf, processor and network performance with the HPL benchmark application, and shared storage with NFS and PVFS in addition to S3. We also evaluated a real scientific computing application through the Swift parallel scripting system at scale. Armed with both detailed benchmarks to gauge expected performance and a detailed monetary cost analysis, we expect this paper will be a recipe cookbook for scientists to help them decide where to deploy and run their scientific applications between public clouds, private clouds, or hybrid clouds.« less

Triangle Computer Science Distinguished Lecture Series

DTIC Science & Technology

2018-01-30

scientific inquiry - the cell, the brain, the market - as well as in the models developed by scientists over the centuries for studying them. Human...the great objects of scientific inquiry - the cell, the brain, the market - as well as in the models developed by scientists over the centuries for...in principle , secure system operation can be achieved. Massive-Scale Streaming Analytics David Bader, Georgia Institute of Technology (telecast from

What makes computational open source software libraries successful?

NASA Astrophysics Data System (ADS)

Bangerth, Wolfgang; Heister, Timo

2013-01-01

Software is the backbone of scientific computing. Yet, while we regularly publish detailed accounts about the results of scientific software, and while there is a general sense of which numerical methods work well, our community is largely unaware of best practices in writing the large-scale, open source scientific software upon which our discipline rests. This is particularly apparent in the commonly held view that writing successful software packages is largely the result of simply ‘being a good programmer’ when in fact there are many other factors involved, for example the social skill of community building. In this paper, we consider what we have found to be the necessary ingredients for successful scientific software projects and, in particular, for software libraries upon which the vast majority of scientific codes are built today. In particular, we discuss the roles of code, documentation, communities, project management and licenses. We also briefly comment on the impact on academic careers of engaging in software projects.

Managing competing elastic Grid and Cloud scientific computing applications using OpenNebula

NASA Astrophysics Data System (ADS)

Bagnasco, S.; Berzano, D.; Lusso, S.; Masera, M.; Vallero, S.

2015-12-01

Elastic cloud computing applications, i.e. applications that automatically scale according to computing needs, work on the ideal assumption of infinite resources. While large public cloud infrastructures may be a reasonable approximation of this condition, scientific computing centres like WLCG Grid sites usually work in a saturated regime, in which applications compete for scarce resources through queues, priorities and scheduling policies, and keeping a fraction of the computing cores idle to allow for headroom is usually not an option. In our particular environment one of the applications (a WLCG Tier-2 Grid site) is much larger than all the others and cannot autoscale easily. Nevertheless, other smaller applications can benefit of automatic elasticity; the implementation of this property in our infrastructure, based on the OpenNebula cloud stack, will be described and the very first operational experiences with a small number of strategies for timely allocation and release of resources will be discussed.

Combinatorial Algorithms to Enable Computational Science and Engineering: Work from the CSCAPES Institute

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boman, Erik G.; Catalyurek, Umit V.; Chevalier, Cedric

2015-01-16

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

Profiling and Improving I/O Performance of a Large-Scale Climate Scientific Application

NASA Technical Reports Server (NTRS)

Liu, Zhuo; Wang, Bin; Wang, Teng; Tian, Yuan; Xu, Cong; Wang, Yandong; Yu, Weikuan; Cruz, Carlos A.; Zhou, Shujia; Clune, Tom;

Data Crosscutting Requirements Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kleese van Dam, Kerstin; Shoshani, Arie; Plata, Charity

2013-04-01

In April 2013, a diverse group of researchers from the U.S. Department of Energy (DOE) scientific community assembled to assess data requirements associated with DOE-sponsored scientific facilities and large-scale experiments. Participants in the review included facilities staff, program managers, and scientific experts from the offices of Basic Energy Sciences, Biological and Environmental Research, High Energy Physics, and Advanced Scientific Computing Research. As part of the meeting, review participants discussed key issues associated with three distinct aspects of the data challenge: 1) processing, 2) management, and 3) analysis. These discussions identified commonalities and differences among the needs of varied scientific communities.more » They also helped to articulate gaps between current approaches and future needs, as well as the research advances that will be required to close these gaps. Moreover, the review provided a rare opportunity for experts from across the Office of Science to learn about their collective expertise, challenges, and opportunities. The "Data Crosscutting Requirements Review" generated specific findings and recommendations for addressing large-scale data crosscutting requirements.« less

Blazing Signature Filter: a library for fast pairwise similarity comparisons

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Joon-Yong; Fujimoto, Grant M.; Wilson, Ryan

Identifying similarities between datasets is a fundamental task in data mining and has become an integral part of modern scientific investigation. Whether the task is to identify co-expressed genes in large-scale expression surveys or to predict combinations of gene knockouts which would elicit a similar phenotype, the underlying computational task is often a multi-dimensional similarity test. As datasets continue to grow, improvements to the efficiency, sensitivity or specificity of such computation will have broad impacts as it allows scientists to more completely explore the wealth of scientific data. A significant practical drawback of large-scale data mining is the vast majoritymore » of pairwise comparisons are unlikely to be relevant, meaning that they do not share a signature of interest. It is therefore essential to efficiently identify these unproductive comparisons as rapidly as possible and exclude them from more time-intensive similarity calculations. The Blazing Signature Filter (BSF) is a highly efficient pairwise similarity algorithm which enables extensive data mining within a reasonable amount of time. The algorithm transforms datasets into binary metrics, allowing it to utilize the computationally efficient bit operators and provide a coarse measure of similarity. As a result, the BSF can scale to high dimensionality and rapidly filter unproductive pairwise comparison. Two bioinformatics applications of the tool are presented to demonstrate the ability to scale to billions of pairwise comparisons and the usefulness of this approach.« less

Parallel Tensor Compression for Large-Scale Scientific Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolda, Tamara G.; Ballard, Grey; Austin, Woody Nathan

As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed memorymore » parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.« less

Enabling large-scale viscoelastic calculations via neural network acceleration

NASA Astrophysics Data System (ADS)

Robinson DeVries, P.; Thompson, T. B.; Meade, B. J.

2017-12-01

One of the most significant challenges involved in efforts to understand the effects of repeated earthquake cycle activity are the computational costs of large-scale viscoelastic earthquake cycle models. Deep artificial neural networks (ANNs) can be used to discover new, compact, and accurate computational representations of viscoelastic physics. Once found, these efficient ANN representations may replace computationally intensive viscoelastic codes and accelerate large-scale viscoelastic calculations by more than 50,000%. This magnitude of acceleration enables the modeling of geometrically complex faults over thousands of earthquake cycles across wider ranges of model parameters and at larger spatial and temporal scales than have been previously possible. Perhaps most interestingly from a scientific perspective, ANN representations of viscoelastic physics may lead to basic advances in the understanding of the underlying model phenomenology. We demonstrate the potential of artificial neural networks to illuminate fundamental physical insights with specific examples.

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

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

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE PAGES

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

2016-07-26

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

NASA's computer science research program

NASA Technical Reports Server (NTRS)

Larsen, R. L.

1983-01-01

Following a major assessment of NASA's computing technology needs, a new program of computer science research has been initiated by the Agency. The program includes work in concurrent processing, management of large scale scientific databases, software engineering, reliable computing, and artificial intelligence. The program is driven by applications requirements in computational fluid dynamics, image processing, sensor data management, real-time mission control and autonomous systems. It consists of university research, in-house NASA research, and NASA's Research Institute for Advanced Computer Science (RIACS) and Institute for Computer Applications in Science and Engineering (ICASE). The overall goal is to provide the technical foundation within NASA to exploit advancing computing technology in aerospace applications.

Slow Invariant Manifolds in Chemically Reactive Systems

NASA Astrophysics Data System (ADS)

Paolucci, Samuel; Powers, Joseph M.

2006-11-01

The scientific design of practical gas phase combustion devices has come to rely on the use of mathematical models which include detailed chemical kinetics. Such models intrinsically admit a wide range of scales which renders their accurate numerical approximation difficult. Over the past decade, rational strategies, such as Intrinsic Low Dimensional Manifolds (ILDM) or Computational Singular Perturbations (CSP), for equilibrating fast time scale events have been successfully developed, though their computation can be challenging and their accuracy in most cases uncertain. Both are approximations to the preferable slow invariant manifold which best describes how the system evolves in the long time limit. Strategies for computing the slow invariant manifold are examined, and results are presented for practical combustion systems.

White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures

NASA Technical Reports Server (NTRS)

Denning, P. J.; Adams, G. B., III; Brown, R. L.; Kanerva, P.; Leiner, B. M.; Raugh, M. R.

1986-01-01

Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA.

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.

NASA Exhibits

NASA Technical Reports Server (NTRS)

Deardorff, Glenn; Djomehri, M. Jahed; Freeman, Ken; Gambrel, Dave; Green, Bryan; Henze, Chris; Hinke, Thomas; Hood, Robert; Kiris, Cetin; Moran, Patrick;

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

Development and Evaluation of Strong-Campbell Interest Inventory Scales to Measure Interests of Military Occupational Specialties of the Marine Corps.

DTIC Science & Technology

1982-08-01

though the two groups were different in terms of SC!I scientific interests and academic orientation scores (the aviation supply sample scored higher on...51 Chemists/Physicists 50 MARINE OFFICERS- COMUNICATION 49 MARINE OFFICERS-DATA SYSTEMS 48 Engineers 47 Biologists 46 Systems Analysts/Computer...Base ( Scientific and Technical Information Office) Commander, Air Force Human Resources Laboratory, Lowry Air Force Base (Technical Training Branch

Large Scale Computing and Storage Requirements for High Energy Physics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gerber, Richard A.; Wasserman, Harvey

2010-11-24

The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility for the Department of Energy's Office of Science, providing high-performance computing (HPC) resources to more than 3,000 researchers working on about 400 projects. NERSC provides large-scale computing resources and, crucially, the support and expertise needed for scientists to make effective use of them. In November 2009, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR), and DOE's Office of High Energy Physics (HEP) held a workshop to characterize the HPC resources needed at NERSC to support HEP research through the next three to five years. Themore » effort is part of NERSC's legacy of anticipating users needs and deploying resources to meet those demands. The workshop revealed several key points, in addition to achieving its goal of collecting and characterizing computing requirements. The chief findings: (1) Science teams need access to a significant increase in computational resources to meet their research goals; (2) Research teams need to be able to read, write, transfer, store online, archive, analyze, and share huge volumes of data; (3) Science teams need guidance and support to implement their codes on future architectures; and (4) Projects need predictable, rapid turnaround of their computational jobs to meet mission-critical time constraints. This report expands upon these key points and includes others. It also presents a number of case studies as representative of the research conducted within HEP. Workshop participants were asked to codify their requirements in this case study format, summarizing their science goals, methods of solution, current and three-to-five year computing requirements, and software and support needs. Participants were also asked to describe their strategy for computing in the highly parallel, multi-core environment that is expected to dominate HPC architectures over the next few years. The report includes a section that describes efforts already underway or planned at NERSC that address requirements collected at the workshop. NERSC has many initiatives in progress that address key workshop findings and are aligned with NERSC's strategic plans.« less

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

NASA Technical Reports Server (NTRS)

Tian, Yuan; Liu, Zhuo; Klasky, Scott; Wang, Bin; Abbasi, Hasan; Zhou, Shujia; Podhorszki, Norbert; Clune, Tom; Logan, Jeremy; Yu, Weikuan

2013-01-01

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method.

RELIABILITY, AVAILABILITY, AND SERVICEABILITY FOR PETASCALE HIGH-END COMPUTING AND BEYOND

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chokchai "Box" Leangsuksun

2011-05-31

Our project is a multi-institutional research effort that adopts interplay of RELIABILITY, AVAILABILITY, and SERVICEABILITY (RAS) aspects for solving resilience issues in highend scientific computing in the next generation of supercomputers. results lie in the following tracks: Failure prediction in a large scale HPC; Investigate reliability issues and mitigation techniques including in GPGPU-based HPC system; HPC resilience runtime & tools.

ArrayBridge: Interweaving declarative array processing with high-performance computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xing, Haoyuan; Floratos, Sofoklis; Blanas, Spyros

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aimsmore » to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.« less

Towards a Scalable and Adaptive Application Support Platform for Large-Scale Distributed E-Sciences in High-Performance Network Environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Chase Qishi; Zhu, Michelle Mengxia

The advent of large-scale collaborative scientific applications has demonstrated the potential for broad scientific communities to pool globally distributed resources to produce unprecedented data acquisition, movement, and analysis. System resources including supercomputers, data repositories, computing facilities, network infrastructures, storage systems, and display devices have been increasingly deployed at national laboratories and academic institutes. These resources are typically shared by large communities of users over Internet or dedicated networks and hence exhibit an inherent dynamic nature in their availability, accessibility, capacity, and stability. Scientific applications using either experimental facilities or computation-based simulations with various physical, chemical, climatic, and biological models featuremore » diverse scientific workflows as simple as linear pipelines or as complex as a directed acyclic graphs, which must be executed and supported over wide-area networks with massively distributed resources. Application users oftentimes need to manually configure their computing tasks over networks in an ad hoc manner, hence significantly limiting the productivity of scientists and constraining the utilization of resources. The success of these large-scale distributed applications requires a highly adaptive and massively scalable workflow platform that provides automated and optimized computing and networking services. This project is to design and develop a generic Scientific Workflow Automation and Management Platform (SWAMP), which contains a web-based user interface specially tailored for a target application, a set of user libraries, and several easy-to-use computing and networking toolkits for application scientists to conveniently assemble, execute, monitor, and control complex computing workflows in heterogeneous high-performance network environments. SWAMP will enable the automation and management of the entire process of scientific workflows with the convenience of a few mouse clicks while hiding the implementation and technical details from end users. Particularly, we will consider two types of applications with distinct performance requirements: data-centric and service-centric applications. For data-centric applications, the main workflow task involves large-volume data generation, catalog, storage, and movement typically from supercomputers or experimental facilities to a team of geographically distributed users; while for service-centric applications, the main focus of workflow is on data archiving, preprocessing, filtering, synthesis, visualization, and other application-specific analysis. We will conduct a comprehensive comparison of existing workflow systems and choose the best suited one with open-source code, a flexible system structure, and a large user base as the starting point for our development. Based on the chosen system, we will develop and integrate new components including a black box design of computing modules, performance monitoring and prediction, and workflow optimization and reconfiguration, which are missing from existing workflow systems. A modular design for separating specification, execution, and monitoring aspects will be adopted to establish a common generic infrastructure suited for a wide spectrum of science applications. We will further design and develop efficient workflow mapping and scheduling algorithms to optimize the workflow performance in terms of minimum end-to-end delay, maximum frame rate, and highest reliability. We will develop and demonstrate the SWAMP system in a local environment, the grid network, and the 100Gpbs Advanced Network Initiative (ANI) testbed. The demonstration will target scientific applications in climate modeling and high energy physics and the functions to be demonstrated include workflow deployment, execution, steering, and reconfiguration. Throughout the project period, we will work closely with the science communities in the fields of climate modeling and high energy physics including Spallation Neutron Source (SNS) and Large Hadron Collider (LHC) projects to mature the system for production use.« less

In Defense of the National Labs and Big-Budget Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goodwin, J R

2008-07-29

The purpose of this paper is to present the unofficial and unsanctioned opinions of a Visiting Scientist at Lawrence Livermore National Laboratory on the values of LLNL and the other National Labs. The basic founding value and goal of the National Labs is big-budget scientific research, along with smaller-budget scientific research that cannot easily be done elsewhere. The most important example in the latter category is classified defense-related research. The historical guiding light here is the Manhattan Project. This endeavor was unique in human history, and might remain so. The scientific expertise and wealth of an entire nation was tappedmore » in a project that was huge beyond reckoning, with no advance guarantee of success. It was in many respects a clash of scientific titans, with a large supporting cast, collaborating toward a single well-defined goal. Never had scientists received so much respect, so much money, and so much intellectual freedom to pursue scientific progress. And never was the gap between theory and implementation so rapidly narrowed, with results that changed the world, completely. Enormous resources are spent at the national or international level on large-scale scientific projects. LLNL has the most powerful computer in the world, Blue Gene/L. (Oops, Los Alamos just seized the title with Roadrunner; such titles regularly change hands.) LLNL also has the largest laser in the world, the National Ignition Facility (NIF). Lawrence Berkeley National Lab (LBNL) has the most powerful microscope in the world. Not only is it beyond the resources of most large corporations to make such expenditures, but the risk exceeds the possible rewards for those corporations that could. Nor can most small countries afford to finance large scientific projects, and not even the richest can afford largess, especially if Congress is under major budget pressure. Some big-budget research efforts are funded by international consortiums, such as the Large Hadron Collider (LHC) at CERN, and the International Tokamak Experimental Reactor (ITER) in Cadarache, France, a magnetic-confinement fusion research project. The postWWII histories of particle and fusion physics contain remarkable examples of both international competition, with an emphasis on secrecy, and international cooperation, with an emphasis on shared knowledge and resources. Initiatives to share sometimes came from surprising directions. Most large-scale scientific projects have potential defense applications. NIF certainly does; it is primarily designed to create small-scale fusion explosions. Blue Gene/L operates in part in service to NIF, and in part to various defense projects. The most important defense projects include stewardship of the national nuclear weapons stockpile, and the proposed redesign and replacement of those weapons with fewer, safer, more reliable, longer-lived, and less apocalyptic warheads. Many well-meaning people will consider the optimal lifetime of a nuclear weapon to be zero, but most thoughtful people, when asked how much longer they think this nation will require them, will ask for some time to think. NIF is also designed to create exothermic small-scale fusion explosions. The malapropos 'exothermic' here is a convenience to cover a profusion of complexities, but the basic idea is that the explosions will create more recoverable energy than was used to create them. One can hope that the primary future benefits of success for NIF will be in cost-effective generation of electrical power through controlled small-scale fusion reactions, rather than in improved large-scale fusion explosions. Blue Gene/L also services climate research, genomic research, materials research, and a myriad of other computational problems that become more feasible, reliable, and precise the larger the number of computational nodes employed. Blue Gene/L has to be sited within a security complex for obvious reasons, but its value extends to the nation and the world. There is a duality here between large-scale scientific research machines and the supercomputers used to model them. An astounding example is illustrated in a graph released by EFDAJET, at Oxfordshire, UK, presently the largest operating magnetic-confinement fusion experiment. The graph shows plasma confinement times (an essential performance parameter) for all the major tokamaks in the international fusion program, over their existing lifetimes. The remarkable thing about the data is not so much confinement-time versus date or scale, but the fact that the data are given for both the computer model predictions and the actual experimental measurements, and the two are in phenomenal agreement over the extended range of scales. Supercomputer models, sometimes operating with the intricacy of Schroedinger's equation at quantum physical scales, have become a costly but enormously cost-saving tool.« less

Key Lessons in Building "Data Commons": The Open Science Data Cloud Ecosystem

NASA Astrophysics Data System (ADS)

Patterson, M.; Grossman, R.; Heath, A.; Murphy, M.; Wells, W.

2015-12-01

Cloud computing technology has created a shift around data and data analysis by allowing researchers to push computation to data as opposed to having to pull data to an individual researcher's computer. Subsequently, cloud-based resources can provide unique opportunities to capture computing environments used both to access raw data in its original form and also to create analysis products which may be the source of data for tables and figures presented in research publications. Since 2008, the Open Cloud Consortium (OCC) has operated the Open Science Data Cloud (OSDC), which provides scientific researchers with computational resources for storing, sharing, and analyzing large (terabyte and petabyte-scale) scientific datasets. OSDC has provided compute and storage services to over 750 researchers in a wide variety of data intensive disciplines. Recently, internal users have logged about 2 million core hours each month. The OSDC also serves the research community by colocating these resources with access to nearly a petabyte of public scientific datasets in a variety of fields also accessible for download externally by the public. In our experience operating these resources, researchers are well served by "data commons," meaning cyberinfrastructure that colocates data archives, computing, and storage infrastructure and supports essential tools and services for working with scientific data. In addition to the OSDC public data commons, the OCC operates a data commons in collaboration with NASA and is developing a data commons for NOAA datasets. As cloud-based infrastructures for distributing and computing over data become more pervasive, we ask, "What does it mean to publish data in a data commons?" Here we present the OSDC perspective and discuss several services that are key in architecting data commons, including digital identifier services.

Computational Challenges in the Analysis of Petrophysics Using Microtomography and Upscaling

NASA Astrophysics Data System (ADS)

Liu, J.; Pereira, G.; Freij-Ayoub, R.; Regenauer-Lieb, K.

2014-12-01

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.

Automated metadata--final project report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schissel, David

This report summarizes the work of the Automated Metadata, Provenance Cataloging, and Navigable Interfaces: Ensuring the Usefulness of Extreme-Scale Data Project (MPO Project) funded by the United States Department of Energy (DOE), Offices of Advanced Scientific Computing Research and Fusion Energy Sciences. Initially funded for three years starting in 2012, it was extended for 6 months with additional funding. The project was a collaboration between scientists at General Atomics, Lawrence Berkley National Laboratory (LBNL), and Massachusetts Institute of Technology (MIT). The group leveraged existing computer science technology where possible, and extended or created new capabilities where required. The MPO projectmore » was able to successfully create a suite of software tools that can be used by a scientific community to automatically document their scientific workflows. These tools were integrated into workflows for fusion energy and climate research illustrating the general applicability of the project’s toolkit. Feedback was very positive on the project’s toolkit and the value of such automatic workflow documentation to the scientific endeavor.« less

Mira: Argonne's 10-petaflops supercomputer

ScienceCinema

Papka, Michael; Coghlan, Susan; Isaacs, Eric; Peters, Mark; Messina, Paul

2018-02-13

Mira, Argonne's petascale IBM Blue Gene/Q system, ushers in a new era of scientific supercomputing at the Argonne Leadership Computing Facility. An engineering marvel, the 10-petaflops supercomputer is capable of carrying out 10 quadrillion calculations per second. As a machine for open science, any researcher with a question that requires large-scale computing resources can submit a proposal for time on Mira, typically in allocations of millions of core-hours, to run programs for their experiments. This adds up to billions of hours of computing time per year.

Mira: Argonne's 10-petaflops supercomputer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Papka, Michael; Coghlan, Susan; Isaacs, Eric

2013-07-03

Mira, Argonne's petascale IBM Blue Gene/Q system, ushers in a new era of scientific supercomputing at the Argonne Leadership Computing Facility. An engineering marvel, the 10-petaflops supercomputer is capable of carrying out 10 quadrillion calculations per second. As a machine for open science, any researcher with a question that requires large-scale computing resources can submit a proposal for time on Mira, typically in allocations of millions of core-hours, to run programs for their experiments. This adds up to billions of hours of computing time per year.

Scalable Automated Model Search

DTIC Science & Technology

2014-05-20

ma- chines. Categories and Subject Descriptors Big Data [Distributed Computing]: Large scale optimization 1. INTRODUCTION Modern scientific and...from Continuum Analytics[1], and Apache Spark 0.8.1. Additionally, we made use of Hadoop 1.0.4 configured on local disks as our data store for the large...Borkar et al. Hyracks: A flexible and extensible foundation for data -intensive computing. In ICDE, 2011. [16] J. Canny and H. Zhao. Big data

Software engineering and automatic continuous verification of scientific software

NASA Astrophysics Data System (ADS)

Piggott, M. D.; Hill, J.; Farrell, P. E.; Kramer, S. C.; Wilson, C. R.; Ham, D.; Gorman, G. J.; Bond, T.

2011-12-01

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.

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE PAGES

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian; ...

2017-09-29

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

A Power Efficient Exaflop Computer Design for Global Cloud System Resolving Climate Models.

NASA Astrophysics Data System (ADS)

Wehner, M. F.; Oliker, L.; Shalf, J.

2008-12-01

Exascale computers would allow routine ensemble modeling of the global climate system at the cloud system resolving scale. Power and cost requirements of traditional architecture systems are likely to delay such capability for many years. We present an alternative route to the exascale using embedded processor technology to design a system optimized for ultra high resolution climate modeling. These power efficient processors, used in consumer electronic devices such as mobile phones, portable music players, cameras, etc., can be tailored to the specific needs of scientific computing. We project that a system capable of integrating a kilometer scale climate model a thousand times faster than real time could be designed and built in a five year time scale for US$75M with a power consumption of 3MW. This is cheaper, more power efficient and sooner than any other existing technology.

Parallel Scaling Characteristics of Selected NERSC User ProjectCodes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Skinner, David; Verdier, Francesca; Anand, Harsh

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

BioPig: a Hadoop-based analytic toolkit for large-scale sequence data.

PubMed

Nordberg, Henrik; Bhatia, Karan; Wang, Kai; Wang, Zhong

2013-12-01

The recent revolution in sequencing technologies has led to an exponential growth of sequence data. As a result, most of the current bioinformatics tools become obsolete as they fail to scale with data. To tackle this 'data deluge', here we introduce the BioPig sequence analysis toolkit as one of the solutions that scale to data and computation. We built BioPig on the Apache's Hadoop MapReduce system and the Pig data flow language. Compared with traditional serial and MPI-based algorithms, BioPig has three major advantages: first, BioPig's programmability greatly reduces development time for parallel bioinformatics applications; second, testing BioPig with up to 500 Gb sequences demonstrates that it scales automatically with size of data; and finally, BioPig can be ported without modification on many Hadoop infrastructures, as tested with Magellan system at National Energy Research Scientific Computing Center and the Amazon Elastic Compute Cloud. In summary, BioPig represents a novel program framework with the potential to greatly accelerate data-intensive bioinformatics analysis.

Architectural Strategies for Enabling Data-Driven Science at Scale

NASA Astrophysics Data System (ADS)

Crichton, D. J.; Law, E. S.; Doyle, R. J.; Little, M. M.

2017-12-01

The analysis of large data collections from NASA or other agencies is often executed through traditional computational and data analysis approaches, which require users to bring data to their desktops and perform local data analysis. Alternatively, data are hauled to large computational environments that provide centralized data analysis via traditional High Performance Computing (HPC). Scientific data archives, however, are not only growing massive, but are also becoming highly distributed. Neither traditional approach provides a good solution for optimizing analysis into the future. Assumptions across the NASA mission and science data lifecycle, which historically assume that all data can be collected, transmitted, processed, and archived, will not scale as more capable instruments stress legacy-based systems. New paradigms are needed to increase the productivity and effectiveness of scientific data analysis. This paradigm must recognize that architectural and analytical choices are interrelated, and must be carefully coordinated in any system that aims to allow efficient, interactive scientific exploration and discovery to exploit massive data collections, from point of collection (e.g., onboard) to analysis and decision support. The most effective approach to analyzing a distributed set of massive data may involve some exploration and iteration, putting a premium on the flexibility afforded by the architectural framework. The framework should enable scientist users to assemble workflows efficiently, manage the uncertainties related to data analysis and inference, and optimize deep-dive analytics to enhance scalability. In many cases, this "data ecosystem" needs to be able to integrate multiple observing assets, ground environments, archives, and analytics, evolving from stewardship of measurements of data to using computational methodologies to better derive insight from the data that may be fused with other sets of data. This presentation will discuss architectural strategies, including a 2015-2016 NASA AIST Study on Big Data, for evolving scientific research towards massively distributed data-driven discovery. It will include example use cases across earth science, planetary science, and other disciplines.

Large Spatial Scale Ground Displacement Mapping through the P-SBAS Processing of Sentinel-1 Data on a Cloud Computing Environment

NASA Astrophysics Data System (ADS)

Casu, F.; Bonano, M.; de Luca, C.; Lanari, R.; Manunta, M.; Manzo, M.; Zinno, I.

2017-12-01

Since its launch in 2014, the Sentinel-1 (S1) constellation has played a key role on SAR data availability and dissemination all over the World. Indeed, the free and open access data policy adopted by the European Copernicus program together with the global coverage acquisition strategy, make the Sentinel constellation as a game changer in the Earth Observation scenario. Being the SAR data become ubiquitous, the technological and scientific challenge is focused on maximizing the exploitation of such huge data flow. In this direction, the use of innovative processing algorithms and distributed computing infrastructures, such as the Cloud Computing platforms, can play a crucial role. In this work we present a Cloud Computing solution for the advanced interferometric (DInSAR) processing chain based on the Parallel SBAS (P-SBAS) approach, aimed at processing S1 Interferometric Wide Swath (IWS) data for the generation of large spatial scale deformation time series in efficient, automatic and systematic way. Such a DInSAR chain ingests Sentinel 1 SLC images and carries out several processing steps, to finally compute deformation time series and mean deformation velocity maps. Different parallel strategies have been designed ad hoc for each processing step of the P-SBAS S1 chain, encompassing both multi-core and multi-node programming techniques, in order to maximize the computational efficiency achieved within a Cloud Computing environment and cut down the relevant processing times. The presented P-SBAS S1 processing chain has been implemented on the Amazon Web Services platform and a thorough analysis of the attained parallel performances has been performed to identify and overcome the major bottlenecks to the scalability. The presented approach is used to perform national-scale DInSAR analyses over Italy, involving the processing of more than 3000 S1 IWS images acquired from both ascending and descending orbits. Such an experiment confirms the big advantage of exploiting large computational and storage resources of Cloud Computing platforms for large scale DInSAR analysis. The presented Cloud Computing P-SBAS processing chain can be a precious tool in the perspective of developing operational services disposable for the EO scientific community related to hazard monitoring and risk prevention and mitigation.

Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Samuel; Oliker, Leonid; Vuduc, Richard

2008-10-16

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific-optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD quad-core, AMD dual-core, and Intel quad-core designs, the heterogeneous STI Cell, as well as one ofmore » the first scientific studies of the highly multithreaded Sun Victoria Falls (a Niagara2 SMP). We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural trade-offs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.« less

Modeling Subsurface Reactive Flows Using Leadership-Class Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, Richard T; Hammond, Glenn; Lichtner, Peter

2009-01-01

We describe our experiences running PFLOTRAN - a code for simulation of coupled hydro-thermal-chemical processes in variably saturated, non-isothermal, porous media - on leadership-class supercomputers, including initial experiences running on the petaflop incarnation of Jaguar, the Cray XT5 at the National Center for Computational Sciences at Oak Ridge National Laboratory. PFLOTRAN utilizes fully implicit time-stepping and is built on top of the Portable, Extensible Toolkit for Scientific Computation (PETSc). We discuss some of the hurdles to 'at scale' performance with PFLOTRAN and the progress we have made in overcoming them on leadership-class computer architectures.

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

NASA Astrophysics Data System (ADS)

Pallant, Amy; Lee, Hee-Sun

2015-04-01

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 tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garzoglio, Gabriele

The Fermilab Grid and Cloud Computing Department and the KISTI Global Science experimental Data hub Center are working on a multi-year Collaborative Research and Development Agreement.With the knowledge developed in the first year on how to provision and manage a federation of virtual machines through Cloud management systems. In this second year, we expanded the work on provisioning and federation, increasing both scale and diversity of solutions, and we started to build on-demand services on the established fabric, introducing the paradigm of Platform as a Service to assist with the execution of scientific workflows. We have enabled scientific workflows ofmore » stakeholders to run on multiple cloud resources at the scale of 1,000 concurrent machines. The demonstrations have been in the areas of (a) Virtual Infrastructure Automation and Provisioning, (b) Interoperability and Federation of Cloud Resources, and (c) On-demand Services for ScientificWorkflows.« less

Final Technical Report - Center for Technology for Advanced Scientific Component Software (TASCS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sussman, Alan

2014-10-21

This is a final technical report for the University of Maryland work in the SciDAC Center for Technology for Advanced Scientific Component Software (TASCS). The Maryland work focused on software tools for coupling parallel software components built using the Common Component Architecture (CCA) APIs. Those tools are based on the Maryland InterComm software framework that has been used in multiple computational science applications to build large-scale simulations of complex physical systems that employ multiple separately developed codes.

A Disciplined Architectural Approach to Scaling Data Analysis for Massive, Scientific Data

NASA Astrophysics Data System (ADS)

Crichton, D. J.; Braverman, A. J.; Cinquini, L.; Turmon, M.; Lee, H.; Law, E.

2014-12-01

Data collections across remote sensing and ground-based instruments in astronomy, Earth science, and planetary science are outpacing scientists' ability to analyze them. Furthermore, the distribution, structure, and heterogeneity of the measurements themselves pose challenges that limit the scalability of data analysis using traditional approaches. Methods for developing science data processing pipelines, distribution of scientific datasets, and performing analysis will require innovative approaches that integrate cyber-infrastructure, algorithms, and data into more systematic approaches that can more efficiently compute and reduce data, particularly distributed data. This requires the integration of computer science, machine learning, statistics and domain expertise to identify scalable architectures for data analysis. The size of data returned from Earth Science observing satellites and the magnitude of data from climate model output, is predicted to grow into the tens of petabytes challenging current data analysis paradigms. This same kind of growth is present in astronomy and planetary science data. One of the major challenges in data science and related disciplines defining new approaches to scaling systems and analysis in order to increase scientific productivity and yield. Specific needs include: 1) identification of optimized system architectures for analyzing massive, distributed data sets; 2) algorithms for systematic analysis of massive data sets in distributed environments; and 3) the development of software infrastructures that are capable of performing massive, distributed data analysis across a comprehensive data science framework. NASA/JPL has begun an initiative in data science to address these challenges. Our goal is to evaluate how scientific productivity can be improved through optimized architectural topologies that identify how to deploy and manage the access, distribution, computation, and reduction of massive, distributed data, while managing the uncertainties of scientific conclusions derived from such capabilities. This talk will provide an overview of JPL's efforts in developing a comprehensive architectural approach to data science.

Managing large-scale workflow execution from resource provisioning to provenance tracking: The CyberShake example

USGS Publications Warehouse

Deelman, E.; Callaghan, S.; Field, E.; Francoeur, H.; Graves, R.; Gupta, N.; Gupta, V.; Jordan, T.H.; Kesselman, C.; Maechling, P.; Mehringer, J.; Mehta, G.; Okaya, D.; Vahi, K.; Zhao, L.

2006-01-01

This paper discusses the process of building an environment where large-scale, complex, scientific analysis can be scheduled onto a heterogeneous collection of computational and storage resources. The example application is the Southern California Earthquake Center (SCEC) CyberShake project, an analysis designed to compute probabilistic seismic hazard curves for sites in the Los Angeles area. We explain which software tools were used to build to the system, describe their functionality and interactions. We show the results of running the CyberShake analysis that included over 250,000 jobs using resources available through SCEC and the TeraGrid. ?? 2006 IEEE.

Science & Technology Review: September 2016

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vogt, Ramona L.; Meissner, Caryn N.; Chinn, Ken B.

2016-09-30

This is the September issue of the Lawrence Livermore National Laboratory's Science & Technology Review, which communicates, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. This month, there are features on "Laboratory Investments Drive Computational Advances" and "Laying the Groundwork for Extreme-Scale Computing." Research highlights include "Nuclear Data Moves into the 21st Century", "Peering into the Future of Lick Observatory", and "Facility Drives Hydrogen Vehicle Innovations."

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sewell, Christopher Meyer

This is a set of slides from a guest lecture for a class at the University of Texas, El Paso on visualization and data analysis for high-performance computing. The topics covered are the following: trends in high-performance computing; scientific visualization, such as OpenGL, ray tracing and volume rendering, VTK, and ParaView; data science at scale, such as in-situ visualization, image databases, distributed memory parallelism, shared memory parallelism, VTK-m, "big data", and then an analysis example.

5 CFR 9301.7 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... and that operates solely for the purpose of conducting scientific research the results of which are... employees who perform the work and costs of conducting large-scale computer searches. (c) Duplicate means to... education, that operates a program or programs of scholarly research. (e) Fee category means one of the...

5 CFR 9301.7 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... and that operates solely for the purpose of conducting scientific research the results of which are... employees who perform the work and costs of conducting large-scale computer searches. (c) Duplicate means to... education, that operates a program or programs of scholarly research. (e) Fee category means one of the...

Pynamic: the Python Dynamic Benchmark

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, G L; Ahn, D H; de Supinksi, B R

2007-07-10

Python is widely used in scientific computing to facilitate application development and to support features such as computational steering. Making full use of some of Python's popular features, which improve programmer productivity, leads to applications that access extremely high numbers of dynamically linked libraries (DLLs). As a result, some important Python-based applications severely stress a system's dynamic linking and loading capabilities and also cause significant difficulties for most development environment tools, such as debuggers. Furthermore, using the Python paradigm for large scale MPI-based applications can create significant file IO and further stress tools and operating systems. In this paper, wemore » present Pynamic, the first benchmark program to support configurable emulation of a wide-range of the DLL usage of Python-based applications for large scale systems. Pynamic has already accurately reproduced system software and tool issues encountered by important large Python-based scientific applications on our supercomputers. Pynamic provided insight for our system software and tool vendors, and our application developers, into the impact of several design decisions. As we describe the Pynamic benchmark, we will highlight some of the issues discovered in our large scale system software and tools using Pynamic.« less

Review of An Introduction to Parallel and Vector Scientific Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bailey, David H.; Lefton, Lew

2006-06-30

On one hand, the field of high-performance scientific computing is thriving beyond measure. Performance of leading-edge systems on scientific calculations, as measured say by the Top500 list, has increased by an astounding factor of 8000 during the 15-year period from 1993 to 2008, which is slightly faster even than Moore's Law. Even more importantly, remarkable advances in numerical algorithms, numerical libraries and parallel programming environments have led to improvements in the scope of what can be computed that are entirely on a par with the advances in computing hardware. And these successes have spread far beyond the confines of largemore » government-operated laboratories, many universities, modest-sized research institutes and private firms now operate clusters that differ only in scale from the behemoth systems at the large-scale facilities. In the wake of these recent successes, researchers from fields that heretofore have not been part of the scientific computing world have been drawn into the arena. For example, at the recent SC07 conference, the exhibit hall, which long has hosted displays from leading computer systems vendors and government laboratories, featured some 70 exhibitors who had not previously participated. In spite of all these exciting developments, and in spite of the clear need to present these concepts to a much broader technical audience, there is a perplexing dearth of training material and textbooks in the field, particularly at the introductory level. Only a handful of universities offer coursework in the specific area of highly parallel scientific computing, and instructors of such courses typically rely on custom-assembled material. For example, the present reviewer and Robert F. Lucas relied on materials assembled in a somewhat ad-hoc fashion from colleagues and personal resources when presenting a course on parallel scientific computing at the University of California, Berkeley, a few years ago. Thus it is indeed refreshing to see the publication of the book An Introduction to Parallel and Vector Scientic Computing, written by Ronald W. Shonkwiler and Lew Lefton, both of the Georgia Institute of Technology. They have taken the bull by the horns and produced a book that appears to be entirely satisfactory as an introductory textbook for use in such a course. It is also of interest to the much broader community of researchers who are already in the field, laboring day by day to improve the power and performance of their numerical simulations. The book is organized into 11 chapters, plus an appendix. The first three chapters describe the basics of system architecture including vector, parallel and distributed memory systems, the details of task dependence and synchronization, and the various programming models currently in use - threads, MPI and OpenMP. Chapters four through nine provide a competent introduction to floating-point arithmetic, numerical error and numerical linear algebra. Some of the topics presented include Gaussian elimination, LU decomposition, tridiagonal systems, Givens rotations, QR decompositions, Gauss-Seidel iterations and Householder transformations. Chapters 10 and 11 introduce Monte Carlo methods and schemes for discrete optimization such as genetic algorithms.« less

An Operational System for Subject Switching between Controlled Vocabularies: A Computational Linguistics Approach.

ERIC Educational Resources Information Center

Silvester, June P.; And Others

This report describes a new automated process that pioneers full-scale operational use of subject switching by the NASA (National Aeronautics and Space Administration) Scientific and Technical Information (STI) Facility. The subject switching process routinely translates machine-readable subject terms from one controlled vocabulary into the…

Scalable Kernel Methods and Algorithms for General Sequence Analysis

ERIC Educational Resources Information Center

Kuksa, Pavel

2011-01-01

Analysis of large-scale sequential data has become an important task in machine learning and pattern recognition, inspired in part by numerous scientific and technological applications such as the document and text classification or the analysis of biological sequences. However, current computational methods for sequence comparison still lack…

Science in the cloud (SIC): A use case in MRI connectomics

PubMed Central

Gorgolewski, Krzysztof J.; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian; Poldrack, Russel A.; Wiener, Martin; Vogelstein, R. Jacob; Burns, Randal

2017-01-01

Abstract Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. PMID:28327935

Science in the cloud (SIC): A use case in MRI connectomics.

PubMed

Kiar, Gregory; Gorgolewski, Krzysztof J; Kleissas, Dean; Roncal, William Gray; Litt, Brian; Wandell, Brian; Poldrack, Russel A; Wiener, Martin; Vogelstein, R Jacob; Burns, Randal; Vogelstein, Joshua T

2017-05-01

Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called 'science in the cloud' (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. © The Author 2017. Published by Oxford University Press.

The Quantitative Analysis of User Behavior Online - Data, Models and Algorithms

NASA Astrophysics Data System (ADS)

Raghavan, Prabhakar

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.

The computational challenges of Earth-system science.

PubMed

O'Neill, Alan; Steenman-Clark, Lois

2002-06-15

The Earth system--comprising atmosphere, ocean, land, cryosphere and biosphere--is an immensely complex system, involving processes and interactions on a wide range of space- and time-scales. To understand and predict the evolution of the Earth system is one of the greatest challenges of modern science, with success likely to bring enormous societal benefits. High-performance computing, along with the wealth of new observational data, is revolutionizing our ability to simulate the Earth system with computer models that link the different components of the system together. There are, however, considerable scientific and technical challenges to be overcome. This paper will consider four of them: complexity, spatial resolution, inherent uncertainty and time-scales. Meeting these challenges requires a significant increase in the power of high-performance computers. The benefits of being able to make reliable predictions about the evolution of the Earth system should, on their own, amply repay this investment.

Scalable parallel distance field construction for large-scale applications

DOE PAGES

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan -Liu; ...

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. Anew distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking overtime, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate itsmore » efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. In conclusion, our work greatly extends the usability of distance fields for demanding applications.« less

Template Interfaces for Agile Parallel Data-Intensive Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramakrishnan, Lavanya; Gunter, Daniel; Pastorello, Gilerto Z.

Tigres provides a programming library to compose and execute large-scale data-intensive scientific workflows from desktops to supercomputers. DOE User Facilities and large science collaborations are increasingly generating large enough data sets that it is no longer practical to download them to a desktop to operate on them. They are instead stored at centralized compute and storage resources such as high performance computing (HPC) centers. Analysis of this data requires an ability to run on these facilities, but with current technologies, scaling an analysis to an HPC center and to a large data set is difficult even for experts. Tigres ismore » addressing the challenge of enabling collaborative analysis of DOE Science data through a new concept of reusable "templates" that enable scientists to easily compose, run and manage collaborative computational tasks. These templates define common computation patterns used in analyzing a data set.« less

Scalable Parallel Distance Field Construction for Large-Scale Applications.

PubMed

Yu, Hongfeng; Xie, Jinrong; Ma, Kwan-Liu; Kolla, Hemanth; Chen, Jacqueline H

2015-10-01

Computing distance fields is fundamental to many scientific and engineering applications. Distance fields can be used to direct analysis and reduce data. In this paper, we present a highly scalable method for computing 3D distance fields on massively parallel distributed-memory machines. A new distributed spatial data structure, named parallel distance tree, is introduced to manage the level sets of data and facilitate surface tracking over time, resulting in significantly reduced computation and communication costs for calculating the distance to the surface of interest from any spatial locations. Our method supports several data types and distance metrics from real-world applications. We demonstrate its efficiency and scalability on state-of-the-art supercomputers using both large-scale volume datasets and surface models. We also demonstrate in-situ distance field computation on dynamic turbulent flame surfaces for a petascale combustion simulation. Our work greatly extends the usability of distance fields for demanding applications.

Institute for scientific computing research;fiscal year 1999 annual report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keyes, D

2000-03-28

Large-scale scientific computation, and all of the disciplines that support it and help to validate it, have been placed at the focus of Lawrence Livermore National Laboratory by the Accelerated Strategic Computing Initiative (ASCI). The Laboratory operates the computer with the highest peak performance in the world and has undertaken some of the largest and most compute-intensive simulations ever performed. Computers at the architectural extremes, however, are notoriously difficult to use efficiently. Even such successes as the Laboratory's two Bell Prizes awarded in November 1999 only emphasize the need for much better ways of interacting with the results of large-scalemore » simulations. Advances in scientific computing research have, therefore, never been more vital to the core missions of the Laboratory than at present. Computational science is evolving so rapidly along every one of its research fronts that to remain on the leading edge, the Laboratory must engage researchers at many academic centers of excellence. In FY 1999, the Institute for Scientific Computing Research (ISCR) has expanded the Laboratory's bridge to the academic community in the form of collaborative subcontracts, visiting faculty, student internships, a workshop, and a very active seminar series. ISCR research participants are integrated almost seamlessly with the Laboratory's Center for Applied Scientific Computing (CASC), which, in turn, addresses computational challenges arising throughout the Laboratory. Administratively, the ISCR flourishes under the Laboratory's University Relations Program (URP). Together with the other four Institutes of the URP, it must navigate a course that allows the Laboratory to benefit from academic exchanges while preserving national security. Although FY 1999 brought more than its share of challenges to the operation of an academic-like research enterprise within the context of a national security laboratory, the results declare the challenges well met and well worth the continued effort. A change of administration for the ISCR occurred during FY 1999. Acting Director John Fitzgerald retired from LLNL in August after 35 years of service, including the last two at helm of the ISCR. David Keyes, who has been a regular visitor in conjunction with ASCI scalable algorithms research since October 1997, overlapped with John for three months and serves half-time as the new Acting Director.« less

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

DOE PAGES

Hendrix, Valerie; Fox, James; Ghoshal, Devarshi; ...

2016-07-21

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterativemore » workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.« less

Tigres Workflow Library: Supporting Scientific Pipelines on HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hendrix, Valerie; Fox, James; Ghoshal, Devarshi

The growth in scientific data volumes has resulted in the need for new tools that enable users to operate on and analyze data on large-scale resources. In the last decade, a number of scientific workflow tools have emerged. These tools often target distributed environments, and often need expert help to compose and execute the workflows. Data-intensive workflows are often ad-hoc, they involve an iterative development process that includes users composing and testing their workflows on desktops, and scaling up to larger systems. In this paper, we present the design and implementation of Tigres, a workflow library that supports the iterativemore » workflow development cycle of data-intensive workflows. Tigres provides an application programming interface to a set of programming templates i.e., sequence, parallel, split, merge, that can be used to compose and execute computational and data pipelines. We discuss the results of our evaluation of scientific and synthetic workflows showing Tigres performs with minimal template overheads (mean of 13 seconds over all experiments). We also discuss various factors (e.g., I/O performance, execution mechanisms) that affect the performance of scientific workflows on HPC systems.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE PAGES

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De; ...

2017-01-28

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bicer, Tekin; Gursoy, Doga; Andrade, Vincent De

Here, synchrotron light source and detector technologies enable scientists to perform advanced experiments. These scientific instruments and experiments produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used data acquisition technique at light sources is Computed Tomography, which can generate tens of GB/s depending on x-ray range. A large-scale tomographic dataset, such as mouse brain, may require hours of computation time with a medium size workstation. In this paper, we present Trace, a data-intensive computing middleware we developed for implementation and parallelization of iterative tomographic reconstruction algorithms. Tracemore » provides fine-grained reconstruction of tomography datasets using both (thread level) shared memory and (process level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations we have done on the replicated reconstruction objects and evaluate them using a shale and a mouse brain sinogram. Our experimental evaluations show that the applied optimizations and parallelization techniques can provide 158x speedup (using 32 compute nodes) over single core configuration, which decreases the reconstruction time of a sinogram (with 4501 projections and 22400 detector resolution) from 12.5 hours to less than 5 minutes per iteration.« less

Managing data from multiple disciplines, scales, and sites to support synthesis and modeling

USGS Publications Warehouse

Olson, R. J.; Briggs, J. M.; Porter, J.H.; Mah, Grant R.; Stafford, S.G.

1999-01-01

The synthesis and modeling of ecological processes at multiple spatial and temporal scales involves bringing together and sharing data from numerous sources. This article describes a data and information system model that facilitates assembling, managing, and sharing diverse data from multiple disciplines, scales, and sites to support integrated ecological studies. Cross-site scientific-domain working groups coordinate the development of data associated with their particular scientific working group, including decisions about data requirements, data to be compiled, data formats, derived data products, and schedules across the sites. The Web-based data and information system consists of nodes for each working group plus a central node that provides data access, project information, data query, and other functionality. The approach incorporates scientists and computer experts in the working groups and provides incentives for individuals to submit documented data to the data and information system.

THE VIRTUAL INSTRUMENT: SUPPORT FOR GRID-ENABLED MCELL SIMULATIONS

PubMed Central

Casanova, Henri; Berman, Francine; Bartol, Thomas; Gokcay, Erhan; Sejnowski, Terry; Birnbaum, Adam; Dongarra, Jack; Miller, Michelle; Ellisman, Mark; Faerman, Marcio; Obertelli, Graziano; Wolski, Rich; Pomerantz, Stuart; Stiles, Joel

2010-01-01

Ensembles of widely distributed, heterogeneous resources, or Grids, have emerged as popular platforms for large-scale scientific applications. In this paper we present the Virtual Instrument project, which provides an integrated application execution environment that enables end-users to run and interact with running scientific simulations on Grids. This work is performed in the specific context of MCell, a computational biology application. While MCell provides the basis for running simulations, its capabilities are currently limited in terms of scale, ease-of-use, and interactivity. These limitations preclude usage scenarios that are critical for scientific advances. Our goal is to create a scientific “Virtual Instrument” from MCell by allowing its users to transparently access Grid resources while being able to steer running simulations. In this paper, we motivate the Virtual Instrument project and discuss a number of relevant issues and accomplishments in the area of Grid software development and application scheduling. We then describe our software design and report on the current implementation. We verify and evaluate our design via experiments with MCell on a real-world Grid testbed. PMID:20689618

ROSE Version 1.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quinlan, D.; Yi, Q.; Buduc, R.

2005-02-17

ROSE is an object-oriented software infrastructure for source-to-source translation that provides an interface for programmers to write their own specialized translators for optimizing scientific applications. ROSE is a part of current research on telescoping languages, which provides optimizations of the use of libraries in scientific applications. ROSE defines approaches to extend the optimization techniques, common in well defined languages, to the optimization of scientific applications using well defined libraries. ROSE includes a rich set of tools for generating customized transformations to support optimization of applications codes. We currently support full C and C++ (including template instantiation etc.), with Fortran 90more » support under development as part of a collaboration and contract with Rice to use their version of the open source Open64 F90 front-end. ROSE represents an attempt to define an open compiler infrastructure to handle the full complexity of full scale DOE applications codes using the languages common to scientific computing within DOE. We expect that such an infrastructure will also be useful for the development of numerous tools that may then realistically expect to work on DOE full scale applications.« less

Computational Prediction of Protein-Protein Interactions

PubMed Central

Ehrenberger, Tobias; Cantley, Lewis C.; Yaffe, Michael B.

2015-01-01

The prediction of protein-protein interactions and kinase-specific phosphorylation sites on individual proteins is critical for correctly placing proteins within signaling pathways and networks. The importance of this type of annotation continues to increase with the continued explosion of genomic and proteomic data, particularly with emerging data categorizing posttranslational modifications on a large scale. A variety of computational tools are available for this purpose. In this chapter, we review the general methodologies for these types of computational predictions and present a detailed user-focused tutorial of one such method and computational tool, Scansite, which is freely available to the entire scientific community over the Internet. PMID:25859943

Computational perspectives in the history of science: to the memory of Peter Damerow.

PubMed

Laubichler, Manfred D; Maienschein, Jane; Renn, Jürgen

2013-03-01

Computational methods and perspectives can transform the history of science by enabling the pursuit of novel types of questions, dramatically expanding the scale of analysis (geographically and temporally), and offering novel forms of publication that greatly enhance access and transparency. This essay presents a brief summary of a computational research system for the history of science, discussing its implications for research, education, and publication practices and its connections to the open-access movement and similar transformations in the natural and social sciences that emphasize big data. It also argues that computational approaches help to reconnect the history of science to individual scientific disciplines.

ISCR Annual Report: Fical Year 2004

DOE Office of Scientific and Technical Information (OSTI.GOV)

McGraw, J R

2005-03-03

Large-scale scientific computation and all of the disciplines that support and help to validate it have been placed at the focus of Lawrence Livermore National Laboratory (LLNL) by the Advanced Simulation and Computing (ASC) program of the National Nuclear Security Administration (NNSA) and the Scientific Discovery through Advanced Computing (SciDAC) initiative of the Office of Science of the Department of Energy (DOE). The maturation of computational simulation as a tool of scientific and engineering research is underscored in the November 2004 statement of the Secretary of Energy that, ''high performance computing is the backbone of the nation's science and technologymore » enterprise''. LLNL operates several of the world's most powerful computers--including today's single most powerful--and has undertaken some of the largest and most compute-intensive simulations ever performed. Ultrascale simulation has been identified as one of the highest priorities in DOE's facilities planning for the next two decades. However, computers at architectural extremes are notoriously difficult to use efficiently. Furthermore, each successful terascale simulation only points out the need for much better ways of interacting with the resulting avalanche of data. Advances in scientific computing research have, therefore, never been more vital to LLNL's core missions than at present. Computational science is evolving so rapidly along every one of its research fronts that to remain on the leading edge, LLNL must engage researchers at many academic centers of excellence. In Fiscal Year 2004, the Institute for Scientific Computing Research (ISCR) served as one of LLNL's main bridges to the academic community with a program of collaborative subcontracts, visiting faculty, student internships, workshops, and an active seminar series. The ISCR identifies researchers from the academic community for computer science and computational science collaborations with LLNL and hosts them for short- and long-term visits with the aim of encouraging long-term academic research agendas that address LLNL's research priorities. Through such collaborations, ideas and software flow in both directions, and LLNL cultivates its future workforce. The Institute strives to be LLNL's ''eyes and ears'' in the computer and information sciences, keeping the Laboratory aware of and connected to important external advances. It also attempts to be the ''feet and hands'' that carry those advances into the Laboratory and incorporates them into practice. ISCR research participants are integrated into LLNL's Computing and Applied Research (CAR) Department, especially into its Center for Applied Scientific Computing (CASC). In turn, these organizations address computational challenges arising throughout the rest of the Laboratory. Administratively, the ISCR flourishes under LLNL's University Relations Program (URP). Together with the other five institutes of the URP, it navigates a course that allows LLNL to benefit from academic exchanges while preserving national security. While it is difficult to operate an academic-like research enterprise within the context of a national security laboratory, the results declare the challenges well met and worth the continued effort.« less

Computing at the speed limit (supercomputers)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernhard, R.

1982-07-01

The author discusses how unheralded efforts in the United States, mainly in universities, have removed major stumbling blocks to building cost-effective superfast computers for scientific and engineering applications within five years. These computers would have sustained speeds of billions of floating-point operations per second (flops), whereas with the fastest machines today the top sustained speed is only 25 million flops, with bursts to 160 megaflops. Cost-effective superfast machines can be built because of advances in very large-scale integration and the special software needed to program the new machines. VLSI greatly reduces the cost per unit of computing power. The developmentmore » of such computers would come at an opportune time. Although the US leads the world in large-scale computer technology, its supremacy is now threatened, not surprisingly, by the Japanese. Publicized reports indicate that the Japanese government is funding a cooperative effort by commercial computer manufacturers to develop superfast computers-about 1000 times faster than modern supercomputers. The US computer industry, by contrast, has balked at attempting to boost computer power so sharply because of the uncertain market for the machines and the failure of similar projects in the past to show significant results.« less

76 FR 41234 - Advanced Scientific Computing Advisory Committee Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-13

... 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...

NAS (Numerical Aerodynamic Simulation Program) technical summaries, March 1989 - February 1990

NASA Technical Reports Server (NTRS)

1990-01-01

Given here are selected scientific results from the Numerical Aerodynamic Simulation (NAS) Program's third year of operation. During this year, the scientific community was given access to a Cray-2 and a Cray Y-MP supercomputer. Topics covered include flow field analysis of fighter wing configurations, large-scale ocean modeling, the Space Shuttle flow field, advanced computational fluid dynamics (CFD) codes for rotary-wing airloads and performance prediction, turbulence modeling of separated flows, airloads and acoustics of rotorcraft, vortex-induced nonlinearities on submarines, and standing oblique detonation waves.

Multiscaling properties of coastal waters particle size distribution from LISST in situ measurements

NASA Astrophysics Data System (ADS)

Pannimpullath Remanan, R.; Schmitt, F. G.; Loisel, H.; Mériaux, X.

2013-12-01

An eulerian high frequency sampling of particle size distribution (PSD) is performed during 5 tidal cycles (65 hours) in a coastal environment of the eastern English Channel at 1 Hz. The particle data are recorded using a LISST-100x type C (Laser In Situ Scattering and Transmissometry, Sequoia Scientific), recording volume concentrations of particles having diameters ranging from 2.5 to 500 mu in 32 size classes in logarithmic scale. This enables the estimation at each time step (every second) of the probability density function of particle sizes. At every time step, the pdf of PSD is hyperbolic. We can thus estimate PSD slope time series. Power spectral analysis shows that the mean diameter of the suspended particles is scaling at high frequencies (from 1s to 1000s). The scaling properties of particle sizes is studied by computing the moment function, from the pdf of the size distribution. Moment functions at many different time scales (from 1s to 1000 s) are computed and their scaling properties considered. The Shannon entropy at each time scale is also estimated and is related to other parameters. The multiscaling properties of the turbidity (coefficient cp computed from the LISST) are also consider on the same time scales, using Empirical Mode Decomposition.

FAST: A multi-processed environment for visualization of computational fluid dynamics

NASA Technical Reports Server (NTRS)

Bancroft, Gordon V.; Merritt, Fergus J.; Plessel, Todd C.; Kelaita, Paul G.; Mccabe, R. Kevin

1991-01-01

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.

Computer modeling in developmental biology: growing today, essential tomorrow.

PubMed

Sharpe, James

2017-12-01

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.

Experience in using commercial clouds in CMS

NASA Astrophysics Data System (ADS)

Bauerdick, L.; Bockelman, B.; Dykstra, D.; Fuess, S.; Garzoglio, G.; Girone, M.; Gutsche, O.; Holzman, B.; Hufnagel, D.; Kim, H.; Kennedy, R.; Mason, D.; Spentzouris, P.; Timm, S.; Tiradani, A.; Vaandering, E.; CMS Collaboration

2017-10-01

Historically high energy physics computing has been performed on large purpose-built computing systems. In the beginning there were single site computing facilities, which evolved into the Worldwide LHC Computing Grid (WLCG) used today. The vast majority of the WLCG resources are used for LHC computing and the resources are scheduled to be continuously used throughout the year. In the last several years there has been an explosion in capacity and capability of commercial and academic computing clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest amongst the cloud providers to demonstrate the capability to perform large scale scientific computing. In this presentation we will discuss results from the CMS experiment using the Fermilab HEPCloud Facility, which utilized both local Fermilab resources and Amazon Web Services (AWS). The goal was to work with AWS through a matching grant to demonstrate a sustained scale approximately equal to half of the worldwide processing resources available to CMS. We will discuss the planning and technical challenges involved in organizing the most IO intensive CMS workflows on a large-scale set of virtualized resource provisioned by the Fermilab HEPCloud. We will describe the data handling and data management challenges. Also, we will discuss the economic issues and cost and operational efficiency comparison to our dedicated resources. At the end we will consider the changes in the working model of HEP computing in a domain with the availability of large scale resources scheduled at peak times.

Experience in using commercial clouds in CMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bauerdick, L.; Bockelman, B.; Dykstra, D.

Historically high energy physics computing has been performed on large purposebuilt computing systems. In the beginning there were single site computing facilities, which evolved into the Worldwide LHC Computing Grid (WLCG) used today. The vast majority of the WLCG resources are used for LHC computing and the resources are scheduled to be continuously used throughout the year. In the last several years there has been an explosion in capacity and capability of commercial and academic computing clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is amore » growing interest amongst the cloud providers to demonstrate the capability to perform large scale scientific computing. In this presentation we will discuss results from the CMS experiment using the Fermilab HEPCloud Facility, which utilized both local Fermilab resources and Amazon Web Services (AWS). The goal was to work with AWS through a matching grant to demonstrate a sustained scale approximately equal to half of the worldwide processing resources available to CMS. We will discuss the planning and technical challenges involved in organizing the most IO intensive CMS workflows on a large-scale set of virtualized resource provisioned by the Fermilab HEPCloud. We will describe the data handling and data management challenges. Also, we will discuss the economic issues and cost and operational efficiency comparison to our dedicated resources. At the end we will consider the changes in the working model of HEP computing in a domain with the availability of large scale resources scheduled at peak times.« less

Trace: a high-throughput tomographic reconstruction engine for large-scale datasets.

PubMed

Bicer, Tekin; Gürsoy, Doğa; Andrade, Vincent De; Kettimuthu, Rajkumar; Scullin, William; Carlo, Francesco De; Foster, Ian T

2017-01-01

Modern synchrotron light sources and detectors produce data at such scale and complexity that large-scale computation is required to unleash their full power. One of the widely used imaging techniques that generates data at tens of gigabytes per second is computed tomography (CT). Although CT experiments result in rapid data generation, the analysis and reconstruction of the collected data may require hours or even days of computation time with a medium-sized workstation, which hinders the scientific progress that relies on the results of analysis. We present Trace, a data-intensive computing engine that we have developed to enable high-performance implementation of iterative tomographic reconstruction algorithms for parallel computers. Trace provides fine-grained reconstruction of tomography datasets using both (thread-level) shared memory and (process-level) distributed memory parallelization. Trace utilizes a special data structure called replicated reconstruction object to maximize application performance. We also present the optimizations that we apply to the replicated reconstruction objects and evaluate them using tomography datasets collected at the Advanced Photon Source. Our experimental evaluations show that our optimizations and parallelization techniques can provide 158× speedup using 32 compute nodes (384 cores) over a single-core configuration and decrease the end-to-end processing time of a large sinogram (with 4501 × 1 × 22,400 dimensions) from 12.5 h to <5 min per iteration. The proposed tomographic reconstruction engine can efficiently process large-scale tomographic data using many compute nodes and minimize reconstruction times.

Using CyberShake Workflows to Manage Big Seismic Hazard Data on Large-Scale Open-Science HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Juve, G.; Vahi, K.; Deelman, E.; Jordan, T. H.

2015-12-01

The CyberShake computational platform, developed by the Southern California Earthquake Center (SCEC), is an integrated collection of scientific software and middleware that performs 3D physics-based probabilistic seismic hazard analysis (PSHA) for Southern California. CyberShake integrates large-scale and high-throughput research codes to produce probabilistic seismic hazard curves for individual locations of interest and hazard maps for an entire region. A recent CyberShake calculation produced about 500,000 two-component seismograms for each of 336 locations, resulting in over 300 million synthetic seismograms in a Los Angeles-area probabilistic seismic hazard model. CyberShake calculations require a series of scientific software programs. Early computational stages produce data used as inputs by later stages, so we describe CyberShake calculations using a workflow definition language. Scientific workflow tools automate and manage the input and output data and enable remote job execution on large-scale HPC systems. To satisfy the requests of broad impact users of CyberShake data, such as seismologists, utility companies, and building code engineers, we successfully completed CyberShake Study 15.4 in April and May 2015, calculating a 1 Hz urban seismic hazard map for Los Angeles. We distributed the calculation between the NSF Track 1 system NCSA Blue Waters, the DOE Leadership-class system OLCF Titan, and USC's Center for High Performance Computing. This study ran for over 5 weeks, burning about 1.1 million node-hours and producing over half a petabyte of data. The CyberShake Study 15.4 results doubled the maximum simulated seismic frequency from 0.5 Hz to 1.0 Hz as compared to previous studies, representing a factor of 16 increase in computational complexity. We will describe how our workflow tools supported splitting the calculation across multiple systems. We will explain how we modified CyberShake software components, including GPU implementations and migrating from file-based communication to MPI messaging, to greatly reduce the I/O demands and node-hour requirements of CyberShake. We will also present performance metrics from CyberShake Study 15.4, and discuss challenges that producers of Big Data on open-science HPC resources face moving forward.

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similaritymore » score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.« less

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

DOE PAGES

Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.

2017-01-01

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similaritymore » score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.« less

Metadata Management on the SCEC PetaSHA Project: Helping Users Describe, Discover, Understand, and Use Simulation Data in a Large-Scale Scientific Collaboration

NASA Astrophysics Data System (ADS)

Okaya, D.; Deelman, E.; Maechling, P.; Wong-Barnum, M.; Jordan, T. H.; Meyers, D.

2007-12-01

Large scientific collaborations, such as the SCEC Petascale Cyberfacility for Physics-based Seismic Hazard Analysis (PetaSHA) Project, involve interactions between many scientists who exchange ideas and research results. These groups must organize, manage, and make accessible their community materials of observational data, derivative (research) results, computational products, and community software. The integration of scientific workflows as a paradigm to solve complex computations provides advantages of efficiency, reliability, repeatability, choices, and ease of use. The underlying resource needed for a scientific workflow to function and create discoverable and exchangeable products is the construction, tracking, and preservation of metadata. In the scientific workflow environment there is a two-tier structure of metadata. Workflow-level metadata and provenance describe operational steps, identity of resources, execution status, and product locations and names. Domain-level metadata essentially define the scientific meaning of data, codes and products. To a large degree the metadata at these two levels are separate. However, between these two levels is a subset of metadata produced at one level but is needed by the other. This crossover metadata suggests that some commonality in metadata handling is needed. SCEC researchers are collaborating with computer scientists at SDSC, the USC Information Sciences Institute, and Carnegie Mellon Univ. in order to perform earthquake science using high-performance computational resources. A primary objective of the "PetaSHA" collaboration is to perform physics-based estimations of strong ground motion associated with real and hypothetical earthquakes located within Southern California. Construction of 3D earth models, earthquake representations, and numerical simulation of seismic waves are key components of these estimations. Scientific workflows are used to orchestrate the sequences of scientific tasks and to access distributed computational facilities such as the NSF TeraGrid. Different types of metadata are produced and captured within the scientific workflows. One workflow within PetaSHA ("Earthworks") performs a linear sequence of tasks with workflow and seismological metadata preserved. Downstream scientific codes ingest these metadata produced by upstream codes. The seismological metadata uses attribute-value pairing in plain text; an identified need is to use more advanced handling methods. Another workflow system within PetaSHA ("Cybershake") involves several complex workflows in order to perform statistical analysis of ground shaking due to thousands of hypothetical but plausible earthquakes. Metadata management has been challenging due to its construction around a number of legacy scientific codes. We describe difficulties arising in the scientific workflow due to the lack of this metadata and suggest corrective steps, which in some cases include the cultural shift of domain science programmers coding for metadata.

Large-scale deep learning for robotically gathered imagery for science

NASA Astrophysics Data System (ADS)

Skinner, K.; Johnson-Roberson, M.; Li, J.; Iscar, E.

2016-12-01

With the explosion of computing power, the intelligence and capability of mobile robotics has dramatically increased over the last two decades. Today, we can deploy autonomous robots to achieve observations in a variety of environments ripe for scientific exploration. These platforms are capable of gathering a volume of data previously unimaginable. Additionally, optical cameras, driven by mobile phones and consumer photography, have rapidly improved in size, power consumption, and quality making their deployment cheaper and easier. Finally, in parallel we have seen the rise of large-scale machine learning approaches, particularly deep neural networks (DNNs), increasing the quality of the semantic understanding that can be automatically extracted from optical imagery. In concert this enables new science using a combination of machine learning and robotics. This work will discuss the application of new low-cost high-performance computing approaches and the associated software frameworks to enable scientists to rapidly extract useful science data from millions of robotically gathered images. The automated analysis of imagery on this scale opens up new avenues of inquiry unavailable using more traditional manual or semi-automated approaches. We will use a large archive of millions of benthic images gathered with an autonomous underwater vehicle to demonstrate how these tools enable new scientific questions to be posed.

Applying the scientific method to small catchment studies: Areview of the Panola Mountain experience

USGS Publications Warehouse

Hooper, R.P.

2001-01-01

A hallmark of the scientific method is its iterative application to a problem to increase and refine the understanding of the underlying processes controlling it. A successful iterative application of the scientific method to catchment science (including the fields of hillslope hydrology and biogeochemistry) has been hindered by two factors. First, the scale at which controlled experiments can be performed is much smaller than the scale of the phenomenon of interest. Second, computer simulation models generally have not been used as hypothesis-testing tools as rigorously as they might have been. Model evaluation often has gone only so far as evaluation of goodness of fit, rather than a full structural analysis, which is more useful when treating the model as a hypothesis. An iterative application of a simple mixing model to the Panola Mountain Research Watershed is reviewed to illustrate the increase in understanding gained by this approach and to discern general principles that may be applicable to other studies. The lessons learned include the need for an explicitly stated conceptual model of the catchment, the definition of objective measures of its applicability, and a clear linkage between the scale of observations and the scale of predictions. Published in 2001 by John Wiley & Sons. Ltd.

Build It: Will They Come?

NASA Astrophysics Data System (ADS)

Corrie, Brian; Zimmerman, Todd

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.

Digital Rocks Portal: a sustainable platform for imaged dataset sharing, translation and automated analysis

NASA Astrophysics Data System (ADS)

Prodanovic, M.; Esteva, M.; Hanlon, M.; Nanda, G.; Agarwal, P.

2015-12-01

Recent advances in imaging have provided a wealth of 3D datasets that reveal pore space microstructure (nm to cm length scale) and allow investigation of nonlinear flow and mechanical phenomena from first principles using numerical approaches. This framework has popularly been called "digital rock physics". Researchers, however, have trouble storing and sharing the datasets both due to their size and the lack of standardized image types and associated metadata for volumetric datasets. This impedes scientific cross-validation of the numerical approaches that characterize large scale porous media properties, as well as development of multiscale approaches required for correct upscaling. A single research group typically specializes in an imaging modality and/or related modeling on a single length scale, and lack of data-sharing infrastructure makes it difficult to integrate different length scales. We developed a sustainable, open and easy-to-use repository called the Digital Rocks Portal, that (1) organizes images and related experimental measurements of different porous materials, (2) improves access to them for a wider community of geosciences or engineering researchers not necessarily trained in computer science or data analysis. Once widely accepter, the repository will jumpstart productivity and enable scientific inquiry and engineering decisions founded on a data-driven basis. This is the first repository of its kind. We show initial results on incorporating essential software tools and pipelines that make it easier for researchers to store and reuse data, and for educators to quickly visualize and illustrate concepts to a wide audience. For data sustainability and continuous access, the portal is implemented within the reliable, 24/7 maintained High Performance Computing Infrastructure supported by the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. Long-term storage is provided through the University of Texas System Research Cyber-infrastructure initiative.

Enabling Diverse Software Stacks on Supercomputers using High Performance Virtual Clusters.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Younge, Andrew J.; Pedretti, Kevin; Grant, Ryan

While large-scale simulations have been the hallmark of the High Performance Computing (HPC) community for decades, Large Scale Data Analytics (LSDA) workloads are gaining attention within the scientific community not only as a processing component to large HPC simulations, but also as standalone scientific tools for knowledge discovery. With the path towards Exascale, new HPC runtime systems are also emerging in a way that differs from classical distributed com- puting models. However, system software for such capabilities on the latest extreme-scale DOE supercomputing needs to be enhanced to more appropriately support these types of emerging soft- ware ecosystems. In thismore » paper, we propose the use of Virtual Clusters on advanced supercomputing resources to enable systems to support not only HPC workloads, but also emerging big data stacks. Specifi- cally, we have deployed the KVM hypervisor within Cray's Compute Node Linux on a XC-series supercomputer testbed. We also use libvirt and QEMU to manage and provision VMs directly on compute nodes, leveraging Ethernet-over-Aries network emulation. To our knowledge, this is the first known use of KVM on a true MPP supercomputer. We investigate the overhead our solution using HPC benchmarks, both evaluating single-node performance as well as weak scaling of a 32-node virtual cluster. Overall, we find single node performance of our solution using KVM on a Cray is very efficient with near-native performance. However overhead increases by up to 20% as virtual cluster size increases, due to limitations of the Ethernet-over-Aries bridged network. Furthermore, we deploy Apache Spark with large data analysis workloads in a Virtual Cluster, ef- fectively demonstrating how diverse software ecosystems can be supported by High Performance Virtual Clusters.« less

76 FR 31945 - Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-02

... 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...

Optimizing CyberShake Seismic Hazard Workflows for Large HPC Resources

NASA Astrophysics Data System (ADS)

Callaghan, S.; Maechling, P. J.; Juve, G.; Vahi, K.; Deelman, E.; Jordan, T. H.

2014-12-01

The CyberShake computational platform is a well-integrated collection of scientific software and middleware that calculates 3D simulation-based probabilistic seismic hazard curves and hazard maps for the Los Angeles region. Currently each CyberShake model comprises about 235 million synthetic seismograms from about 415,000 rupture variations computed at 286 sites. CyberShake integrates large-scale parallel and high-throughput serial seismological research codes into a processing framework in which early stages produce files used as inputs by later stages. Scientific workflow tools are used to manage the jobs, data, and metadata. The Southern California Earthquake Center (SCEC) developed the CyberShake platform using USC High Performance Computing and Communications systems and open-science NSF resources.CyberShake calculations were migrated to the NSF Track 1 system NCSA Blue Waters when it became operational in 2013, via an interdisciplinary team approach including domain scientists, computer scientists, and middleware developers. Due to the excellent performance of Blue Waters and CyberShake software optimizations, we reduced the makespan (a measure of wallclock time-to-solution) of a CyberShake study from 1467 to 342 hours. We will describe the technical enhancements behind this improvement, including judicious introduction of new GPU software, improved scientific software components, increased workflow-based automation, and Blue Waters-specific workflow optimizations.Our CyberShake performance improvements highlight the benefits of scientific workflow tools. The CyberShake workflow software stack includes the Pegasus Workflow Management System (Pegasus-WMS, which includes Condor DAGMan), HTCondor, and Globus GRAM, with Pegasus-mpi-cluster managing the high-throughput tasks on the HPC resources. The workflow tools handle data management, automatically transferring about 13 TB back to SCEC storage.We will present performance metrics from the most recent CyberShake study, executed on Blue Waters. We will compare the performance of CPU and GPU versions of our large-scale parallel wave propagation code, AWP-ODC-SGT. Finally, we will discuss how these enhancements have enabled SCEC to move forward with plans to increase the CyberShake simulation frequency to 1.0 Hz.

The TeraShake Computational Platform for Large-Scale Earthquake Simulations

NASA Astrophysics Data System (ADS)

Cui, Yifeng; Olsen, Kim; Chourasia, Amit; Moore, Reagan; Maechling, Philip; Jordan, Thomas

Geoscientific and computer science researchers with the Southern California Earthquake Center (SCEC) are conducting a large-scale, physics-based, computationally demanding earthquake system science research program with the goal of developing predictive models of earthquake processes. The computational demands of this program continue to increase rapidly as these researchers seek to perform physics-based numerical simulations of earthquake processes for larger meet the needs of this research program, a multiple-institution team coordinated by SCEC has integrated several scientific codes into a numerical modeling-based research tool we call the TeraShake computational platform (TSCP). A central component in the TSCP is a highly scalable earthquake wave propagation simulation program called the TeraShake anelastic wave propagation (TS-AWP) code. In this chapter, we describe how we extended an existing, stand-alone, wellvalidated, finite-difference, anelastic wave propagation modeling code into the highly scalable and widely used TS-AWP and then integrated this code into the TeraShake computational platform that provides end-to-end (initialization to analysis) research capabilities. We also describe the techniques used to enhance the TS-AWP parallel performance on TeraGrid supercomputers, as well as the TeraShake simulations phases including input preparation, run time, data archive management, and visualization. As a result of our efforts to improve its parallel efficiency, the TS-AWP has now shown highly efficient strong scaling on over 40K processors on IBM’s BlueGene/L Watson computer. In addition, the TSCP has developed into a computational system that is useful to many members of the SCEC community for performing large-scale earthquake simulations.

75 FR 9887 - Advanced Scientific Computing Advisory Committee

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2010-03-04

... 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...

76 FR 9765 - Advanced Scientific Computing Advisory Committee

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2011-02-22

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77 FR 45345 - DOE/Advanced Scientific Computing Advisory Committee

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2012-07-31

... 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...

75 FR 64720 - DOE/Advanced Scientific Computing Advisory Committee

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2010-10-20

... 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...

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

A Systematic Approach for Obtaining Performance on Matrix-Like Operations

NASA Astrophysics Data System (ADS)

Veras, Richard Michael

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.

ISCR FY2005 Annual Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Keyes, D E; McGraw, J R

2006-02-02

Large-scale scientific computation and all of the disciplines that support and help validate it have been placed at the focus of Lawrence Livermore National Laboratory (LLNL) by the Advanced Simulation and Computing (ASC) program of the National Nuclear Security Administration (NNSA) and the Scientific Discovery through Advanced Computing (SciDAC) initiative of the Office of Science of the Department of Energy (DOE). The maturation of simulation as a fundamental tool of scientific and engineering research is underscored in the President's Information Technology Advisory Committee (PITAC) June 2005 finding that ''computational science has become critical to scientific leadership, economic competitiveness, and nationalmore » security''. LLNL operates several of the world's most powerful computers--including today's single most powerful--and has undertaken some of the largest and most compute-intensive simulations ever performed, most notably the molecular dynamics simulation that sustained more than 100 Teraflop/s and won the 2005 Gordon Bell Prize. Ultrascale simulation has been identified as one of the highest priorities in DOE's facilities planning for the next two decades. However, computers at architectural extremes are notoriously difficult to use in an efficient manner. Furthermore, each successful terascale simulation only points out the need for much better ways of interacting with the resulting avalanche of data. Advances in scientific computing research have, therefore, never been more vital to the core missions of LLNL than at present. Computational science is evolving so rapidly along every one of its research fronts that to remain on the leading edge, LLNL must engage researchers at many academic centers of excellence. In FY 2005, the Institute for Scientific Computing Research (ISCR) served as one of LLNL's main bridges to the academic community with a program of collaborative subcontracts, visiting faculty, student internships, workshops, and an active seminar series. The ISCR identifies researchers from the academic community for computer science and computational science collaborations with LLNL and hosts them for both brief and extended visits with the aim of encouraging long-term academic research agendas that address LLNL research priorities. Through these collaborations, ideas and software flow in both directions, and LLNL cultivates its future workforce. The Institute strives to be LLNL's ''eyes and ears'' in the computer and information sciences, keeping the Laboratory aware of and connected to important external advances. It also attempts to be the ''hands and feet'' that carry those advances into the Laboratory and incorporate them into practice. ISCR research participants are integrated into LLNL's Computing Applications and Research (CAR) Department, especially into its Center for Applied Scientific Computing (CASC). In turn, these organizations address computational challenges arising throughout the rest of the Laboratory. Administratively, the ISCR flourishes under LLNL's University Relations Program (URP). Together with the other four institutes of the URP, the ISCR navigates a course that allows LLNL to benefit from academic exchanges while preserving national security. While it is difficult to operate an academic-like research enterprise within the context of a national security laboratory, the results declare the challenges well met and worth the continued effort. The pages of this annual report summarize the activities of the faculty members, postdoctoral researchers, students, and guests from industry and other laboratories who participated in LLNL's computational mission under the auspices of the ISCR during FY 2005.« less

The International Symposium on Grids and Clouds

NASA Astrophysics Data System (ADS)

The International Symposium on Grids and Clouds (ISGC) 2012 will be held at Academia Sinica in Taipei from 26 February to 2 March 2012, with co-located events and workshops. The conference is hosted by the Academia Sinica Grid Computing Centre (ASGC). 2012 is the decennium anniversary of the ISGC which over the last decade has tracked the convergence, collaboration and innovation of individual researchers across the Asia Pacific region to a coherent community. With the continuous support and dedication from the delegates, ISGC has provided the primary international distributed computing platform where distinguished researchers and collaboration partners from around the world share their knowledge and experiences. The last decade has seen the wide-scale emergence of e-Infrastructure as a critical asset for the modern e-Scientist. The emergence of large-scale research infrastructures and instruments that has produced a torrent of electronic data is forcing a generational change in the scientific process and the mechanisms used to analyse the resulting data deluge. No longer can the processing of these vast amounts of data and production of relevant scientific results be undertaken by a single scientist. Virtual Research Communities that span organisations around the world, through an integrated digital infrastructure that connects the trust and administrative domains of multiple resource providers, have become critical in supporting these analyses. Topics covered in ISGC 2012 include: High Energy Physics, Biomedicine & Life Sciences, Earth Science, Environmental Changes and Natural Disaster Mitigation, Humanities & Social Sciences, Operations & Management, Middleware & Interoperability, Security and Networking, Infrastructure Clouds & Virtualisation, Business Models & Sustainability, Data Management, Distributed Volunteer & Desktop Grid Computing, High Throughput Computing, and High Performance, Manycore & GPU Computing.

A Computationally Efficient Parallel Levenberg-Marquardt Algorithm for Large-Scale Big-Data Inversion

NASA Astrophysics Data System (ADS)

Lin, Y.; O'Malley, D.; Vesselinov, V. V.

2015-12-01

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.

Blueprint for a microwave trapped ion quantum computer.

PubMed

Lekitsch, Bjoern; Weidt, Sebastian; Fowler, Austin G; Mølmer, Klaus; Devitt, Simon J; Wunderlich, Christof; Hensinger, Winfried K

2017-02-01

The availability of a universal quantum computer may have a fundamental impact on a vast number of research fields and on society as a whole. An increasingly large scientific and industrial community is working toward the realization of such a device. An arbitrarily large quantum computer may best be constructed using a modular approach. We present a blueprint for a trapped ion-based scalable quantum computer module, making it possible to create a scalable quantum computer architecture based on long-wavelength radiation quantum gates. The modules control all operations as stand-alone units, are constructed using silicon microfabrication techniques, and are within reach of current technology. To perform the required quantum computations, the modules make use of long-wavelength radiation-based quantum gate technology. To scale this microwave quantum computer architecture to a large size, we present a fully scalable design that makes use of ion transport between different modules, thereby allowing arbitrarily many modules to be connected to construct a large-scale device. A high error-threshold surface error correction code can be implemented in the proposed architecture to execute fault-tolerant operations. With appropriate adjustments, the proposed modules are also suitable for alternative trapped ion quantum computer architectures, such as schemes using photonic interconnects.

Finding Tropical Cyclones on a Cloud Computing Cluster: Using Parallel Virtualization for Large-Scale Climate Simulation Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hasenkamp, Daren; Sim, Alexander; Wehner, Michael

Extensive computing power has been used to tackle issues such as climate changes, fusion energy, and other pressing scientific challenges. These computations produce a tremendous amount of data; however, many of the data analysis programs currently only run a single processor. In this work, we explore the possibility of using the emerging cloud computing platform to parallelize such sequential data analysis tasks. As a proof of concept, we wrap a program for analyzing trends of tropical cyclones in a set of virtual machines (VMs). This approach allows the user to keep their familiar data analysis environment in the VMs, whilemore » we provide the coordination and data transfer services to ensure the necessary input and output are directed to the desired locations. This work extensively exercises the networking capability of the cloud computing systems and has revealed a number of weaknesses in the current cloud system software. In our tests, we are able to scale the parallel data analysis job to a modest number of VMs and achieve a speedup that is comparable to running the same analysis task using MPI. However, compared to MPI based parallelization, the cloud-based approach has a number of advantages. The cloud-based approach is more flexible because the VMs can capture arbitrary software dependencies without requiring the user to rewrite their programs. The cloud-based approach is also more resilient to failure; as long as a single VM is running, it can make progress while as soon as one MPI node fails the whole analysis job fails. In short, this initial work demonstrates that a cloud computing system is a viable platform for distributed scientific data analyses traditionally conducted on dedicated supercomputing systems.« less

2008 ALCF annual report.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Drugan, C.

2009-12-07

The word 'breakthrough' aptly describes the transformational science and milestones achieved at the Argonne Leadership Computing Facility (ALCF) throughout 2008. The number of research endeavors undertaken at the ALCF through the U.S. Department of Energy's (DOE) Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program grew from 9 in 2007 to 20 in 2008. The allocation of computer time awarded to researchers on the Blue Gene/P also spiked significantly - from nearly 10 million processor hours in 2007 to 111 million in 2008. To support this research, we expanded the capabilities of Intrepid, an IBM Blue Gene/P systemmore » at the ALCF, to 557 teraflops (TF) for production use. Furthermore, we enabled breakthrough levels of productivity and capability in visualization and data analysis with Eureka, a powerful installation of NVIDIA Quadro Plex S4 external graphics processing units. Eureka delivered a quantum leap in visual compute density, providing more than 111 TF and more than 3.2 terabytes of RAM. On April 21, 2008, the dedication of the ALCF realized DOE's vision to bring the power of the Department's high performance computing to open scientific research. In June, the IBM Blue Gene/P supercomputer at the ALCF debuted as the world's fastest for open science and third fastest overall. No question that the science benefited from this growth and system improvement. Four research projects spearheaded by Argonne National Laboratory computer scientists and ALCF users were named to the list of top ten scientific accomplishments supported by DOE's Advanced Scientific Computing Research (ASCR) program. Three of the top ten projects used extensive grants of computing time on the ALCF's Blue Gene/P to model the molecular basis of Parkinson's disease, design proteins at atomic scale, and create enzymes. As the year came to a close, the ALCF was recognized with several prestigious awards at SC08 in November. We provided resources for Linear Scaling Divide-and-Conquer Electronic Structure Calculations for Thousand Atom Nanostructures, a collaborative effort between Argonne, Lawrence Berkeley National Laboratory, and Oak Ridge National Laboratory that received the ACM Gordon Bell Prize Special Award for Algorithmic Innovation. The ALCF also was named a winner in two of the four categories in the HPC Challenge best performance benchmark competition.« less

75 FR 43518 - Advanced Scientific Computing Advisory Committee; Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2010-07-26

... 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...

The Virtual Geophysics Laboratory (VGL): Scientific Workflows Operating Across Organizations and Across Infrastructures

NASA Astrophysics Data System (ADS)

Cox, S. J.; Wyborn, L. A.; Fraser, R.; Rankine, T.; Woodcock, R.; Vote, J.; Evans, B.

2012-12-01

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.

Computing Cluster for Large Scale Turbulence Simulations and Applications in Computational Aeroacoustics

NASA Astrophysics Data System (ADS)

Lele, Sanjiva K.

2002-08-01

Funds were received in April 2001 under the Department of Defense DURIP program for construction of a 48 processor high performance computing cluster. This report details the hardware which was purchased and how it has been used to enable and enhance research activities directly supported by, and of interest to, the Air Force Office of Scientific Research and the Department of Defense. The report is divided into two major sections. The first section after this summary describes the computer cluster, its setup, and some cluster performance benchmark results. The second section explains ongoing research efforts which have benefited from the cluster hardware, and presents highlights of those efforts since installation of the cluster.

Resource-Efficient, Hierarchical Auto-Tuning of a Hybrid Lattice Boltzmann Computation on the Cray XT4

DOE Office of Scientific and Technical Information (OSTI.GOV)

Computational Research Division, Lawrence Berkeley National Laboratory; NERSC, Lawrence Berkeley National Laboratory; Computer Science Department, University of California, Berkeley

2009-05-04

We apply auto-tuning to a hybrid MPI-pthreads lattice Boltzmann computation running on the Cray XT4 at National Energy Research Scientific Computing Center (NERSC). Previous work showed that multicore-specific auto-tuning can improve the performance of lattice Boltzmann magnetohydrodynamics (LBMHD) by a factor of 4x when running on dual- and quad-core Opteron dual-socket SMPs. We extend these studies to the distributed memory arena via a hybrid MPI/pthreads implementation. In addition to conventional auto-tuning at the local SMP node, we tune at the message-passing level to determine the optimal aspect ratio as well as the correct balance between MPI tasks and threads permore » MPI task. Our study presents a detailed performance analysis when moving along an isocurve of constant hardware usage: fixed total memory, total cores, and total nodes. Overall, our work points to approaches for improving intra- and inter-node efficiency on large-scale multicore systems for demanding scientific applications.« less

Optimization of sparse matrix-vector multiplication on emerging multicore platforms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Samuel; Oliker, Leonid; Vuduc, Richard

2007-01-01

We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientificmore » study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.« less

Final Report. Institute for Ultralscale Visualization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Kwan-Liu; Galli, Giulia; Gygi, Francois

The SciDAC Institute for Ultrascale Visualization brought together leading experts from visualization, high-performance computing, and science application areas to make advanced visualization solutions for SciDAC scientists and the broader community. Over the five-year project, the Institute introduced many new enabling visualization techniques, which have significantly enhanced scientists’ ability to validate their simulations, interpret their data, and communicate with others about their work and findings. This Institute project involved a large number of junior and student researchers, who received the opportunities to work on some of the most challenging science applications and gain access to the most powerful high-performance computing facilitiesmore » in the world. They were readily trained and prepared for facing the greater challenges presented by extreme-scale computing. The Institute’s outreach efforts, through publications, workshops and tutorials, successfully disseminated the new knowledge and technologies to the SciDAC and the broader scientific communities. The scientific findings and experience of the Institute team helped plan the SciDAC3 program.« less

Status and Roadmap of CernVM

NASA Astrophysics Data System (ADS)

Berzano, D.; Blomer, J.; Buncic, P.; Charalampidis, I.; Ganis, G.; Meusel, R.

2015-12-01

Cloud resources nowadays contribute an essential share of resources for computing in high-energy physics. Such resources can be either provided by private or public IaaS clouds (e.g. OpenStack, Amazon EC2, Google Compute Engine) or by volunteers computers (e.g. LHC@Home 2.0). In any case, experiments need to prepare a virtual machine image that provides the execution environment for the physics application at hand. The CernVM virtual machine since version 3 is a minimal and versatile virtual machine image capable of booting different operating systems. The virtual machine image is less than 20 megabyte in size. The actual operating system is delivered on demand by the CernVM File System. CernVM 3 has matured from a prototype to a production environment. It is used, for instance, to run LHC applications in the cloud, to tune event generators using a network of volunteer computers, and as a container for the historic Scientific Linux 5 and Scientific Linux 4 based software environments in the course of long-term data preservation efforts of the ALICE, CMS, and ALEPH experiments. We present experience and lessons learned from the use of CernVM at scale. We also provide an outlook on the upcoming developments. These developments include adding support for Scientific Linux 7, the use of container virtualization, such as provided by Docker, and the streamlining of virtual machine contextualization towards the cloud-init industry standard.

Recommendations for open data science.

PubMed

Gymrek, Melissa; Farjoun, Yossi

2016-01-01

Life science research increasingly relies on large-scale computational analyses. However, the code and data used for these analyses are often lacking in publications. To maximize scientific impact, reproducibility, and reuse, it is crucial that these resources are made publicly available and are fully transparent. We provide recommendations for improving the openness of data-driven studies in life sciences.

Seafloor identification in sonar imagery via simulations of Helmholtz equations and discrete optimization

NASA Astrophysics Data System (ADS)

Engquist, Björn; Frederick, Christina; Huynh, Quyen; Zhou, Haomin

2017-06-01

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.

Research on the Construction Management and Sustainable Development of Large-Scale Scientific Facilities in China

NASA Astrophysics Data System (ADS)

Guiquan, Xi; Lin, Cong; Xuehui, Jin

2018-05-01

As an important platform for scientific and technological development, large -scale scientific facilities are the cornerstone of technological innovation and a guarantee for economic and social development. Researching management of large-scale scientific facilities can play a key role in scientific research, sociology and key national strategy. This paper reviews the characteristics of large-scale scientific facilities, and summarizes development status of China's large-scale scientific facilities. At last, the construction, management, operation and evaluation of large-scale scientific facilities is analyzed from the perspective of sustainable development.

The impact of supercomputers on experimentation: A view from a national laboratory

NASA Technical Reports Server (NTRS)

Peterson, V. L.; Arnold, J. O.

1985-01-01

The relative roles of large scale scientific computers and physical experiments in several science and engineering disciplines are discussed. Increasing dependence on computers is shown to be motivated both by the rapid growth in computer speed and memory, which permits accurate numerical simulation of complex physical phenomena, and by the rapid reduction in the cost of performing a calculation, which makes computation an increasingly attractive complement to experimentation. Computer speed and memory requirements are presented for selected areas of such disciplines as fluid dynamics, aerodynamics, aerothermodynamics, chemistry, atmospheric sciences, astronomy, and astrophysics, together with some examples of the complementary nature of computation and experiment. Finally, the impact of the emerging role of computers in the technical disciplines is discussed in terms of both the requirements for experimentation and the attainment of previously inaccessible information on physical processes.

Wayfinding in Social Networks

NASA Astrophysics Data System (ADS)

Liben-Nowell, David

With the recent explosion of popularity of commercial social-networking sites like Facebook and MySpace, the size of social networks that can be studied scientifically has passed from the scale traditionally studied by sociologists and anthropologists to the scale of networks more typically studied by computer scientists. In this chapter, I will highlight a recent line of computational research into the modeling and analysis of the small-world phenomenon - the observation that typical pairs of people in a social network are connected by very short chains of intermediate friends - and the ability of members of a large social network to collectively find efficient routes to reach individuals in the network. I will survey several recent mathematical models of social networks that account for these phenomena, with an emphasis on both the provable properties of these social-network models and the empirical validation of the models against real large-scale social-network data.

A Combined Eulerian-Lagrangian Data Representation for Large-Scale Applications.

PubMed

Sauer, Franz; Xie, Jinrong; Ma, Kwan-Liu

2017-10-01

The Eulerian and Lagrangian reference frames each provide a unique perspective when studying and visualizing results from scientific systems. As a result, many large-scale simulations produce data in both formats, and analysis tasks that simultaneously utilize information from both representations are becoming increasingly popular. However, due to their fundamentally different nature, drawing correlations between these data formats is a computationally difficult task, especially in a large-scale setting. In this work, we present a new data representation which combines both reference frames into a joint Eulerian-Lagrangian format. By reorganizing Lagrangian information according to the Eulerian simulation grid into a "unit cell" based approach, we can provide an efficient out-of-core means of sampling, querying, and operating with both representations simultaneously. We also extend this design to generate multi-resolution subsets of the full data to suit the viewer's needs and provide a fast flow-aware trajectory construction scheme. We demonstrate the effectiveness of our method using three large-scale real world scientific datasets and provide insight into the types of performance gains that can be achieved.

Analysis Report for Exascale Storage Requirements for Scientific Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ruwart, Thomas M.

Over the next 10 years, the Department of Energy will be transitioning from Petascale to Exascale Computing resulting in data storage, networking, and infrastructure requirements to increase by three orders of magnitude. The technologies and best practices used today are the result of a relatively slow evolution of ancestral technologies developed in the 1950s and 1960s. These include magnetic tape, magnetic disk, networking, databases, file systems, and operating systems. These technologies will continue to evolve over the next 10 to 15 years on a reasonably predictable path. Experience with the challenges involved in transitioning these fundamental technologies from Terascale tomore » Petascale computing systems has raised questions about how these will scale another 3 or 4 orders of magnitude to meet the requirements imposed by Exascale computing systems. This report is focused on the most concerning scaling issues with data storage systems as they relate to High Performance Computing- and presents options for a path forward. Given the ability to store exponentially increasing amounts of data, far more advanced concepts and use of metadata will be critical to managing data in Exascale computing systems.« less

Cloud computing and validation of expandable in silico livers.

PubMed

Ropella, Glen E P; Hunt, C Anthony

2010-12-03

In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware.

BlazeDEM3D-GPU A Large Scale DEM simulation code for GPUs

NASA Astrophysics Data System (ADS)

Govender, Nicolin; Wilke, Daniel; Pizette, Patrick; Khinast, Johannes

2017-06-01

Accurately predicting the dynamics of particulate materials is of importance to numerous scientific and industrial areas with applications ranging across particle scales from powder flow to ore crushing. Computational discrete element simulations is a viable option to aid in the understanding of particulate dynamics and design of devices such as mixers, silos and ball mills, as laboratory scale tests comes at a significant cost. However, the computational time required to simulate an industrial scale simulation which consists of tens of millions of particles can take months to complete on large CPU clusters, making the Discrete Element Method (DEM) unfeasible for industrial applications. Simulations are therefore typically restricted to tens of thousands of particles with highly detailed particle shapes or a few million of particles with often oversimplified particle shapes. However, a number of applications require accurate representation of the particle shape to capture the macroscopic behaviour of the particulate system. In this paper we give an overview of the recent extensions to the open source GPU based DEM code, BlazeDEM3D-GPU, that can simulate millions of polyhedra and tens of millions of spheres on a desktop computer with a single or multiple GPUs.

Compiling for Application Specific Computational Acceleration in Reconfigurable Architectures Final Report CRADA No. TSB-2033-01

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Supinski, B.; Caliga, D.

2017-09-28

The primary objective of this project was to develop memory optimization technology to efficiently deliver data to, and distribute data within, the SRC-6's Field Programmable Gate Array- ("FPGA") based Multi-Adaptive Processors (MAPs). The hardware/software approach was to explore efficient MAP configurations and generate the compiler technology to exploit those configurations. This memory accessing technology represents an important step towards making reconfigurable symmetric multi-processor (SMP) architectures that will be a costeffective solution for large-scale scientific computing.

High-performance dual-speed CCD camera system for scientific imaging

NASA Astrophysics Data System (ADS)

Simpson, Raymond W.

1996-03-01

Traditionally, scientific camera systems were partitioned with a `camera head' containing the CCD and its support circuitry and a camera controller, which provided analog to digital conversion, timing, control, computer interfacing, and power. A new, unitized high performance scientific CCD camera with dual speed readout at 1 X 106 or 5 X 106 pixels per second, 12 bit digital gray scale, high performance thermoelectric cooling, and built in composite video output is described. This camera provides all digital, analog, and cooling functions in a single compact unit. The new system incorporates the A/C converter, timing, control and computer interfacing in the camera, with the power supply remaining a separate remote unit. A 100 Mbyte/second serial link transfers data over copper or fiber media to a variety of host computers, including Sun, SGI, SCSI, PCI, EISA, and Apple Macintosh. Having all the digital and analog functions in the camera made it possible to modify this system for the Woods Hole Oceanographic Institution for use on a remote controlled submersible vehicle. The oceanographic version achieves 16 bit dynamic range at 1.5 X 105 pixels/second, can be operated at depths of 3 kilometers, and transfers data to the surface via a real time fiber optic link.

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

DOE PAGES

Christensen, A. J.; Srinivasan, V.; Hart, J. C.; ...

2018-03-17

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have ledmore » to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.« less

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

DOE Office of Scientific and Technical Information (OSTI.GOV)

Christensen, A. J.; Srinivasan, V.; Hart, J. C.

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have ledmore » to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. Lastly, this survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.« less

Accelerating Science with the NERSC Burst Buffer Early User Program

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhimji, Wahid; Bard, Debbie; Romanus, Melissa

NVRAM-based Burst Buffers are an important part of the emerging HPC storage landscape. The National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory recently installed one of the first Burst Buffer systems as part of its new Cori supercomputer, collaborating with Cray on the development of the DataWarp software. NERSC has a diverse user base comprised of over 6500 users in 700 different projects spanning a wide variety of scientific computing applications. The use-cases of the Burst Buffer at NERSC are therefore also considerable and diverse. We describe here performance measurements and lessons learned from the Burstmore » Buffer Early User Program at NERSC, which selected a number of research projects to gain early access to the Burst Buffer and exercise its capability to enable new scientific advancements. To the best of our knowledge this is the first time a Burst Buffer has been stressed at scale by diverse, real user workloads and therefore these lessons will be of considerable benefit to shaping the developing use of Burst Buffers at HPC centers.« less

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security.

PubMed

Christensen, A J; Srinivasan, Venkatraman; Hart, John C; Marshall-Colon, Amy

2018-05-01

Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in "big data" analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields.

Use of computational modeling combined with advanced visualization to develop strategies for the design of crop ideotypes to address food security

PubMed Central

Christensen, A J; Srinivasan, Venkatraman; Hart, John C; Marshall-Colon, Amy

2018-01-01

Abstract Sustainable crop production is a contributing factor to current and future food security. Innovative technologies are needed to design strategies that will achieve higher crop yields on less land and with fewer resources. Computational modeling coupled with advanced scientific visualization enables researchers to explore and interact with complex agriculture, nutrition, and climate data to predict how crops will respond to untested environments. These virtual observations and predictions can direct the development of crop ideotypes designed to meet future yield and nutritional demands. This review surveys modeling strategies for the development of crop ideotypes and scientific visualization technologies that have led to discoveries in “big data” analysis. Combined modeling and visualization approaches have been used to realistically simulate crops and to guide selection that immediately enhances crop quantity and quality under challenging environmental conditions. This survey of current and developing technologies indicates that integrative modeling and advanced scientific visualization may help overcome challenges in agriculture and nutrition data as large-scale and multidimensional data become available in these fields. PMID:29562368

Cancer Diagnosis Epigenomics Scientific Workflow Scheduling in the Cloud Computing Environment Using an Improved PSO Algorithm

PubMed

N, Sadhasivam; R, Balamurugan; M, Pandi

2018-01-27

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

A Federal Vision for Future Computing: A Nanotechnology-Inspired Grand Challenge

DTIC Science & Technology

2016-07-29

Science Foundation (NSF), Department of Defense (DOD), National Institute of Standards and Technology (NIST), Intelligence Community (IC) Introduction...multiple Federal agencies: • Intelligent big data sensors that act autonomously and are programmable via the network for increased flexibility, and... intelligence for scientific discovery enabled by rapid extreme-scale data analysis, capable of understanding and making sense of results and thereby

PuLP/XtraPuLP : Partitioning Tools for Extreme-Scale Graphs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Slota, George M; Rajamanickam, Sivasankaran; Madduri, Kamesh

2017-09-21

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.

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: Earth System Modeling Software Framework Survey

NASA Technical Reports Server (NTRS)

Talbot, Bryan; Zhou, Shu-Jia; Higgins, Glenn; Zukor, Dorothy (Technical Monitor)

2002-01-01

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task, both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation. while maintaining high performance across numerous supercomputer and workstation architectures. This document surveys numerous software frameworks for potential use in Earth science modeling. Several frameworks are evaluated in depth, including Parallel Object-Oriented Methods and Applications (POOMA), Cactus (from (he relativistic physics community), Overture, Goddard Earth Modeling System (GEMS), the National Center for Atmospheric Research Flux Coupler, and UCLA/UCB Distributed Data Broker (DDB). Frameworks evaluated in less detail include ROOT, Parallel Application Workspace (PAWS), and Advanced Large-Scale Integrated Computational Environment (ALICE). A host of other frameworks and related tools are referenced in this context. The frameworks are evaluated individually and also compared with each other.

Human computers: the first pioneers of the information age.

PubMed

Grier, D A

2001-03-01

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.

Load Balancing Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pearce, Olga Tkachyshyn

2014-12-01

The largest supercomputers have millions of independent processors, and concurrency levels are rapidly increasing. For ideal efficiency, developers of the simulations that run on these machines must ensure that computational work is evenly balanced among processors. Assigning work evenly is challenging because many large modern parallel codes simulate behavior of physical systems that evolve over time, and their workloads change over time. Furthermore, the cost of imbalanced load increases with scale because most large-scale scientific simulations today use a Single Program Multiple Data (SPMD) parallel programming model, and an increasing number of processors will wait for the slowest one atmore » the synchronization points. To address load imbalance, many large-scale parallel applications use dynamic load balance algorithms to redistribute work evenly. The research objective of this dissertation is to develop methods to decide when and how to load balance the application, and to balance it effectively and affordably. We measure and evaluate the computational load of the application, and develop strategies to decide when and how to correct the imbalance. Depending on the simulation, a fast, local load balance algorithm may be suitable, or a more sophisticated and expensive algorithm may be required. We developed a model for comparison of load balance algorithms for a specific state of the simulation that enables the selection of a balancing algorithm that will minimize overall runtime.« less

The Astronomy Workshop: Scientific Notation and Solar System Visualizer

NASA Astrophysics Data System (ADS)

Deming, Grace; Hamilton, D.; Hayes-Gehrke, M.

2008-09-01

The Astronomy Workshop (http://janus.astro.umd.edu) is a collection of interactive World Wide Web tools that were developed under the direction of Doug Hamilton for use in undergraduate classes and by the general public. The philosophy of the site is to foster student interest in astronomy by exploiting their fascination with computers and the internet. We have expanded the "Scientific Notation” tool from simply converting decimal numbers into and out of scientific notation to adding, subtracting, multiplying, and dividing numbers expressed in scientific notation. Students practice these skills and when confident they may complete a quiz. In addition, there are suggestions on how instructors may use the site to encourage students to practice these basic skills. The Solar System Visualizer animates orbits of planets, moons, and rings to scale. Extrasolar planetary systems are also featured. This research was sponsored by NASA EPO grant NNG06GGF99G.

Job Superscheduler Architecture and Performance in Computational Grid Environments

NASA Technical Reports Server (NTRS)

Shan, Hongzhang; Oliker, Leonid; Biswas, Rupak

2003-01-01

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.

A parallel implementation of an off-lattice individual-based model of multicellular populations

NASA Astrophysics Data System (ADS)

Harvey, Daniel G.; Fletcher, Alexander G.; Osborne, James M.; Pitt-Francis, Joe

2015-07-01

As computational models of multicellular populations include ever more detailed descriptions of biophysical and biochemical processes, the computational cost of simulating such models limits their ability to generate novel scientific hypotheses and testable predictions. While developments in microchip technology continue to increase the power of individual processors, parallel computing offers an immediate increase in available processing power. To make full use of parallel computing technology, it is necessary to develop specialised algorithms. To this end, we present a parallel algorithm for a class of off-lattice individual-based models of multicellular populations. The algorithm divides the spatial domain between computing processes and comprises communication routines that ensure the model is correctly simulated on multiple processors. The parallel algorithm is shown to accurately reproduce the results of a deterministic simulation performed using a pre-existing serial implementation. We test the scaling of computation time, memory use and load balancing as more processes are used to simulate a cell population of fixed size. We find approximate linear scaling of both speed-up and memory consumption on up to 32 processor cores. Dynamic load balancing is shown to provide speed-up for non-regular spatial distributions of cells in the case of a growing population.

Paradigms and strategies for scientific computing on distributed memory concurrent computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foster, I.T.; Walker, D.W.

1994-06-01

In this work we examine recent advances in parallel languages and abstractions that have the potential for improving the programmability and maintainability of large-scale, parallel, scientific applications running on high performance architectures and networks. This paper focuses on Fortran M, a set of extensions to Fortran 77 that supports the modular design of message-passing programs. We describe the Fortran M implementation of a particle-in-cell (PIC) plasma simulation application, and discuss issues in the optimization of the code. The use of two other methodologies for parallelizing the PIC application are considered. The first is based on the shared object abstraction asmore » embodied in the Orca language. The second approach is the Split-C language. In Fortran M, Orca, and Split-C the ability of the programmer to control the granularity of communication is important is designing an efficient implementation.« less

Experience Paper: Software Engineering and Community Codes Track in ATPESC

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dubey, Anshu; Riley, Katherine M.

Argonne Training Program in Extreme Scale Computing (ATPESC) was started by the Argonne National Laboratory with the objective of expanding the ranks of better prepared users of high performance computing (HPC) machines. One of the unique aspects of the program was inclusion of software engineering and community codes track. The inclusion was motivated by the observation that the projects with a good scientific and software process were better able to meet their scientific goals. In this paper we present our experience of running the software track from the beginning of the program until now. We discuss the motivations, the reception,more » and the evolution of the track over the years. We welcome discussion and input from the community to enhance the track in ATPESC, and also to facilitate inclusion of similar tracks in other HPC oriented training programs.« less

Blueprint for a microwave trapped ion quantum computer

PubMed Central

Lekitsch, Bjoern; Weidt, Sebastian; Fowler, Austin G.; Mølmer, Klaus; Devitt, Simon J.; Wunderlich, Christof; Hensinger, Winfried K.

2017-01-01

The availability of a universal quantum computer may have a fundamental impact on a vast number of research fields and on society as a whole. An increasingly large scientific and industrial community is working toward the realization of such a device. An arbitrarily large quantum computer may best be constructed using a modular approach. We present a blueprint for a trapped ion–based scalable quantum computer module, making it possible to create a scalable quantum computer architecture based on long-wavelength radiation quantum gates. The modules control all operations as stand-alone units, are constructed using silicon microfabrication techniques, and are within reach of current technology. To perform the required quantum computations, the modules make use of long-wavelength radiation–based quantum gate technology. To scale this microwave quantum computer architecture to a large size, we present a fully scalable design that makes use of ion transport between different modules, thereby allowing arbitrarily many modules to be connected to construct a large-scale device. A high error–threshold surface error correction code can be implemented in the proposed architecture to execute fault-tolerant operations. With appropriate adjustments, the proposed modules are also suitable for alternative trapped ion quantum computer architectures, such as schemes using photonic interconnects. PMID:28164154

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data.

PubMed

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H

2012-11-06

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the "big data" challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce.

Towards Building a High Performance Spatial Query System for Large Scale Medical Imaging Data

PubMed Central

Aji, Ablimit; Wang, Fusheng; Saltz, Joel H.

2013-01-01

Support of high performance queries on large volumes of scientific spatial data is becoming increasingly important in many applications. This growth is driven by not only geospatial problems in numerous fields, but also emerging scientific applications that are increasingly data- and compute-intensive. For example, digital pathology imaging has become an emerging field during the past decade, where examination of high resolution images of human tissue specimens enables more effective diagnosis, prediction and treatment of diseases. Systematic analysis of large-scale pathology images generates tremendous amounts of spatially derived quantifications of micro-anatomic objects, such as nuclei, blood vessels, and tissue regions. Analytical pathology imaging provides high potential to support image based computer aided diagnosis. One major requirement for this is effective querying of such enormous amount of data with fast response, which is faced with two major challenges: the “big data” challenge and the high computation complexity. In this paper, we present our work towards building a high performance spatial query system for querying massive spatial data on MapReduce. Our framework takes an on demand index building approach for processing spatial queries and a partition-merge approach for building parallel spatial query pipelines, which fits nicely with the computing model of MapReduce. We demonstrate our framework on supporting multi-way spatial joins for algorithm evaluation and nearest neighbor queries for microanatomic objects. To reduce query response time, we propose cost based query optimization to mitigate the effect of data skew. Our experiments show that the framework can efficiently support complex analytical spatial queries on MapReduce. PMID:24501719

Are Cloud Environments Ready for Scientific Applications?

NASA Astrophysics Data System (ADS)

Mehrotra, P.; Shackleford, K.

2011-12-01

Cloud computing environments are becoming widely available both in the commercial and government sectors. They provide flexibility to rapidly provision resources in order to meet dynamic and changing computational needs without the customers incurring capital expenses and/or requiring technical expertise. Clouds also provide reliable access to resources even though the end-user may not have in-house expertise for acquiring or operating such resources. Consolidation and pooling in a cloud environment allow organizations to achieve economies of scale in provisioning or procuring computing resources and services. Because of these and other benefits, many businesses and organizations are migrating their business applications (e.g., websites, social media, and business processes) to cloud environments-evidenced by the commercial success of offerings such as the Amazon EC2. In this paper, we focus on the feasibility of utilizing cloud environments for scientific workloads and workflows particularly of interest to NASA scientists and engineers. There is a wide spectrum of such technical computations. These applications range from small workstation-level computations to mid-range computing requiring small clusters to high-performance simulations requiring supercomputing systems with high bandwidth/low latency interconnects. Data-centric applications manage and manipulate large data sets such as satellite observational data and/or data previously produced by high-fidelity modeling and simulation computations. Most of the applications are run in batch mode with static resource requirements. However, there do exist situations that have dynamic demands, particularly ones with public-facing interfaces providing information to the general public, collaborators and partners, as well as to internal NASA users. In the last few months we have been studying the suitability of cloud environments for NASA's technical and scientific workloads. We have ported several applications to multiple cloud environments including NASA's Nebula environment, Amazon's EC2, Magellan at NERSC, and SGI's Cyclone system. We critically examined the performance of the applications on these systems. We also collected information on the usability of these cloud environments. In this talk we will present the results of our study focusing on the efficacy of using clouds for NASA's scientific applications.

Dynamic computer model for the metallogenesis and tectonics of the Circum-North Pacific

USGS Publications Warehouse

Scotese, Christopher R.; Nokleberg, Warren J.; Monger, James W.H.; Norton, Ian O.; Parfenov, Leonid M.; Khanchuk, Alexander I.; Bundtzen, Thomas K.; Dawson, Kenneth M.; Eremin, Roman A.; Frolov, Yuri F.; Fujita, Kazuya; Goryachev, Nikolai A.; Pozdeev, Anany I.; Ratkin, Vladimir V.; Rodinov, Sergey M.; Rozenblum, Ilya S.; Scholl, David W.; Shpikerman, Vladimir I.; Sidorov, Anatoly A.; Stone, David B.

2001-01-01

The digital files on this report consist of a dynamic computer model of the metallogenesis and tectonics of the Circum-North Pacific, and background articles, figures, and maps. The tectonic part of the dynamic computer model is derived from a major analysis of the tectonic evolution of the Circum-North Pacific which is also contained in directory tectevol. The dynamic computer model and associated materials on this CD-ROM are part of a project on the major mineral deposits, metallogenesis, and tectonics of the Russian Far East, Alaska, and the Canadian Cordillera. The project provides critical information on bedrock geology and geophysics, tectonics, major metalliferous mineral resources, metallogenic patterns, and crustal origin and evolution of mineralizing systems for this region. The major scientific goals and benefits of the project are to: (1) provide a comprehensive international data base on the mineral resources of the region that is the first, extensive knowledge available in English; (2) provide major new interpretations of the origin and crustal evolution of mineralizing systems and their host rocks, thereby enabling enhanced, broad-scale tectonic reconstructions and interpretations; and (3) promote trade and scientific and technical exchanges between North America and Eastern Asia.

Advances and Challenges In Uncertainty Quantification with Application to Climate Prediction, ICF design and Science Stockpile Stewardship

NASA Astrophysics Data System (ADS)

Klein, R.; Woodward, C. S.; Johannesson, G.; Domyancic, D.; Covey, C. C.; Lucas, D. D.

2012-12-01

Uncertainty Quantification (UQ) is a critical field within 21st century simulation science that resides at the very center of the web of emerging predictive capabilities. The science of UQ holds the promise of giving much greater meaning to the results of complex large-scale simulations, allowing for quantifying and bounding uncertainties. This powerful capability will yield new insights into scientific predictions (e.g. Climate) of great impact on both national and international arenas, allow informed decisions on the design of critical experiments (e.g. ICF capsule design, MFE, NE) in many scientific fields, and assign confidence bounds to scientifically predictable outcomes (e.g. nuclear weapons design). In this talk I will discuss a major new strategic initiative (SI) we have developed at Lawrence Livermore National Laboratory to advance the science of Uncertainty Quantification at LLNL focusing in particular on (a) the research and development of new algorithms and methodologies of UQ as applied to multi-physics multi-scale codes, (b) incorporation of these advancements into a global UQ Pipeline (i.e. a computational superstructure) that will simplify user access to sophisticated tools for UQ studies as well as act as a self-guided, self-adapting UQ engine for UQ studies on extreme computing platforms and (c) use laboratory applications as a test bed for new algorithms and methodologies. The initial SI focus has been on applications for the quantification of uncertainty associated with Climate prediction, but the validated UQ methodologies we have developed are now being fed back into Science Based Stockpile Stewardship (SSS) and ICF UQ efforts. To make advancements in several of these UQ grand challenges, I will focus in talk on the following three research areas in our Strategic Initiative: Error Estimation in multi-physics and multi-scale codes ; Tackling the "Curse of High Dimensionality"; and development of an advanced UQ Computational Pipeline to enable complete UQ workflow and analysis for ensemble runs at the extreme scale (e.g. exascale) with self-guiding adaptation in the UQ Pipeline engine. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was funded by the Uncertainty Quantification Strategic Initiative Laboratory Directed Research and Development Project at LLNL under project tracking code 10-SI-013 (UCRL LLNL-ABS-569112).

Network bandwidth utilization forecast model on high bandwidth networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wuchert; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Network Bandwidth Utilization Forecast Model on High Bandwidth Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology,more » our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.« less

Assessing Scientific Practices Using Machine-Learning Methods: How Closely Do They Match Clinical Interview Performance?

NASA Astrophysics Data System (ADS)

Beggrow, Elizabeth P.; Ha, Minsu; Nehm, Ross H.; Pearl, Dennis; Boone, William J.

2014-02-01

The landscape of science education is being transformed by the new Framework for Science Education (National Research Council, A framework for K-12 science education: practices, crosscutting concepts, and core ideas. The National Academies Press, Washington, DC, 2012), which emphasizes the centrality of scientific practices—such as explanation, argumentation, and communication—in science teaching, learning, and assessment. A major challenge facing the field of science education is developing assessment tools that are capable of validly and efficiently evaluating these practices. Our study examined the efficacy of a free, open-source machine-learning tool for evaluating the quality of students' written explanations of the causes of evolutionary change relative to three other approaches: (1) human-scored written explanations, (2) a multiple-choice test, and (3) clinical oral interviews. A large sample of undergraduates (n = 104) exposed to varying amounts of evolution content completed all three assessments: a clinical oral interview, a written open-response assessment, and a multiple-choice test. Rasch analysis was used to compute linear person measures and linear item measures on a single logit scale. We found that the multiple-choice test displayed poor person and item fit (mean square outfit >1.3), while both oral interview measures and computer-generated written response measures exhibited acceptable fit (average mean square outfit for interview: person 0.97, item 0.97; computer: person 1.03, item 1.06). Multiple-choice test measures were more weakly associated with interview measures (r = 0.35) than the computer-scored explanation measures (r = 0.63). Overall, Rasch analysis indicated that computer-scored written explanation measures (1) have the strongest correspondence to oral interview measures; (2) are capable of capturing students' normative scientific and naive ideas as accurately as human-scored explanations, and (3) more validly detect understanding than the multiple-choice assessment. These findings demonstrate the great potential of machine-learning tools for assessing key scientific practices highlighted in the new Framework for Science Education.

The scientific data acquisition system of the GAMMA-400 space project

NASA Astrophysics Data System (ADS)

Bobkov, S. G.; Serdin, O. V.; Gorbunov, M. S.; Arkhangelskiy, A. I.; Topchiev, N. P.

2016-02-01

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.

Access control and privacy in large distributed systems

NASA Technical Reports Server (NTRS)

Leiner, B. M.; Bishop, M.

1986-01-01

Large scale distributed systems consists of workstations, mainframe computers, supercomputers and other types of servers, all connected by a computer network. These systems are being used in a variety of applications including the support of collaborative scientific research. In such an environment, issues of access control and privacy arise. Access control is required for several reasons, including the protection of sensitive resources and cost control. Privacy is also required for similar reasons, including the protection of a researcher's proprietary results. A possible architecture for integrating available computer and communications security technologies into a system that meet these requirements is described. This architecture is meant as a starting point for discussion, rather that the final answer.

Scientific Visualization and Computational Science: Natural Partners

NASA Technical Reports Server (NTRS)

Uselton, Samuel P.; Lasinski, T. A. (Technical Monitor)

1995-01-01

Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization research because the results vary so widely and include things that have no known appearance. The amount of data creates additional challenges for both hardware and software systems. Evaluations of visualization should ultimately reflect the insight gained into the scientific phenomena. So making good visualizations requires consideration of characteristics of the user and the purpose of the visualization. Knowledge about human perception and graphic design is also relevant. It is this breadth of knowledge that stimulates proposals for multidisciplinary visualization teams and intelligent visualization assistant software. Visualization is an immature field, but computational science is stimulating research on a broad front.

Multicore Architecture-aware Scientific Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Srinivasa, Avinash

Modern high performance systems are becoming increasingly complex and powerful due to advancements in processor and memory architecture. In order to keep up with this increasing complexity, applications have to be augmented with certain capabilities to fully exploit such systems. These may be at the application level, such as static or dynamic adaptations or at the system level, like having strategies in place to override some of the default operating system polices, the main objective being to improve computational performance of the application. The current work proposes two such capabilites with respect to multi-threaded scientific applications, in particular a largemore » scale physics application computing ab-initio nuclear structure. The first involves using a middleware tool to invoke dynamic adaptations in the application, so as to be able to adjust to the changing computational resource availability at run-time. The second involves a strategy for effective placement of data in main memory, to optimize memory access latencies and bandwidth. These capabilties when included were found to have a significant impact on the application performance, resulting in average speedups of as much as two to four times.« less

Creating a Parallel Version of VisIt for Microsoft Windows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitlock, B J; Biagas, K S; Rawson, P L

2011-12-07

VisIt is a popular, free interactive parallel visualization and analysis tool for scientific data. Users can quickly generate visualizations from their data, animate them through time, manipulate them, and save the resulting images or movies for presentations. VisIt was designed from the ground up to work on many scales of computers from modest desktops up to massively parallel clusters. VisIt is comprised of a set of cooperating programs. All programs can be run locally or in client/server mode in which some run locally and some run remotely on compute clusters. The VisIt program most able to harness today's computing powermore » is the VisIt compute engine. The compute engine is responsible for reading simulation data from disk, processing it, and sending results or images back to the VisIt viewer program. In a parallel environment, the compute engine runs several processes, coordinating using the Message Passing Interface (MPI) library. Each MPI process reads some subset of the scientific data and filters the data in various ways to create useful visualizations. By using MPI, VisIt has been able to scale well into the thousands of processors on large computers such as dawn and graph at LLNL. The advent of multicore CPU's has made parallelism the 'new' way to achieve increasing performance. With today's computers having at least 2 cores and in many cases up to 8 and beyond, it is more important than ever to deploy parallel software that can use that computing power not only on clusters but also on the desktop. We have created a parallel version of VisIt for Windows that uses Microsoft's MPI implementation (MSMPI) to process data in parallel on the Windows desktop as well as on a Windows HPC cluster running Microsoft Windows Server 2008. Initial desktop parallel support for Windows was deployed in VisIt 2.4.0. Windows HPC cluster support has been completed and will appear in the VisIt 2.5.0 release. We plan to continue supporting parallel VisIt on Windows so our users will be able to take full advantage of their multicore resources.« less

From cosmos to connectomes: the evolution of data-intensive science.

PubMed

Burns, Randal; Vogelstein, Joshua T; Szalay, Alexander S

2014-09-17

The analysis of data requires computation: originally by hand and more recently by computers. Different models of computing are designed and optimized for different kinds of data. In data-intensive science, the scale and complexity of data exceeds the comfort zone of local data stores on scientific workstations. Thus, cloud computing emerges as the preeminent model, utilizing data centers and high-performance clusters, enabling remote users to access and query subsets of the data efficiently. We examine how data-intensive computational systems originally built for cosmology, the Sloan Digital Sky Survey (SDSS), are now being used in connectomics, at the Open Connectome Project. We list lessons learned and outline the top challenges we expect to face. Success in computational connectomics would drastically reduce the time between idea and discovery, as SDSS did in cosmology. Copyright © 2014 Elsevier Inc. All rights reserved.

Computational Science at the Argonne Leadership Computing Facility

NASA Astrophysics Data System (ADS)

Romero, Nichols

2014-03-01

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.

A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment.

PubMed

He, Bo; Zhang, Shujing; Yan, Tianhong; Zhang, Tao; Liang, Yan; Zhang, Hongjin

2011-01-01

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.

Performance analysis of a dual-tree algorithm for computing spatial distance histograms

PubMed Central

Chen, Shaoping; Tu, Yi-Cheng; Xia, Yuni

2011-01-01

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

Changing computing paradigms towards power efficiency

PubMed Central

Klavík, Pavel; Malossi, A. Cristiano I.; Bekas, Costas; Curioni, Alessandro

2014-01-01

Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a power-efficient computing paradigm that combines low- and high-precision arithmetic. We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a fine-grain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications. PMID:24842033

Improvements in the Scalability of the NASA Goddard Multiscale Modeling Framework for Hurricane Climate Studies

NASA Technical Reports Server (NTRS)

Shen, Bo-Wen; Tao, Wei-Kuo; Chern, Jiun-Dar

2007-01-01

Improving our understanding of hurricane inter-annual variability and the impact of climate change (e.g., doubling CO2 and/or global warming) on hurricanes brings both scientific and computational challenges to researchers. As hurricane dynamics involves multiscale interactions among synoptic-scale flows, mesoscale vortices, and small-scale cloud motions, an ideal numerical model suitable for hurricane studies should demonstrate its capabilities in simulating these interactions. The newly-developed multiscale modeling framework (MMF, Tao et al., 2007) and the substantial computing power by the NASA Columbia supercomputer show promise in pursuing the related studies, as the MMF inherits the advantages of two NASA state-of-the-art modeling components: the GEOS4/fvGCM and 2D GCEs. This article focuses on the computational issues and proposes a revised methodology to improve the MMF's performance and scalability. It is shown that this prototype implementation enables 12-fold performance improvements with 364 CPUs, thereby making it more feasible to study hurricane climate.

National Laboratory for Advanced Scientific Visualization at UNAM - Mexico

NASA Astrophysics Data System (ADS)

Manea, Marina; Constantin Manea, Vlad; Varela, Alfredo

2016-04-01

In 2015, the National Autonomous University of Mexico (UNAM) joined the family of Universities and Research Centers where advanced visualization and computing plays a key role to promote and advance missions in research, education, community outreach, as well as business-oriented consulting. This initiative provides access to a great variety of advanced hardware and software resources and offers a range of consulting services that spans a variety of areas related to scientific visualization, among which are: neuroanatomy, embryonic development, genome related studies, geosciences, geography, physics and mathematics related disciplines. The National Laboratory for Advanced Scientific Visualization delivers services through three main infrastructure environments: the 3D fully immersive display system Cave, the high resolution parallel visualization system Powerwall, the high resolution spherical displays Earth Simulator. The entire visualization infrastructure is interconnected to a high-performance-computing-cluster (HPCC) called ADA in honor to Ada Lovelace, considered to be the first computer programmer. The Cave is an extra large 3.6m wide room with projected images on the front, left and right, as well as floor walls. Specialized crystal eyes LCD-shutter glasses provide a strong stereo depth perception, and a variety of tracking devices allow software to track the position of a user's hand, head and wand. The Powerwall is designed to bring large amounts of complex data together through parallel computing for team interaction and collaboration. This system is composed by 24 (6x4) high-resolution ultra-thin (2 mm) bezel monitors connected to a high-performance GPU cluster. The Earth Simulator is a large (60") high-resolution spherical display used for global-scale data visualization like geophysical, meteorological, climate and ecology data. The HPCC-ADA, is a 1000+ computing core system, which offers parallel computing resources to applications that requires large quantity of memory as well as large and fast parallel storage systems. The entire system temperature is controlled by an energy and space efficient cooling solution, based on large rear door liquid cooled heat exchangers. This state-of-the-art infrastructure will boost research activities in the region, offer a powerful scientific tool for teaching at undergraduate and graduate levels, and enhance association and cooperation with business-oriented organizations.

Applied Mathematics at the U.S. Department of Energy: Past, Present and a View to the Future

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, D L; Bell, J; Estep, D

2008-02-15

Over the past half-century, the Applied Mathematics program in the U.S. Department of Energy's Office of Advanced Scientific Computing Research has made significant, enduring advances in applied mathematics that have been essential enablers of modern computational science. Motivated by the scientific needs of the Department of Energy and its predecessors, advances have been made in mathematical modeling, numerical analysis of differential equations, optimization theory, mesh generation for complex geometries, adaptive algorithms and other important mathematical areas. High-performance mathematical software libraries developed through this program have contributed as much or more to the performance of modern scientific computer codes as themore » high-performance computers on which these codes run. The combination of these mathematical advances and the resulting software has enabled high-performance computers to be used for scientific discovery in ways that could only be imagined at the program's inception. Our nation, and indeed our world, face great challenges that must be addressed in coming years, and many of these will be addressed through the development of scientific understanding and engineering advances yet to be discovered. The U.S. Department of Energy (DOE) will play an essential role in providing science-based solutions to many of these problems, particularly those that involve the energy, environmental and national security needs of the country. As the capability of high-performance computers continues to increase, the types of questions that can be answered by applying this huge computational power become more varied and more complex. It will be essential that we find new ways to develop and apply the mathematics necessary to enable the new scientific and engineering discoveries that are needed. In August 2007, a panel of experts in applied, computational and statistical mathematics met for a day and a half in Berkeley, California to understand the mathematical developments required to meet the future science and engineering needs of the DOE. It is important to emphasize that the panelists were not asked to speculate only on advances that might be made in their own research specialties. Instead, the guidance this panel was given was to consider the broad science and engineering challenges that the DOE faces and identify the corresponding advances that must occur across the field of mathematics for these challenges to be successfully addressed. As preparation for the meeting, each panelist was asked to review strategic planning and other informational documents available for one or more of the DOE Program Offices, including the Offices of Science, Nuclear Energy, Fossil Energy, Environmental Management, Legacy Management, Energy Efficiency & Renewable Energy, Electricity Delivery & Energy Reliability and Civilian Radioactive Waste Management as well as the National Nuclear Security Administration. The panelists reported on science and engineering needs for each of these offices, and then discussed and identified mathematical advances that will be required if these challenges are to be met. A review of DOE challenges in energy, the environment and national security brings to light a broad and varied array of questions that the DOE must answer in the coming years. A representative subset of such questions includes: (1) Can we predict the operating characteristics of a clean coal power plant? (2) How stable is the plasma containment in a tokamak? (3) How quickly is climate change occurring and what are the uncertainties in the predicted time scales? (4) How quickly can an introduced bio-weapon contaminate the agricultural environment in the US? (5) How do we modify models of the atmosphere and clouds to incorporate newly collected data of possibly of new types? (6) How quickly can the United States recover if part of the power grid became inoperable? (7) What are optimal locations and communication protocols for sensing devices in a remote-sensing network? (8) How can new materials be designed with a specified desirable set of properties? In comparing and contrasting these and other questions of importance to DOE, the panel found that while the scientific breadth of the requirements is enormous, a central theme emerges: Scientists are being asked to identify or provide technology, or to give expert analysis to inform policy-makers that requires the scientific understanding of increasingly complex physical and engineered systems. In addition, as the complexity of the systems of interest increases, neither experimental observation nor mathematical and computational modeling alone can access all components of the system over the entire range of scales or conditions needed to provide the required scientific understanding.« less

Preface: SciDAC 2005

NASA Astrophysics Data System (ADS)

Mezzacappa, Anthony

2005-01-01

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.

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.

On Establishing Big Data Wave Breakwaters with Analytics (Invited)

NASA Astrophysics Data System (ADS)

Riedel, M.

2013-12-01

The Research Data Alliance Big Data Analytics (RDA-BDA) Interest Group seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. RDA-BDA seeks to analyze different scientific domain applications and their potential use of various big data analytics techniques. A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. These combinations are complex since a wide variety of different data analysis algorithms exist (e.g. specific algorithms using GPUs of analyzing brain images) that need to work together with multiple analytical tools reaching from simple (iterative) map-reduce methods (e.g. with Apache Hadoop or Twister) to sophisticated higher level frameworks that leverage machine learning algorithms (e.g. Apache Mahout). These computational analysis techniques are often augmented with visual analytics techniques (e.g. computational steering on large-scale high performance computing platforms) to put the human judgement into the analysis loop or new approaches with databases that are designed to support new forms of unstructured or semi-structured data as opposed to the rather tradtional structural databases (e.g. relational databases). More recently, data analysis and underpinned analytics frameworks also have to consider energy footprints of underlying resources. To sum up, the aim of this talk is to provide pieces of information to understand big data analytics in the context of science and engineering using the aforementioned classification as the lighthouse and as the frame of reference for a systematic approach. This talk will provide insights about big data analytics methods in context of science within varios communities and offers different views of how approaches of correlation and causality offer complementary methods to advance in science and engineering today. The RDA Big Data Analytics Group seeks to understand what approaches are not only technically feasible, but also scientifically feasible. The lighthouse Goal of the RDA Big Data Analytics Group is a classification of clever combinations of various Technologies and scientific applications in order to provide clear recommendations to the scientific community what approaches are technicalla and scientifically feasible.

Center for Technology for Advanced Scientific Componet Software (TASCS)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Govindaraju, Madhusudhan

Advanced Scientific Computing Research Computer Science FY 2010Report Center for Technology for Advanced Scientific Component Software: Distributed CCA State University of New York, Binghamton, NY, 13902 Summary The overall objective of Binghamton's involvement is to work on enhancements of the CCA environment, motivated by the applications and research initiatives discussed in the proposal. This year we are working on re-focusing our design and development efforts to develop proof-of-concept implementations that have the potential to significantly impact scientific components. We worked on developing parallel implementations for non-hydrostatic code and worked on a model coupling interface for biogeochemical computations coded in MATLAB.more » We also worked on the design and implementation modules that will be required for the emerging MapReduce model to be effective for scientific applications. Finally, we focused on optimizing the processing of scientific datasets on multi-core processors. Research Details We worked on the following research projects that we are working on applying to CCA-based scientific applications. 1. Non-Hydrostatic Hydrodynamics: Non-static hydrodynamics are significantly more accurate at modeling internal waves that may be important in lake ecosystems. Non-hydrostatic codes, however, are significantly more computationally expensive, often prohibitively so. We have worked with Chin Wu at the University of Wisconsin to parallelize non-hydrostatic code. We have obtained a speed up of about 26 times maximum. Although this is significant progress, we hope to improve the performance further, such that it becomes a practical alternative to hydrostatic codes. 2. Model-coupling for water-based ecosystems: To answer pressing questions about water resources requires that physical models (hydrodynamics) be coupled with biological and chemical models. Most hydrodynamics codes are written in Fortran, however, while most ecologists work in MATLAB. This disconnect creates a great barrier. To address this, we are working on a model coupling interface that will allow biogeochemical computations written in MATLAB to couple with Fortran codes. This will greatly improve the productivity of ecosystem scientists. 2. Low overhead and Elastic MapReduce Implementation Optimized for Memory and CPU-Intensive Applications: Since its inception, MapReduce has frequently been associated with Hadoop and large-scale datasets. Its deployment at Amazon in the cloud, and its applications at Yahoo! for large-scale distributed document indexing and database building, among other tasks, have thrust MapReduce to the forefront of the data processing application domain. The applicability of the paradigm however extends far beyond its use with data intensive applications and diskbased systems, and can also be brought to bear in processing small but CPU intensive distributed applications. MapReduce however carries its own burdens. Through experiments using Hadoop in the context of diverse applications, we uncovered latencies and delay conditions potentially inhibiting the expected performance of a parallel execution in CPU-intensive applications. Furthermore, as it currently stands, MapReduce is favored for data-centric applications, and as such tends to be solely applied to disk-based applications. The paradigm, falls short in bringing its novelty to diskless systems dedicated to in-memory applications, and compute intensive programs processing much smaller data, but requiring intensive computations. In this project, we focused both on the performance of processing large-scale hierarchical data in distributed scientific applications, as well as the processing of smaller but demanding input sizes primarily used in diskless, and memory resident I/O systems. We designed LEMO-MR [1], a Low overhead, elastic, configurable for in- memory applications, and on-demand fault tolerance, an optimized implementation of MapReduce, for both on disk and in memory applications. We conducted experiments to identify not only the necessary components of this model, but also trade offs and factors to be considered. We have initial results to show the efficacy of our implementation in terms of potential speedup that can be achieved for representative data sets used by cloud applications. We have quantified the performance gains exhibited by our MapReduce implementation over Apache Hadoop in a compute intensive environment. 3. Cache Performance Optimization for Processing XML and HDF-based Application Data on Multi-core Processors: It is important to design and develop scientific middleware libraries to harness the opportunities presented by emerging multi-core processors. Implementations of scientific middleware and applications that do not adapt to the programming paradigm when executing on emerging processors can severely impact the overall performance. In this project, we focused on the utilization of the L2 cache, which is a critical shared resource on chip multiprocessors (CMP). The access pattern of the shared L2 cache, which is dependent on how the application schedules and assigns processing work to each thread, can either enhance or hurt the ability to hide memory latency on a multi-core processor. Therefore, while processing scientific datasets such as HDF5, it is essential to conduct fine-grained analysis of cache utilization, to inform scheduling decisions in multi-threaded programming. In this project, using the TAU toolkit for performance feedback from dual- and quad-core machines, we conducted performance analysis and recommendations on how processing threads can be scheduled on multi-core nodes to enhance the performance of a class of scientific applications that requires processing of HDF5 data. In particular, we quantified the gains associated with the use of the adaptations we have made to the Cache-Affinity and Balanced-Set scheduling algorithms to improve L2 cache performance, and hence the overall application execution time [2]. References: 1. Zacharia Fadika, Madhusudhan Govindaraju, ``MapReduce Implementation for Memory-Based and Processing Intensive Applications'', accepted in 2nd IEEE International Conference on Cloud Computing Technology and Science, Indianapolis, USA, Nov 30 - Dec 3, 2010. 2. Rajdeep Bhowmik, Madhusudhan Govindaraju, ``Cache Performance Optimization for Processing XML-based Application Data on Multi-core Processors'', in proceedings of The 10th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, May 17-20, 2010, Melbourne, Victoria, Australia. Contact Information: Madhusudhan Govindaraju Binghamton University State University of New York (SUNY) mgovinda@cs.binghamton.edu Phone: 607-777-4904« less

RIKEN BNL Research Center

NASA Astrophysics Data System (ADS)

Samios, Nicholas

2014-09-01

Since its inception in 1997, the RIKEN BNL Research Center (RBRC) has been a major force in the realms of Spin Physics, Relativistic Heavy Ion Physics, large scale Computing Physics and the training of a new generation of extremely talented physicists. This has been accomplished through the recruitment of an outstanding non-permanent staff of Fellows and Research associates in theory and experiment. RBRC is now a mature organization that has reached a steady level in the size of scientific and support staff while at the same time retaining its vibrant youth. A brief history of the scientific accomplishments and contributions of the RBRC physicists will be presented as well as a discussion of the unique RBRC management structure.

Toward Transparent Data Management in Multi-layer Storage Hierarchy for HPC Systems

DOE PAGES

Wadhwa, Bharti; Byna, Suren; Butt, Ali R.

2018-04-17

Upcoming exascale high performance computing (HPC) systems are expected to comprise multi-tier storage hierarchy, and thus will necessitate innovative storage and I/O mechanisms. Traditional disk and block-based interfaces and file systems face severe challenges in utilizing capabilities of storage hierarchies due to the lack of hierarchy support and semantic interfaces. Object-based and semantically-rich data abstractions for scientific data management on large scale systems offer a sustainable solution to these challenges. Such data abstractions can also simplify users involvement in data movement. Here, we take the first steps of realizing such an object abstraction and explore storage mechanisms for these objectsmore » to enhance I/O performance, especially for scientific applications. We explore how an object-based interface can facilitate next generation scalable computing systems by presenting the mapping of data I/O from two real world HPC scientific use cases: a plasma physics simulation code (VPIC) and a cosmology simulation code (HACC). Our storage model stores data objects in different physical organizations to support data movement across layers of memory/storage hierarchy. Our implementation sclaes well to 16K parallel processes, and compared to the state of the art, such as MPI-IO and HDF5, our object-based data abstractions and data placement strategy in multi-level storage hierarchy achieves up to 7 X I/O performance improvement for scientific data.« less

Toward Transparent Data Management in Multi-layer Storage Hierarchy for HPC Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wadhwa, Bharti; Byna, Suren; Butt, Ali R.

Upcoming exascale high performance computing (HPC) systems are expected to comprise multi-tier storage hierarchy, and thus will necessitate innovative storage and I/O mechanisms. Traditional disk and block-based interfaces and file systems face severe challenges in utilizing capabilities of storage hierarchies due to the lack of hierarchy support and semantic interfaces. Object-based and semantically-rich data abstractions for scientific data management on large scale systems offer a sustainable solution to these challenges. Such data abstractions can also simplify users involvement in data movement. Here, we take the first steps of realizing such an object abstraction and explore storage mechanisms for these objectsmore » to enhance I/O performance, especially for scientific applications. We explore how an object-based interface can facilitate next generation scalable computing systems by presenting the mapping of data I/O from two real world HPC scientific use cases: a plasma physics simulation code (VPIC) and a cosmology simulation code (HACC). Our storage model stores data objects in different physical organizations to support data movement across layers of memory/storage hierarchy. Our implementation sclaes well to 16K parallel processes, and compared to the state of the art, such as MPI-IO and HDF5, our object-based data abstractions and data placement strategy in multi-level storage hierarchy achieves up to 7 X I/O performance improvement for scientific data.« less

Building Nationally-Focussed, Globally Federated, High Performance Earth Science Platforms to Solve Next Generation Social and Economic Issues.

NASA Astrophysics Data System (ADS)

Wyborn, Lesley; Evans, Ben; Foster, Clinton; Pugh, Timothy; Uhlherr, Alfred

2015-04-01

Digital geoscience data and information are integral to informing decisions on the social, economic and environmental management of natural resources. Traditionally, such decisions were focused on regional or national viewpoints only, but it is increasingly being recognised that global perspectives are required to meet new challenges such as predicting impacts of climate change; sustainably exploiting scarce water, mineral and energy resources; and protecting our communities through better prediction of the behaviour of natural hazards. In recent years, technical advances in scientific instruments have resulted in a surge in data volumes, with data now being collected at unprecedented rates and at ever increasing resolutions. The size of many earth science data sets now exceed the computational capacity of many government and academic organisations to locally store and dynamically access the data sets; to internally process and analyse them to high resolutions; and then to deliver them online to clients, partners and stakeholders. Fortunately, at the same time, computational capacities have commensurately increased (both cloud and HPC): these can now provide the capability to effectively access the ever-growing data assets within realistic time frames. However, to achieve this, data and computing need to be co-located: bandwidth limits the capacity to move the large data sets; the data transfers are too slow; and latencies to access them are too high. These scenarios are driving the move towards more centralised High Performance (HP) Infrastructures. The rapidly increasing scale of data, the growing complexity of software and hardware environments, combined with the energy costs of running such infrastructures is creating a compelling economic argument for just having one or two major national (or continental) HP facilities that can be federated internationally to enable earth and environmental issues to be tackled at global scales. But at the same time, if properly constructed, these infrastructures can also service very small-scale research projects. The National Computational Infrastructure (NCI) at the Australian National University (ANU) has built such an HP infrastructure as part of the Australian Government's National Collaborative Research Infrastructure Strategy. NCI operates as a formal partnership between the ANU and the three major Australian National Government Scientific Agencies: the Commonwealth Scientific and Industrial Research Organisation (CSIRO), the Bureau of Meteorology and Geoscience Australia. The government partners agreed to explore the new opportunities offered within the partnership with NCI, rather than each running their own separate agenda independently. The data from these national agencies, as well as from collaborating overseas organisations (e.g., NASA, NOAA, USGS, CMIP, etc.) are either replicated to, or produced at, NCI. By co-locating and harmonising these vast data collections within the integrated HP computing environments at NCI, new opportunities have arisen for Data-intensive Interdisciplinary Science at scales and resolutions not hitherto possible. The new NCI infrastructure has also enabled the blending of research by the university sector with the more operational business of government science agencies, with the fundamental shift being that researchers from both sectors work and collaborate within a federated data and computational environment that contains both national and international data collections.

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.

Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Dean N.; Silva, Claudio

2013-09-30

For the past three years, a large analysis and visualization effort—funded by the Department of Energy’s Office of Biological and Environmental Research (BER), the National Aeronautics and Space Administration (NASA), and the National Oceanic and Atmospheric Administration (NOAA)—has brought together a wide variety of industry-standard scientific computing libraries and applications to create Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT) to serve the global climate simulation and observational research communities. To support interactive analysis and visualization, all components connect through a provenance application–programming interface to capture meaningful history and workflow. Components can be loosely coupled into the framework for fast integrationmore » or tightly coupled for greater system functionality and communication with other components. The overarching goal of UV-CDAT is to provide a new paradigm for access to and analysis of massive, distributed scientific data collections by leveraging distributed data architectures located throughout the world. The UV-CDAT framework addresses challenges in analysis and visualization and incorporates new opportunities, including parallelism for better efficiency, higher speed, and more accurate scientific inferences. Today, it provides more than 600 users access to more analysis and visualization products than any other single source.« less

Adaptive LES Methodology for Turbulent Flow Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oleg V. Vasilyev

2008-06-12

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

78 FR 41046 - Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-09

... 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...

Cloud computing and validation of expandable in silico livers

PubMed Central

2010-01-01

Background In Silico Livers (ISLs) are works in progress. They are used to challenge multilevel, multi-attribute, mechanistic hypotheses about the hepatic disposition of xenobiotics coupled with hepatic responses. To enhance ISL-to-liver mappings, we added discrete time metabolism, biliary elimination, and bolus dosing features to a previously validated ISL and initiated re-validated experiments that required scaling experiments to use more simulated lobules than previously, more than could be achieved using the local cluster technology. Rather than dramatically increasing the size of our local cluster we undertook the re-validation experiments using the Amazon EC2 cloud platform. So doing required demonstrating the efficacy of scaling a simulation to use more cluster nodes and assessing the scientific equivalence of local cluster validation experiments with those executed using the cloud platform. Results The local cluster technology was duplicated in the Amazon EC2 cloud platform. Synthetic modeling protocols were followed to identify a successful parameterization. Experiment sample sizes (number of simulated lobules) on both platforms were 49, 70, 84, and 152 (cloud only). Experimental indistinguishability was demonstrated for ISL outflow profiles of diltiazem using both platforms for experiments consisting of 84 or more samples. The process was analogous to demonstration of results equivalency from two different wet-labs. Conclusions The results provide additional evidence that disposition simulations using ISLs can cover the behavior space of liver experiments in distinct experimental contexts (there is in silico-to-wet-lab phenotype similarity). The scientific value of experimenting with multiscale biomedical models has been limited to research groups with access to computer clusters. The availability of cloud technology coupled with the evidence of scientific equivalency has lowered the barrier and will greatly facilitate model sharing as well as provide straightforward tools for scaling simulations to encompass greater detail with no extra investment in hardware. PMID:21129207

Changing computing paradigms towards power efficiency.

PubMed

Klavík, Pavel; Malossi, A Cristiano I; Bekas, Costas; Curioni, Alessandro

2014-06-28

Power awareness is fast becoming immensely important in computing, ranging from the traditional high-performance computing applications to the new generation of data centric workloads. In this work, we describe our efforts towards a power-efficient computing paradigm that combines low- and high-precision arithmetic. We showcase our ideas for the widely used kernel of solving systems of linear equations that finds numerous applications in scientific and engineering disciplines as well as in large-scale data analytics, statistics and machine learning. Towards this goal, we developed tools for the seamless power profiling of applications at a fine-grain level. In addition, we verify here previous work on post-FLOPS/W metrics and show that these can shed much more light in the power/energy profile of important applications. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

DOE PAGES

Barrett, R. F.; Crozier, P. S.; Doerfler, D. W.; ...

2014-09-28

Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of miniapps has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively representmore » these performance issues. We applied this methodology to four miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, ''Under what conditions does a miniapp represent a key performance characteristic in a full app?''« less

Cross-Identification of Astronomical Catalogs on Multiple GPUs

NASA Astrophysics Data System (ADS)

Lee, M. A.; Budavári, T.

2013-10-01

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.

Requirements for migration of NSSD code systems from LTSS to NLTSS

NASA Technical Reports Server (NTRS)

Pratt, M.

1984-01-01

The purpose of this document is to address the requirements necessary for a successful conversion of the Nuclear Design (ND) application code systems to the NLTSS environment. The ND application code system community can be characterized as large-scale scientific computation carried out on supercomputers. NLTSS is a distributed operating system being developed at LLNL to replace the LTSS system currently in use. The implications of change are examined including a description of the computational environment and users in ND. The discussion then turns to requirements, first in a general way, followed by specific requirements, including a proposal for managing the transition.

Geoscience Through the Lens of Art: a collaborative course of science and art for undergraduates of various disciplines

NASA Astrophysics Data System (ADS)

Ellins, K. K.; Eriksson, S. C.; Samsel, F.; Lavier, L.

2017-12-01

A new undergraduate, upper level geoscience course was developed and taught by faculty and staff of the UT Austin Jackson School of Geosciences, the Center for Agile Technology, and the Texas Advanced Computational Center. The course examined the role of the visual arts in placing the scientific process and knowledge in a broader context and introduced students to innovations in the visual arts that promote scientific investigation through collaboration between geoscientists and artists. The course addressed (1) the role of the visual arts in teaching geoscience concepts and promoting geoscience learning; (2) the application of innovative visualization and artistic techniques to large volumes of geoscience data to enhance scientific understanding and to move scientific investigation forward; and (3) the illustrative power of art to communicate geoscience to the public. In-class activities and discussions, computer lab instruction on the application of Paraview software, reading assignments, lectures, and group projects with presentations comprised the two-credit, semester-long "special topics" course, which was taken by geoscience, computer science, and engineering students. Assessment of student learning was carried out by the instructors and course evaluation was done by an external evaluator using rubrics, likert-scale surveys and focus goups. The course achieved its goals of students' learning the concepts and techniques of the visual arts. The final projects demonstrated this, along with the communication of geologic concepts using what they had learned in the course. The basic skill of sketching for learning and using best practices in visual communication were used extensively and, in most cases, very effectively. The use of an advanced visualization tool, Paraview, was received with mixed reviews because of the lack of time to really learn the tool and the fact that it is not a tool used routinely in geoscience. Those senior students with advanced computer skills saw the importance of this tool. Students worked in teams, more or less effectively, and made suggestions for improving future offerings of the course.

Opal web services for biomedical applications.

PubMed

Ren, Jingyuan; Williams, Nadya; Clementi, Luca; Krishnan, Sriram; Li, Wilfred W

2010-07-01

Biomedical applications have become increasingly complex, and they often require large-scale high-performance computing resources with a large number of processors and memory. The complexity of application deployment and the advances in cluster, grid and cloud computing require new modes of support for biomedical research. Scientific Software as a Service (sSaaS) enables scalable and transparent access to biomedical applications through simple standards-based Web interfaces. Towards this end, we built a production web server (http://ws.nbcr.net) in August 2007 to support the bioinformatics application called MEME. The server has grown since to include docking analysis with AutoDock and AutoDock Vina, electrostatic calculations using PDB2PQR and APBS, and off-target analysis using SMAP. All the applications on the servers are powered by Opal, a toolkit that allows users to wrap scientific applications easily as web services without any modification to the scientific codes, by writing simple XML configuration files. Opal allows both web forms-based access and programmatic access of all our applications. The Opal toolkit currently supports SOAP-based Web service access to a number of popular applications from the National Biomedical Computation Resource (NBCR) and affiliated collaborative and service projects. In addition, Opal's programmatic access capability allows our applications to be accessed through many workflow tools, including Vision, Kepler, Nimrod/K and VisTrails. From mid-August 2007 to the end of 2009, we have successfully executed 239,814 jobs. The number of successfully executed jobs more than doubled from 205 to 411 per day between 2008 and 2009. The Opal-enabled service model is useful for a wide range of applications. It provides for interoperation with other applications with Web Service interfaces, and allows application developers to focus on the scientific tool and workflow development. Web server availability: http://ws.nbcr.net.

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus

PubMed Central

Karp, Peter D.; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida). PMID:26097686

ISCB Ebola Award for Important Future Research on the Computational Biology of Ebola Virus.

PubMed

Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-01-01

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains as well as 3-D protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature, and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2,000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology (ISMB) 2016, Orlando, Florida).

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

NASA Technical Reports Server (NTRS)

Vogelmann, Andrew M.; McFarquhar, Greg M.; Ogren, John A.; Turner, David D.; Comstock, Jennifer M.; Feingold, Graham; Long, Charles N.; Jonsson, Haflidi H.; Bucholtz, Anthony; Collins, Don R.;

Report of the Working Group on Large-Scale Computing in Aeronautics.

DTIC Science & Technology

1984-06-01

incompressible approximations that are presently made in the lifting line or lifting surface representations of rotor blades. Finally, viscous effects in the forms... Effects of Rotor Model Degradation in the Accuracy of Rotocraft Real-Time Simulation, NASA TN D-8378;1977. 20. Gullen, R. K., Cattell, C. S., and Overton...assistance to member nations for the purpose of increasing their scientific and technical potential; - Recommending effective ways for the member nations

Control Circuitry for High Speed VLSI (Very Large Scale Integration) Winograd Fourier Transform Processors.

DTIC Science & Technology

1985-12-01

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

The Science DMZ: A Network Design Pattern for Data-Intensive Science

DOE PAGES

Dart, Eli; Rotman, Lauren; Tierney, Brian; ...

2014-01-01

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

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.

Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers

DOE PAGES

Basu, Protonu; Williams, Samuel; Van Straalen, Brian; ...

2017-04-05

GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model like CUDA, OpenCL, or OpenACC. Thus, in order to deliver portability across CPU-based and GPU-accelerated supercomputers, programmers are forced to write and maintain two versions of their applications or frameworks. In this paper, we explore the use of a compiler-based autotuning framework based on CUDA-CHiLL to deliver not only portability, but also performance portability across CPU- and GPU-accelerated platforms for the geometric multigrid linear solvers found inmore » many scientific applications. We also show that with autotuning we can attain near Roofline (a performance bound for a computation and target architecture) performance across the key operations in the miniGMG benchmark for both CPU- and GPU-based architectures as well as for a multiple stencil discretizations and smoothers. We show that our technology is readily interoperable with MPI resulting in performance at scale equal to that obtained via hand-optimized MPI+CUDA implementation.« less

Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse.

PubMed

Eppig, Janan T

2017-07-01

The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided. © The Author 2017. Published by Oxford University Press.

Making Advanced Scientific Algorithms and Big Scientific Data Management More Accessible

DOE Office of Scientific and Technical Information (OSTI.GOV)

Venkatakrishnan, S. V.; Mohan, K. Aditya; Beattie, Keith

2016-02-14

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are known as user facilities. They are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resourcesmore » to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography).« less

Compiler-based code generation and autotuning for geometric multigrid on GPU-accelerated supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Basu, Protonu; Williams, Samuel; Van Straalen, Brian

GPUs, with their high bandwidths and computational capabilities are an increasingly popular target for scientific computing. Unfortunately, to date, harnessing the power of the GPU has required use of a GPU-specific programming model like CUDA, OpenCL, or OpenACC. Thus, in order to deliver portability across CPU-based and GPU-accelerated supercomputers, programmers are forced to write and maintain two versions of their applications or frameworks. In this paper, we explore the use of a compiler-based autotuning framework based on CUDA-CHiLL to deliver not only portability, but also performance portability across CPU- and GPU-accelerated platforms for the geometric multigrid linear solvers found inmore » many scientific applications. We also show that with autotuning we can attain near Roofline (a performance bound for a computation and target architecture) performance across the key operations in the miniGMG benchmark for both CPU- and GPU-based architectures as well as for a multiple stencil discretizations and smoothers. We show that our technology is readily interoperable with MPI resulting in performance at scale equal to that obtained via hand-optimized MPI+CUDA implementation.« less

Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse

PubMed Central

Eppig, Janan T.

2017-01-01

Abstract The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided. PMID:28838066

Science Gateways, Scientific Workflows and Open Community Software

NASA Astrophysics Data System (ADS)

Pierce, M. E.; Marru, S.

2014-12-01

Science gateways and scientific workflows occupy different ends of the spectrum of user-focused cyberinfrastructure. Gateways, sometimes called science portals, provide a way for enabling large numbers of users to take advantage of advanced computing resources (supercomputers, advanced storage systems, science clouds) by providing Web and desktop interfaces and supporting services. Scientific workflows, at the other end of the spectrum, support advanced usage of cyberinfrastructure that enable "power users" to undertake computational experiments that are not easily done through the usual mechanisms (managing simulations across multiple sites, for example). Despite these different target communities, gateways and workflows share many similarities and can potentially be accommodated by the same software system. For example, pipelines to process InSAR imagery sets or to datamine GPS time series data are workflows. The results and the ability to make downstream products may be made available through a gateway, and power users may want to provide their own custom pipelines. In this abstract, we discuss our efforts to build an open source software system, Apache Airavata, that can accommodate both gateway and workflow use cases. Our approach is general, and we have applied the software to problems in a number of scientific domains. In this talk, we discuss our applications to usage scenarios specific to earth science, focusing on earthquake physics examples drawn from the QuakSim.org and GeoGateway.org efforts. We also examine the role of the Apache Software Foundation's open community model as a way to build up common commmunity codes that do not depend upon a single "owner" to sustain. Pushing beyond open source software, we also see the need to provide gateways and workflow systems as cloud services. These services centralize operations, provide well-defined programming interfaces, scale elastically, and have global-scale fault tolerance. We discuss our work providing Apache Airavata as a hosted service to provide these features.

Extreme Scale Computing to Secure the Nation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, D L; McGraw, J R; Johnson, J R

2009-11-10

Since the dawn of modern electronic computing in the mid 1940's, U.S. national security programs have been dominant users of every new generation of high-performance computer. Indeed, the first general-purpose electronic computer, ENIAC (the Electronic Numerical Integrator and Computer), was used to calculate the expected explosive yield of early thermonuclear weapons designs. Even the U. S. numerical weather prediction program, another early application for high-performance computing, was initially funded jointly by sponsors that included the U.S. Air Force and Navy, agencies interested in accurate weather predictions to support U.S. military operations. For the decades of the cold war, national securitymore » requirements continued to drive the development of high performance computing (HPC), including advancement of the computing hardware and development of sophisticated simulation codes to support weapons and military aircraft design, numerical weather prediction as well as data-intensive applications such as cryptography and cybersecurity U.S. national security concerns continue to drive the development of high-performance computers and software in the U.S. and in fact, events following the end of the cold war have driven an increase in the growth rate of computer performance at the high-end of the market. This mainly derives from our nation's observance of a moratorium on underground nuclear testing beginning in 1992, followed by our voluntary adherence to the Comprehensive Test Ban Treaty (CTBT) beginning in 1995. The CTBT prohibits further underground nuclear tests, which in the past had been a key component of the nation's science-based program for assuring the reliability, performance and safety of U.S. nuclear weapons. In response to this change, the U.S. Department of Energy (DOE) initiated the Science-Based Stockpile Stewardship (SBSS) program in response to the Fiscal Year 1994 National Defense Authorization Act, which requires, 'in the absence of nuclear testing, a progam to: (1) Support a focused, multifaceted program to increase the understanding of the enduring stockpile; (2) Predict, detect, and evaluate potential problems of the aging of the stockpile; (3) Refurbish and re-manufacture weapons and components, as required; and (4) Maintain the science and engineering institutions needed to support the nation's nuclear deterrent, now and in the future'. This program continues to fulfill its national security mission by adding significant new capabilities for producing scientific results through large-scale computational simulation coupled with careful experimentation, including sub-critical nuclear experiments permitted under the CTBT. To develop the computational science and the computational horsepower needed to support its mission, SBSS initiated the Accelerated Strategic Computing Initiative, later renamed the Advanced Simulation & Computing (ASC) program (sidebar: 'History of ASC Computing Program Computing Capability'). The modern 3D computational simulation capability of the ASC program supports the assessment and certification of the current nuclear stockpile through calibration with past underground test (UGT) data. While an impressive accomplishment, continued evolution of national security mission requirements will demand computing resources at a significantly greater scale than we have today. In particular, continued observance and potential Senate confirmation of the Comprehensive Test Ban Treaty (CTBT) together with the U.S administration's promise for a significant reduction in the size of the stockpile and the inexorable aging and consequent refurbishment of the stockpile all demand increasing refinement of our computational simulation capabilities. Assessment of the present and future stockpile with increased confidence of the safety and reliability without reliance upon calibration with past or future test data is a long-term goal of the ASC program. This will be accomplished through significant increases in the scientific bases that underlie the computational tools. Computer codes must be developed that replace phenomenology with increased levels of scientific understanding together with an accompanying quantification of uncertainty. These advanced codes will place significantly higher demands on the computing infrastructure than do the current 3D ASC codes. This article discusses not only the need for a future computing capability at the exascale for the SBSS program, but also considers high performance computing requirements for broader national security questions. For example, the increasing concern over potential nuclear terrorist threats demands a capability to assess threats and potential disablement technologies as well as a rapid forensic capability for determining a nuclear weapons design from post-detonation evidence (nuclear counterterrorism).« less

GPU Multi-Scale Particle Tracking and Multi-Fluid Simulations of the Radiation Belts

NASA Astrophysics Data System (ADS)

Ziemba, T.; Carscadden, J.; O'Donnell, D.; Winglee, R.; Harnett, E.; Cash, M.

2007-12-01

The properties of the radiation belts can vary dramatically under the influence of magnetic storms and storm-time substorms. The task of understanding and predicting radiation belt properties is made difficult because their properties determined by global processes as well as small-scale wave-particle interactions. A full solution to the problem will require major innovations in technique and computer hardware. The proposed work will demonstrates liked particle tracking codes with new multi-scale/multi-fluid global simulations that provide the first means to include small-scale processes within the global magnetospheric context. A large hurdle to the problem is having sufficient computer hardware that is able to handle the dissipate temporal and spatial scale sizes. A major innovation of the work is that the codes are designed to run of graphics processing units (GPUs). GPUs are intrinsically highly parallelized systems that provide more than an order of magnitude computing speed over a CPU based systems, for little more cost than a high end-workstation. Recent advancements in GPU technologies allow for full IEEE float specifications with performance up to several hundred GFLOPs per GPU and new software architectures have recently become available to ease the transition from graphics based to scientific applications. This allows for a cheap alternative to standard supercomputing methods and should increase the time to discovery. A demonstration of the code pushing more than 500,000 particles faster than real time is presented, and used to provide new insight into radiation belt dynamics.

A Component Approach to Collaborative Scientific Software Development: Tools and Techniques Utilized by the Quantum Chemistry Science Application Partnership

DOE PAGES

Kenny, Joseph P.; Janssen, Curtis L.; Gordon, Mark S.; ...

2008-01-01

Cutting-edge scientific computing software is complex, increasingly involving the coupling of multiple packages to combine advanced algorithms or simulations at multiple physical scales. Component-based software engineering (CBSE) has been advanced as a technique for managing this complexity, and complex component applications have been created in the quantum chemistry domain, as well as several other simulation areas, using the component model advocated by the Common Component Architecture (CCA) Forum. While programming models do indeed enable sound software engineering practices, the selection of programming model is just one building block in a comprehensive approach to large-scale collaborative development which must also addressmore » interface and data standardization, and language and package interoperability. We provide an overview of the development approach utilized within the Quantum Chemistry Science Application Partnership, identifying design challenges, describing the techniques which we have adopted to address these challenges and highlighting the advantages which the CCA approach offers for collaborative development.« less

Integrating Data Base into the Elementary School Science Program.

ERIC Educational Resources Information Center

Schlenker, Richard M.

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…

A high performance scientific cloud computing environment for materials simulations

NASA Astrophysics Data System (ADS)

Jorissen, K.; Vila, F. D.; Rehr, J. J.

2012-09-01

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.

High Energy Physics Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and High Energy Physics, June 10-12, 2015, Bethesda, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Habib, Salman; Roser, Robert; Gerber, Richard

The U.S. Department of Energy (DOE) Office of Science (SC) Offices of High Energy Physics (HEP) and Advanced Scientific Computing Research (ASCR) convened a programmatic Exascale Requirements Review on June 10–12, 2015, in Bethesda, Maryland. This report summarizes the findings, results, and recommendations derived from that meeting. The high-level findings and observations are as follows. Larger, more capable computing and data facilities are needed to support HEP science goals in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of the demand at the 2025 timescale is at least two orders of magnitude — and in some cases greatermore » — than that available currently. The growth rate of data produced by simulations is overwhelming the current ability of both facilities and researchers to store and analyze it. Additional resources and new techniques for data analysis are urgently needed. Data rates and volumes from experimental facilities are also straining the current HEP infrastructure in its ability to store and analyze large and complex data volumes. Appropriately configured leadership-class facilities can play a transformational role in enabling scientific discovery from these datasets. A close integration of high-performance computing (HPC) simulation and data analysis will greatly aid in interpreting the results of HEP experiments. Such an integration will minimize data movement and facilitate interdependent workflows. Long-range planning between HEP and ASCR will be required to meet HEP’s research needs. To best use ASCR HPC resources, the experimental HEP program needs (1) an established, long-term plan for access to ASCR computational and data resources, (2) the ability to map workflows to HPC resources, (3) the ability for ASCR facilities to accommodate workflows run by collaborations potentially comprising thousands of individual members, (4) to transition codes to the next-generation HPC platforms that will be available at ASCR facilities, (5) to build up and train a workforce capable of developing and using simulations and analysis to support HEP scientific research on next-generation systems.« less

Optimized Materials From First Principles Simulations: Are We There Yet?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Galli, G; Gygi, F

2005-07-26

In the past thirty years, the use of scientific computing has become pervasive in all disciplines: collection and interpretation of most experimental data is carried out using computers, and physical models in computable form, with various degrees of complexity and sophistication, are utilized in all fields of science. However, full prediction of physical and chemical phenomena based on the basic laws of Nature, using computer simulations, is a revolution still in the making, and it involves some formidable theoretical and computational challenges. We illustrate the progress and successes obtained in recent years in predicting fundamental properties of materials in condensedmore » phases and at the nanoscale, using ab-initio, quantum simulations. We also discuss open issues related to the validation of the approximate, first principles theories used in large scale simulations, and the resulting complex interplay between computation and experiment. Finally, we describe some applications, with focus on nanostructures and liquids, both at ambient and under extreme conditions.« less

Challenges in Managing Trustworthy Large-scale Digital Science

NASA Astrophysics Data System (ADS)

Evans, B. J. K.

2017-12-01

The increased use of large-scale international digital science has opened a number of challenges for managing, handling, using and preserving scientific information. The large volumes of information are driven by three main categories - model outputs including coupled models and ensembles, data products that have been processing to a level of usability, and increasingly heuristically driven data analysis. These data products are increasingly the ones that are usable by the broad communities, and far in excess of the raw instruments data outputs. The data, software and workflows are then shared and replicated to allow broad use at an international scale, which places further demands of infrastructure to support how the information is managed reliably across distributed resources. Users necessarily rely on these underlying "black boxes" so that they are productive to produce new scientific outcomes. The software for these systems depend on computational infrastructure, software interconnected systems, and information capture systems. This ranges from the fundamentals of the reliability of the compute hardware, system software stacks and libraries, and the model software. Due to these complexities and capacity of the infrastructure, there is an increased emphasis of transparency of the approach and robustness of the methods over the full reproducibility. Furthermore, with large volume data management, it is increasingly difficult to store the historical versions of all model and derived data. Instead, the emphasis is on the ability to access the updated products and the reliability by which both previous outcomes are still relevant and can be updated for the new information. We will discuss these challenges and some of the approaches underway that are being used to address these issues.

Young adolescents' engagement in dietary behaviour - the impact of gender, socio-economic status, self-efficacy and scientific literacy. Methodological aspects of constructing measures in nutrition literacy research using the Rasch model.

PubMed

Guttersrud, Øystein; Petterson, Kjell Sverre

2015-10-01

The present study validates a revised scale measuring individuals' level of the 'engagement in dietary behaviour' aspect of 'critical nutrition literacy' and describes how background factors affect this aspect of Norwegian tenth-grade students' nutrition literacy. Data were gathered electronically during a field trial of a standardised sample test in science. Test items and questionnaire constructs were distributed evenly across four electronic field-test booklets. Data management and analysis were performed using the RUMM2030 item analysis package and the IBM SPSS Statistics 20 statistical software package. Students responded on computers at school. Seven hundred and forty tenth-grade students at twenty-seven randomly sampled public schools were enrolled in the field-test study. The engagement in dietary behaviour scale and the self-efficacy in science scale were distributed to 178 of these students. The dietary behaviour scale and the self-efficacy in science scale came out as valid, reliable and well-targeted instruments usable for the construction of measurements. Girls and students with high self-efficacy reported higher engagement in dietary behaviour than other students. Socio-economic status and scientific literacy - measured as ability in science by applying an achievement test - did not correlate significantly different from zero with students' engagement in dietary behaviour.

Developing science gateways for drug discovery in a grid environment.

PubMed

Pérez-Sánchez, Horacio; Rezaei, Vahid; Mezhuyev, Vitaliy; Man, Duhu; Peña-García, Jorge; den-Haan, Helena; Gesing, Sandra

2016-01-01

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.

An Application-Based Performance Characterization of the Columbia Supercluster

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Djomehri, Jahed M.; Hood, Robert; Jin, Hoaqiang; Kiris, Cetin; Saini, Subhash

2005-01-01

Columbia is a 10,240-processor supercluster consisting of 20 Altix nodes with 512 processors each, and currently ranked as the second-fastest computer in the world. In this paper, we present the performance characteristics of Columbia obtained on up to four computing nodes interconnected via the InfiniBand and/or NUMAlink4 communication fabrics. We evaluate floating-point performance, memory bandwidth, message passing communication speeds, and compilers using a subset of the HPC Challenge benchmarks, and some of the NAS Parallel Benchmarks including the multi-zone versions. We present detailed performance results for three scientific applications of interest to NASA, one from molecular dynamics, and two from computational fluid dynamics. Our results show that both the NUMAlink4 and the InfiniBand hold promise for application scaling to a large number of processors.

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

ERIC Educational Resources Information Center

Pallant, Amy; Lee, Hee-Sun

2015-01-01

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…

Computational Cosmology at the Bleeding Edge

NASA Astrophysics Data System (ADS)

Habib, Salman

2013-04-01

Large-area sky surveys are providing a wealth of cosmological information to address the mysteries of dark energy and dark matter. Observational probes based on tracking the formation of cosmic structure are essential to this effort, and rely crucially on N-body simulations that solve the Vlasov-Poisson equation in an expanding Universe. As statistical errors from survey observations continue to shrink, and cosmological probes increase in number and complexity, simulations are entering a new regime in their use as tools for scientific inference. Changes in supercomputer architectures provide another rationale for developing new parallel simulation and analysis capabilities that can scale to computational concurrency levels measured in the millions to billions. In this talk I will outline the motivations behind the development of the HACC (Hardware/Hybrid Accelerated Cosmology Code) extreme-scale cosmological simulation framework and describe its essential features. By exploiting a novel algorithmic structure that allows flexible tuning across diverse computer architectures, including accelerated and many-core systems, HACC has attained a performance of 14 PFlops on the IBM BG/Q Sequoia system at 69% of peak, using more than 1.5 million cores.

Understanding I/O workload characteristics of a Peta-scale storage system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Youngjae; Gunasekaran, Raghul

2015-01-01

Understanding workload characteristics is critical for optimizing and improving the performance of current systems and software, and architecting new storage systems based on observed workload patterns. In this paper, we characterize the I/O workloads of scientific applications of one of the world s fastest high performance computing (HPC) storage cluster, Spider, at the Oak Ridge Leadership Computing Facility (OLCF). OLCF flagship petascale simulation platform, Titan, and other large HPC clusters, in total over 250 thousands compute cores, depend on Spider for their I/O needs. We characterize the system utilization, the demands of reads and writes, idle time, storage space utilization,more » and the distribution of read requests to write requests for the Peta-scale Storage Systems. From this study, we develop synthesized workloads, and we show that the read and write I/O bandwidth usage as well as the inter-arrival time of requests can be modeled as a Pareto distribution. We also study the I/O load imbalance problems using I/O performance data collected from the Spider storage system.« less

AIMES Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katz, Daniel S; Jha, Shantenu; Weissman, Jon

2017-01-31

This is the final technical report for the AIMES project. Many important advances in science and engineering are due to large-scale distributed computing. Notwithstanding this reliance, we are still learning how to design and deploy large-scale production Distributed Computing Infrastructures (DCI). This is evidenced by missing design principles for DCI, and an absence of generally acceptable and usable distributed computing abstractions. The AIMES project was conceived against this backdrop, following on the heels of a comprehensive survey of scientific distributed applications. AIMES laid the foundations to address the tripartite challenge of dynamic resource management, integrating information, and portable and interoperablemore » distributed applications. Four abstractions were defined and implemented: skeleton, resource bundle, pilot, and execution strategy. The four abstractions were implemented into software modules and then aggregated into the AIMES middleware. This middleware successfully integrates information across the application layer (skeletons) and resource layer (Bundles), derives a suitable execution strategy for the given skeleton and enacts its execution by means of pilots on one or more resources, depending on the application requirements, and resource availabilities and capabilities.« less

AIMES Final Technical Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weissman, Jon; Katz, Dan; Jha, Shantenu

2017-01-31

This is the final technical report for the AIMES project. Many important advances in science and engineering are due to large scale distributed computing. Notwithstanding this reliance, we are still learning how to design and deploy large-scale production Distributed Computing Infrastructures (DCI). This is evidenced by missing design principles for DCI, and an absence of generally acceptable and usable distributed computing abstractions. The AIMES project was conceived against this backdrop, following on the heels of a comprehensive survey of scientific distributed applications. AIMES laid the foundations to address the tripartite challenge of dynamic resource management, integrating information, and portable andmore » interoperable distributed applications. Four abstractions were defined and implemented: skeleton, resource bundle, pilot, and execution strategy. The four abstractions were implemented into software modules and then aggregated into the AIMES middleware. This middleware successfully integrates information across the application layer (skeletons) and resource layer (Bundles), derives a suitable execution strategy for the given skeleton and enacts its execution by means of pilots on one or more resources, depending on the application requirements, and resource availabilities and capabilities.« less

Large-Scale NASA Science Applications on the Columbia Supercluster

NASA Technical Reports Server (NTRS)

Brooks, Walter

2005-01-01

Columbia, NASA's newest 61 teraflops supercomputer that became operational late last year, is a highly integrated Altix cluster of 10,240 processors, and was named to honor the crew of the Space Shuttle lost in early 2003. Constructed in just four months, Columbia increased NASA's computing capability ten-fold, and revitalized the Agency's high-end computing efforts. Significant cutting-edge science and engineering simulations in the areas of space and Earth sciences, as well as aeronautics and space operations, are already occurring on this largest operational Linux supercomputer, demonstrating its capacity and capability to accelerate NASA's space exploration vision. The presentation will describe how an integrated environment consisting not only of next-generation systems, but also modeling and simulation, high-speed networking, parallel performance optimization, and advanced data analysis and visualization, is being used to reduce design cycle time, accelerate scientific discovery, conduct parametric analysis of multiple scenarios, and enhance safety during the life cycle of NASA missions. The talk will conclude by discussing how NAS partnered with various NASA centers, other government agencies, computer industry, and academia, to create a national resource in large-scale modeling and simulation.

Parallel processing for scientific computations

NASA Technical Reports Server (NTRS)

Alkhatib, Hasan S.

1995-01-01

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.

Conceiving processes in atmospheric models-General equations, subscale parameterizations, and 'superparameterizations'

NASA Astrophysics Data System (ADS)

Gramelsberger, Gabriele

The scientific understanding of atmospheric processes has been rooted in the mechanical and physical view of nature ever since dynamic meteorology gained ground in the late 19th century. Conceiving the atmosphere as a giant 'air mass circulation engine' entails applying hydro- and thermodynamical theory to the subject in order to describe the atmosphere's behaviour on small scales. But when it comes to forecasting, it turns out that this view is far too complex to be computed. The limitation of analytical methods precludes an exact solution, forcing scientists to make use of numerical simulation. However, simulation introduces two prerequisites to meteorology: First, the partitioning of the theoretical view into two parts-the large-scale behaviour of the atmosphere, and the effects of smaller-scale processes on this large-scale behaviour, so-called parametrizations; and second, the dependency on computational power in order to achieve a higher resolution. The history of today's atmospheric circulation modelling can be reconstructed as the attempt to improve the handling of these basic constraints. It can be further seen as the old schism between theory and application under new circumstances, which triggers a new discussion about the question of how processes may be conceived in atmospheric modelling.

Enabling Extreme Scale Earth Science Applications at the Oak Ridge Leadership Computing Facility

NASA Astrophysics Data System (ADS)

Anantharaj, V. G.; Mozdzynski, G.; Hamrud, M.; Deconinck, W.; Smith, L.; Hack, J.

2014-12-01

The Oak Ridge Leadership Facility (OLCF), established at the Oak Ridge National Laboratory (ORNL) under the auspices of the U.S. Department of Energy (DOE), welcomes investigators from universities, government agencies, national laboratories and industry who are prepared to perform breakthrough research across a broad domain of scientific disciplines, including earth and space sciences. Titan, the OLCF flagship system, is currently listed as #2 in the Top500 list of supercomputers in the world, and the largest available for open science. The computational resources are allocated primarily via the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program, sponsored by the U.S. DOE Office of Science. In 2014, over 2.25 billion core hours on Titan were awarded via INCITE projects., including 14% of the allocation toward earth sciences. The INCITE competition is also open to research scientists based outside the USA. In fact, international research projects account for 12% of the INCITE awards in 2014. The INCITE scientific review panel also includes 20% participation from international experts. Recent accomplishments in earth sciences at OLCF include the world's first continuous simulation of 21,000 years of earth's climate history (2009); and an unprecedented simulation of a magnitude 8 earthquake over 125 sq. miles. One of the ongoing international projects involves scaling the ECMWF Integrated Forecasting System (IFS) model to over 200K cores of Titan. ECMWF is a partner in the EU funded Collaborative Research into Exascale Systemware, Tools and Applications (CRESTA) project. The significance of the research carried out within this project is the demonstration of techniques required to scale current generation Petascale capable simulation codes towards the performance levels required for running on future Exascale systems. One of the techniques pursued by ECMWF is to use Fortran2008 coarrays to overlap computations and communications and to reduce the total volume of data communicated. Use of Titan has enabled ECMWF to plan future scalability developments and resource requirements. We will also discuss the best practices developed over the years in navigating logistical, legal and regulatory hurdles involved in supporting the facility's diverse user community.

Unified Performance and Power Modeling of Scientific Workloads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Shuaiwen; Barker, Kevin J.; Kerbyson, Darren J.

2013-11-17

It is expected that scientific applications executing on future large-scale HPC must be optimized not only in terms of performance, but also in terms of power consumption. As power and energy become increasingly constrained resources, researchers and developers must have access to tools that will allow for accurate prediction of both performance and power consumption. Reasoning about performance and power consumption in concert will be critical for achieving maximum utilization of limited resources on future HPC systems. To this end, we present a unified performance and power model for the Nek-Bone mini-application developed as part of the DOE's CESAR Exascalemore » Co-Design Center. Our models consider the impact of computation, point-to-point communication, and collective communication« less

The European perspective for LSST

NASA Astrophysics Data System (ADS)

Gangler, Emmanuel

2017-06-01

LSST is a next generation telescope that will produce an unprecedented data flow. The project goal is to deliver data products such as images and catalogs thus enabling scientific analysis for a wide community of users. As a large scale survey, LSST data will be complementary with other facilities in a wide range of scientific domains, including data from ESA or ESO. European countries have invested in LSST since 2007, in the construction of the camera as well as in the computing effort. This latter will be instrumental in designing the next step: how to distribute LSST data to Europe. Astroinformatics challenges for LSST indeed includes not only the analysis of LSST big data, but also the practical efficiency of the data access.

Introducing Enabling Computational Tools to the Climate Sciences: Multi-Resolution Climate Modeling with Adaptive Cubed-Sphere Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jablonowski, Christiane

The research investigates and advances strategies how to bridge the scale discrepancies between local, regional and global phenomena in climate models without the prohibitive computational costs of global cloud-resolving simulations. In particular, the research explores new frontiers in computational geoscience by introducing high-order Adaptive Mesh Refinement (AMR) techniques into climate research. AMR and statically-adapted variable-resolution approaches represent an emerging trend for atmospheric models and are likely to become the new norm in future-generation weather and climate models. The research advances the understanding of multi-scale interactions in the climate system and showcases a pathway how to model these interactions effectively withmore » advanced computational tools, like the Chombo AMR library developed at the Lawrence Berkeley National Laboratory. The research is interdisciplinary and combines applied mathematics, scientific computing and the atmospheric sciences. In this research project, a hierarchy of high-order atmospheric models on cubed-sphere computational grids have been developed that serve as an algorithmic prototype for the finite-volume solution-adaptive Chombo-AMR approach. The foci of the investigations have lied on the characteristics of both static mesh adaptations and dynamically-adaptive grids that can capture flow fields of interest like tropical cyclones. Six research themes have been chosen. These are (1) the introduction of adaptive mesh refinement techniques into the climate sciences, (2) advanced algorithms for nonhydrostatic atmospheric dynamical cores, (3) an assessment of the interplay between resolved-scale dynamical motions and subgrid-scale physical parameterizations, (4) evaluation techniques for atmospheric model hierarchies, (5) the comparison of AMR refinement strategies and (6) tropical cyclone studies with a focus on multi-scale interactions and variable-resolution modeling. The results of this research project demonstrate significant advances in all six research areas. The major conclusions are that statically-adaptive variable-resolution modeling is currently becoming mature in the climate sciences, and that AMR holds outstanding promise for future-generation weather and climate models on high-performance computing architectures.« less

History of the numerical aerodynamic simulation program

NASA Technical Reports Server (NTRS)

Peterson, Victor L.; Ballhaus, William F., Jr.

1987-01-01

The Numerical Aerodynamic Simulation (NAS) program has reached a milestone with the completion of the initial operating configuration of the NAS Processing System Network. This achievement is the first major milestone in the continuing effort to provide a state-of-the-art supercomputer facility for the national aerospace community and to serve as a pathfinder for the development and use of future supercomputer systems. The underlying factors that motivated the initiation of the program are first identified and then discussed. These include the emergence and evolution of computational aerodynamics as a powerful new capability in aerodynamics research and development, the computer power required for advances in the discipline, the complementary nature of computation and wind tunnel testing, and the need for the government to play a pathfinding role in the development and use of large-scale scientific computing systems. Finally, the history of the NAS program is traced from its inception in 1975 to the present time.

Job Scheduling in a Heterogeneous Grid Environment

NASA Technical Reports Server (NTRS)

Shan, Hong-Zhang; Smith, Warren; Oliker, Leonid; Biswas, Rupak

2004-01-01

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.

Nuclear Science Symposium, 31st and Symposium on Nuclear Power Systems, 16th, Orlando, FL, October 31-November 2, 1984, Proceedings

NASA Technical Reports Server (NTRS)

Biggerstaff, J. A. (Editor)

1985-01-01

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.

A GPU accelerated and error-controlled solver for the unbounded Poisson equation in three dimensions

NASA Astrophysics Data System (ADS)

Exl, Lukas

2017-12-01

An efficient solver for the three dimensional free-space Poisson equation is presented. The underlying numerical method is based on finite Fourier series approximation. While the error of all involved approximations can be fully controlled, the overall computation error is driven by the convergence of the finite Fourier series of the density. For smooth and fast-decaying densities the proposed method will be spectrally accurate. The method scales with O(N log N) operations, where N is the total number of discretization points in the Cartesian grid. The majority of the computational costs come from fast Fourier transforms (FFT), which makes it ideal for GPU computation. Several numerical computations on CPU and GPU validate the method and show efficiency and convergence behavior. Tests are performed using the Vienna Scientific Cluster 3 (VSC3). A free MATLAB implementation for CPU and GPU is provided to the interested community.

The NCI High Performance Computing (HPC) and High Performance Data (HPD) Platform to Support the Analysis of Petascale Environmental Data Collections

NASA Astrophysics Data System (ADS)

Evans, B. J. K.; Pugh, T.; Wyborn, L. A.; Porter, D.; Allen, C.; Smillie, J.; Antony, J.; Trenham, C.; Evans, B. J.; Beckett, D.; Erwin, T.; King, E.; Hodge, J.; Woodcock, R.; Fraser, R.; Lescinsky, D. T.

2014-12-01

The National Computational Infrastructure (NCI) has co-located a priority set of national data assets within a HPC research platform. This powerful in-situ computational platform has been created to help serve and analyse the massive amounts of data across the spectrum of environmental collections - in particular the climate, observational data and geoscientific domains. This paper examines the infrastructure, innovation and opportunity for this significant research platform. NCI currently manages nationally significant data collections (10+ PB) categorised as 1) earth system sciences, climate and weather model data assets and products, 2) earth and marine observations and products, 3) geosciences, 4) terrestrial ecosystem, 5) water management and hydrology, and 6) astronomy, social science and biosciences. The data is largely sourced from the NCI partners (who include the custodians of many of the national scientific records), major research communities, and collaborating overseas organisations. By co-locating these large valuable data assets, new opportunities have arisen by harmonising the data collections, making a powerful transdisciplinary research platformThe data is accessible within an integrated HPC-HPD environment - a 1.2 PFlop supercomputer (Raijin), a HPC class 3000 core OpenStack cloud system and several highly connected large scale and high-bandwidth Lustre filesystems. New scientific software, cloud-scale techniques, server-side visualisation and data services have been harnessed and integrated into the platform, so that analysis is performed seamlessly across the traditional boundaries of the underlying data domains. Characterisation of the techniques along with performance profiling ensures scalability of each software component, all of which can either be enhanced or replaced through future improvements. A Development-to-Operations (DevOps) framework has also been implemented to manage the scale of the software complexity alone. This ensures that software is both upgradable and maintainable, and can be readily reused with complexly integrated systems and become part of the growing global trusted community tools for cross-disciplinary research.

CFD and ventilation research.

PubMed

Li, Y; Nielsen, P V

2011-12-01

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.

To simulate or not to simulate: what are the questions?

PubMed

Dudai, Yadin; Evers, Kathinka

2014-10-22

Simulation is a powerful method in science and engineering. However, simulation is an umbrella term, and its meaning and goals differ among disciplines. Rapid advances in neuroscience and computing draw increasing attention to large-scale brain simulations. What is the meaning of simulation, and what should the method expect to achieve? We discuss the concept of simulation from an integrated scientific and philosophical vantage point and pinpoint selected issues that are specific to brain simulation.

Shake, Rattle and Roles: Lessons from Experimental Earthquake Engineering for Incorporating Remote Users in Large-Scale E-Science Experiments

DTIC Science & Technology

2007-01-01

Mechanical Turk: Artificial Artificial Intelligence . Retrieved May 15, 2006 from http://www.mturk.com/ mturk/welcome Atkins, D. E., Droegemeier, K. K...Turk (Amazon, 2006) site goes beyond volunteers and pays people to do Human Intelligence Tasks, those that are difficult for computers but relatively...geographically distributed scientific collaboration, and the use of videogame technology for training. Address: U.S. Army Research Institute, 2511 Jefferson

HEC Applications on Columbia Project

NASA Technical Reports Server (NTRS)

Taft, Jim

2004-01-01

NASA's Columbia system consists of a cluster of twenty 512 processor SGI Altix systems. Each of these systems is 3 TFLOP/s in peak performance - approximately the same as the entire compute capability at NAS just one year ago. Each 512p system is a single system image machine with one Linunx O5, one high performance file system, and one globally shared memory. The NAS Terascale Applications Group (TAG) is chartered to assist in scaling NASA's mission critical codes to at least 512p in order to significantly improve emergency response during flight operations, as well as provide significant improvements in the codes. and rate of scientific discovery across the scientifc disciplines within NASA's Missions. Recent accomplishments are 4x improvements to codes in the ocean modeling community, 10x performance improvements in a number of computational fluid dynamics codes used in aero-vehicle design, and 5x improvements in a number of space science codes dealing in extreme physics. The TAG group will continue its scaling work to 2048p and beyond (10240 cpus) as the Columbia system becomes fully operational and the upgrades to the SGI NUMAlink memory fabric are in place. The NUMlink uprades dramatically improve system scalability for a single application. These upgrades will allow a number of codes to execute faster at higher fidelity than ever before on any other system, thus increasing the rate of scientific discovery even further

The Australian Computational Earth Systems Simulator

NASA Astrophysics Data System (ADS)

Mora, P.; Muhlhaus, H.; Lister, G.; Dyskin, A.; Place, D.; Appelbe, B.; Nimmervoll, N.; Abramson, D.

2001-12-01

Numerical simulation of the physics and dynamics of the entire earth system offers an outstanding opportunity for advancing earth system science and technology but represents a major challenge due to the range of scales and physical processes involved, as well as the magnitude of the software engineering effort required. However, new simulation and computer technologies are bringing this objective within reach. Under a special competitive national funding scheme to establish new Major National Research Facilities (MNRF), the Australian government together with a consortium of Universities and research institutions have funded construction of the Australian Computational Earth Systems Simulator (ACcESS). The Simulator or computational virtual earth will provide the research infrastructure to the Australian earth systems science community required for simulations of dynamical earth processes at scales ranging from microscopic to global. It will consist of thematic supercomputer infrastructure and an earth systems simulation software system. The Simulator models and software will be constructed over a five year period by a multi-disciplinary team of computational scientists, mathematicians, earth scientists, civil engineers and software engineers. The construction team will integrate numerical simulation models (3D discrete elements/lattice solid model, particle-in-cell large deformation finite-element method, stress reconstruction models, multi-scale continuum models etc) with geophysical, geological and tectonic models, through advanced software engineering and visualization technologies. When fully constructed, the Simulator aims to provide the software and hardware infrastructure needed to model solid earth phenomena including global scale dynamics and mineralisation processes, crustal scale processes including plate tectonics, mountain building, interacting fault system dynamics, and micro-scale processes that control the geological, physical and dynamic behaviour of earth systems. ACcESS represents a part of Australia's contribution to the APEC Cooperation for Earthquake Simulation (ACES) international initiative. Together with other national earth systems science initiatives including the Japanese Earth Simulator and US General Earthquake Model projects, ACcESS aims to provide a driver for scientific advancement and technological breakthroughs including: quantum leaps in understanding of earth evolution at global, crustal, regional and microscopic scales; new knowledge of the physics of crustal fault systems required to underpin the grand challenge of earthquake prediction; new understanding and predictive capabilities of geological processes such as tectonics and mineralisation.

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Koo, Michelle; Cao, Yu

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe-more » art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster. Our study processed many terabytes of system logs and application performance measurements collected on the HPC systems at NERSC. The implementation of our tool is generic enough to be used for analyzing the performance of other HPC systems and Big Data workows.« less

Introduction to the LaRC central scientific computing complex

NASA Technical Reports Server (NTRS)

Shoosmith, John N.

1993-01-01

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.

OMPC: an Open-Source MATLAB®-to-Python Compiler

PubMed Central

Jurica, Peter; van Leeuwen, Cees

2008-01-01

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

POSEIDON: An integrated system for analysis and forecast of hydrological, meteorological and surface marine fields in the Mediterranean area

NASA Astrophysics Data System (ADS)

Speranza, A.; Accadia, C.; Casaioli, M.; Mariani, S.; Monacelli, G.; Inghilesi, R.; Tartaglione, N.; Ruti, P. M.; Carillo, A.; Bargagli, A.; Pisacane, G.; Valentinotti, F.; Lavagnini, A.

2004-07-01

The Mediterranean area is characterized by relevant hydrological, meteorological and marine processes developing at horizontal space-scales of the order of 1-100 km. In the recent past, several international programs have been addressed (ALPEX, POEM, MAP, etc.) to "resolving" the dynamics of such motions. Other projects (INTERREG-Flooding, MEDEX, etc.) are at present being developed with special emphasis on catastrophic events with major impact on human society that are, quite often, characterized in their manifestation by processes with the above-mentioned scales of motion. In the dynamical evolution of such events, however, equally important is the dynamics of interaction of the local (and sometimes very damaging) processes with others developing at larger scales of motion. In fact, some of the most catastrophic events in the history of Mediterranean countries are associated with dynamical processes covering all the range of space-time scales from planetary to local. The Prevision Operational System for the mEditerranean basIn and the Defence of the lagOon of veNice (POSEIDON) is an integrated system for the analysis and forecast of hydrological, meteorological, oceanic fields specifically designed and set up in order to bridge the gap between global and local scales of motion, by modeling explicitly the above referred to dynamical processes in the range of scales from Mediterranean to local. The core of POSEIDON consists of a "cascade" of numerical models that, starting from global scale numerical analysis-forecast, goes all the way to very local phenomena, like tidal propagation in Venice Lagoon. The large computational load imposed by such operational design requires necessarily parallel computing technology: the first model in the cascade is a parallelised version of BOlogna Limited Area Model (BOLAM) running on a Quadrics 128 processors computer (also known as QBOLAM). POSEIDON, developed in the context of a co-operation between the Italian Agency for New technologies, Energy and Environment (Ente per le Nuove tecnologie, l'Energia e l'Ambiente, ENEA) and the Italian Agency for Environmental Protection and Technical Services (Agenzia per la Protezione dell'Ambiente e per i Servizi Tecnici, APAT), has become operational in 2000 and we are presently in the condition of drawing some preliminary conclusions about its performance. In the paper we describe the scientific concepts that were at the basis of the original planning, the structure of the system, its operational cycle and some preliminary scientific and technical evaluations after two years of experimentation.

Scientific Computing Paradigm

NASA Technical Reports Server (NTRS)

VanZandt, John

1994-01-01

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.

Commentary: Considerations in Pedagogy and Assessment in the Use of Computers to Promote Learning about Scientific Models

ERIC Educational Resources Information Center

Adams, Stephen T.

2004-01-01

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…

High-End Scientific Computing

EPA Pesticide Factsheets

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.

A Modular Environment for Geophysical Inversion and Run-time Autotuning using Heterogeneous Computing Systems

NASA Astrophysics Data System (ADS)

Myre, Joseph M.

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.

[Modeling continuous scaling of NDVI based on fractal theory].

PubMed

Luan, Hai-Jun; Tian, Qing-Jiu; Yu, Tao; Hu, Xin-Li; Huang, Yan; Du, Ling-Tong; Zhao, Li-Min; Wei, Xi; Han, Jie; Zhang, Zhou-Wei; Li, Shao-Peng

2013-07-01

Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals' relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters' variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI (computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.

Simulating and mapping spatial complexity using multi-scale techniques

USGS Publications Warehouse

De Cola, L.

1994-01-01

A central problem in spatial analysis is the mapping of data for complex spatial fields using relatively simple data structures, such as those of a conventional GIS. This complexity can be measured using such indices as multi-scale variance, which reflects spatial autocorrelation, and multi-fractal dimension, which characterizes the values of fields. These indices are computed for three spatial processes: Gaussian noise, a simple mathematical function, and data for a random walk. Fractal analysis is then used to produce a vegetation map of the central region of California based on a satellite image. This analysis suggests that real world data lie on a continuum between the simple and the random, and that a major GIS challenge is the scientific representation and understanding of rapidly changing multi-scale fields. -Author

Load Balancing Unstructured Adaptive Grids for CFD Problems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid

1996-01-01

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.

An Expert Assistant for Computer Aided Parallelization

NASA Technical Reports Server (NTRS)

Jost, Gabriele; Chun, Robert; Jin, Haoqiang; Labarta, Jesus; Gimenez, Judit

2004-01-01

The prototype implementation of an expert system was developed to assist the user in the computer aided parallelization process. The system interfaces to tools for automatic parallelization and performance analysis. By fusing static program structure information and dynamic performance analysis data the expert system can help the user to filter, correlate, and interpret the data gathered by the existing tools. Sections of the code that show poor performance and require further attention are rapidly identified and suggestions for improvements are presented to the user. In this paper we describe the components of the expert system and discuss its interface to the existing tools. We present a case study to demonstrate the successful use in full scale scientific applications.

Moose: An Open-Source Framework to Enable Rapid Development of Collaborative, Multi-Scale, Multi-Physics Simulation Tools

NASA Astrophysics Data System (ADS)

Slaughter, A. E.; Permann, C.; Peterson, J. W.; Gaston, D.; Andrs, D.; Miller, J.

2014-12-01

The Idaho National Laboratory (INL)-developed Multiphysics Object Oriented Simulation Environment (MOOSE; www.mooseframework.org), is an open-source, parallel computational framework for enabling the solution of complex, fully implicit multiphysics systems. MOOSE provides a set of computational tools that scientists and engineers can use to create sophisticated multiphysics simulations. Applications built using MOOSE have computed solutions for chemical reaction and transport equations, computational fluid dynamics, solid mechanics, heat conduction, mesoscale materials modeling, geomechanics, and others. To facilitate the coupling of diverse and highly-coupled physical systems, MOOSE employs the Jacobian-free Newton-Krylov (JFNK) method when solving the coupled nonlinear systems of equations arising in multiphysics applications. The MOOSE framework is written in C++, and leverages other high-quality, open-source scientific software packages such as LibMesh, Hypre, and PETSc. MOOSE uses a "hybrid parallel" model which combines both shared memory (thread-based) and distributed memory (MPI-based) parallelism to ensure efficient resource utilization on a wide range of computational hardware. MOOSE-based applications are inherently modular, which allows for simulation expansion (via coupling of additional physics modules) and the creation of multi-scale simulations. Any application developed with MOOSE supports running (in parallel) any other MOOSE-based application. Each application can be developed independently, yet easily communicate with other applications (e.g., conductivity in a slope-scale model could be a constant input, or a complete phase-field micro-structure simulation) without additional code being written. This method of development has proven effective at INL and expedites the development of sophisticated, sustainable, and collaborative simulation tools.

Evaluating non-relational storage technology for HEP metadata and meta-data catalog

NASA Astrophysics Data System (ADS)

Grigorieva, M. A.; Golosova, M. V.; Gubin, M. Y.; Klimentov, A. A.; Osipova, V. V.; Ryabinkin, E. A.

2016-10-01

Large-scale scientific experiments produce vast volumes of data. These data are stored, processed and analyzed in a distributed computing environment. The life cycle of experiment is managed by specialized software like Distributed Data Management and Workload Management Systems. In order to be interpreted and mined, experimental data must be accompanied by auxiliary metadata, which are recorded at each data processing step. Metadata describes scientific data and represent scientific objects or results of scientific experiments, allowing them to be shared by various applications, to be recorded in databases or published via Web. Processing and analysis of constantly growing volume of auxiliary metadata is a challenging task, not simpler than the management and processing of experimental data itself. Furthermore, metadata sources are often loosely coupled and potentially may lead to an end-user inconsistency in combined information queries. To aggregate and synthesize a range of primary metadata sources, and enhance them with flexible schema-less addition of aggregated data, we are developing the Data Knowledge Base architecture serving as the intelligence behind GUIs and APIs.

Defining Computational Thinking for Mathematics and Science Classrooms

ERIC Educational Resources Information Center

Weintrop, David; Beheshti, Elham; Horn, Michael; Orton, Kai; Jona, Kemi; Trouille, Laura; Wilensky, Uri

2016-01-01

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…

Challenges of Big Data Analysis.

PubMed

Fan, Jianqing; Han, Fang; Liu, Han

2014-06-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

On the Relevancy of Efficient, Integrated Computer and Network Monitoring in HEP Distributed Online Environment

NASA Astrophysics Data System (ADS)

Carvalho, D.; Gavillet, Ph.; Delgado, V.; Albert, J. N.; Bellas, N.; Javello, J.; Miere, Y.; Ruffinoni, D.; Smith, G.

Large Scientific Equipments are controlled by Computer Systems whose complexity is growing driven, on the one hand by the volume and variety of the information, its distributed nature, the sophistication of its treatment and, on the other hand by the fast evolution of the computer and network market. Some people call them genetically Large-Scale Distributed Data Intensive Information Systems or Distributed Computer Control Systems (DCCS) for those systems dealing more with real time control. Taking advantage of (or forced by) the distributed architecture, the tasks are more and more often implemented as Client-Server applications. In this framework the monitoring of the computer nodes, the communications network and the applications becomes of primary importance for ensuring the safe running and guaranteed performance of the system. With the future generation of HEP experiments, such as those at the LHC in view, it is proposed to integrate the various functions of DCCS monitoring into one general purpose Multi-layer System.

Challenges of Big Data Analysis

PubMed Central

Fan, Jianqing; Han, Fang; Liu, Han

2014-01-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions. PMID:25419469

Ermittlung von Wortstaemmen in russischen wissenschaftlichen Fachsprachen mit Hilfe des Computers (Establishing Word Stems in Scientific Russian With the Aid of a Computer)

ERIC Educational Resources Information Center

Halbauer, Siegfried

1976-01-01

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)

Scientific Visualization: The Modern Oscilloscope for "Seeing the Unseeable" (LBNL Summer Lecture Series)

ScienceCinema

Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division and Scientific Visualization Group

2018-05-07

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.

A second golden age of aeroacoustics?

PubMed

Lele, Sanjiva K; Nichols, Joseph W

2014-08-13

In 1992, Sir James Lighthill foresaw the dawn of a second golden age in aeroacoustics enabled by computer simulations (Hardin JC, Hussaini MY (eds) 1993 Computational aeroacoustics, New York, NY: Springer (doi:10.1007/978-1-4613-8342-0)). This review traces the progress in large-scale computations to resolve the noise-source processes and the methods devised to predict the far-field radiated sound using this information. Keeping focus on aviation-related noise sources a brief account of the progress in simulations of jet noise, fan noise and airframe noise is given highlighting the key technical issues and challenges. The complex geometry of nozzle elements and airframe components as well as the high Reynolds number of target applications require careful assessment of the discretization algorithms on unstructured grids and modelling compromises. High-fidelity simulations with 200-500 million points are not uncommon today and are used to improve scientific understanding of the noise generation process in specific situations. We attempt to discern where the future might take us, especially if exascale computing becomes a reality in 10 years. A pressing question in this context concerns the role of modelling in the coming era. While the sheer scale of the data generated by large-scale simulations will require new methods for data analysis and data visualization, it is our view that suitable theoretical formulations and reduced models will be even more important in future. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Scientific Visualization, Seeing the Unseeable

ScienceCinema

LBNL

2017-12-09

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.

A feasibility study on porting the community land model onto accelerators using OpenACC

DOE PAGES

Wang, Dali; Wu, Wei; Winkler, Frank; ...

2014-01-01

As environmental models (such as Accelerated Climate Model for Energy (ACME), Parallel Reactive Flow and Transport Model (PFLOTRAN), Arctic Terrestrial Simulator (ATS), etc.) became more and more complicated, we are facing enormous challenges regarding to porting those applications onto hybrid computing architecture. OpenACC appears as a very promising technology, therefore, we have conducted a feasibility analysis on porting the Community Land Model (CLM), a terrestrial ecosystem model within the Community Earth System Models (CESM)). Specifically, we used automatic function testing platform to extract a small computing kernel out of CLM, then we apply this kernel into the actually CLM dataflowmore » procedure, and investigate the strategy of data parallelization and the benefit of data movement provided by current implementation of OpenACC. Even it is a non-intensive kernel, on a single 16-core computing node, the performance (based on the actual computation time using one GPU) of OpenACC implementation is 2.3 time faster than that of OpenMP implementation using single OpenMP thread, but it is 2.8 times slower than the performance of OpenMP implementation using 16 threads. On multiple nodes, MPI_OpenACC implementation demonstrated very good scalability on up to 128 GPUs on 128 computing nodes. This study also provides useful information for us to look into the potential benefits of “deep copy” capability and “routine” feature of OpenACC standards. In conclusion, we believe that our experience on the environmental model, CLM, can be beneficial to many other scientific research programs who are interested to porting their large scale scientific code using OpenACC onto high-end computers, empowered by hybrid computing architecture.« less

Towards the Teraflop CFD

NASA Technical Reports Server (NTRS)

Schreiber, Robert; Simon, Horst D.

1992-01-01

We are surveying current projects in the area of parallel supercomputers. The machines considered here will become commercially available in the 1990 - 1992 time frame. All are suitable for exploring the critical issues in applying parallel processors to large scale scientific computations, in particular CFD calculations. This chapter presents an overview of the surveyed machines, and a detailed analysis of the various architectural and technology approaches taken. Particular emphasis is placed on the feasibility of a Teraflops capability following the paths proposed by various developers.

Workshop on Advances in Scientific Computation and Differential Equations 󈨡 (SCADE)

DTIC Science & Technology

1994-07-18

STATEMENT ~~’"j’’ Approved for public release; distribution unlimited. I ABSTRACT (MAMMU 200WOMW 94 808 1 64 4.L SUBIECT TERMS Ii11URE Of PAGES 12 16...called differential algebraic ODEs (DAES). (Some important early research on this topic was by L. Petzold.) Both theoretically and in terms of...completely specify the solution. In many physical systems, especially those in biology, or other large scale slowly responding systems, the inclusion of some

Climate science in the tropics: waves, vortices and PDEs

NASA Astrophysics Data System (ADS)

Khouider, Boualem; Majda, Andrew J.; Stechmann, Samuel N.

2013-01-01

Clouds in the tropics can organize the circulation on planetary scales and profoundly impact long range seasonal forecasting and climate on the entire globe, yet contemporary operational computer models are often deficient in representing these phenomena. On the other hand, contemporary observations reveal remarkably complex coherent waves and vortices in the tropics interacting across a bewildering range of scales from kilometers to ten thousand kilometers. This paper reviews the interdisciplinary contributions over the last decade through the modus operandi of applied mathematics to these important scientific problems. Novel physical phenomena, new multiscale equations, novel PDEs, and numerical algorithms are presented here with the goal of attracting mathematicians and physicists to this exciting research area.

Visual Data Exploration and Analysis - Report on the Visualization Breakout Session of the SCaLeS Workshop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bethel, E. Wes; Frank, Randy; Fulcomer, Sam

Scientific visualization is the transformation of abstract information into images, and it plays an integral role in the scientific process by facilitating insight into observed or simulated phenomena. Visualization as a discipline spans many research areas from computer science, cognitive psychology and even art. Yet the most successful visualization applications are created when close synergistic interactions with domain scientists are part of the algorithmic design and implementation process, leading to visual representations with clear scientific meaning. Visualization is used to explore, to debug, to gain understanding, and as an analysis tool. Visualization is literally everywhere--images are present in this report,more » on television, on the web, in books and magazines--the common theme is the ability to present information visually that is rapidly assimilated by human observers, and transformed into understanding or insight. As an indispensable part a modern science laboratory, visualization is akin to the biologist's microscope or the electrical engineer's oscilloscope. Whereas the microscope is limited to small specimens or use of optics to focus light, the power of scientific visualization is virtually limitless: visualization provides the means to examine data that can be at galactic or atomic scales, or at any size in between. Unlike the traditional scientific tools for visual inspection, visualization offers the means to ''see the unseeable.'' Trends in demographics or changes in levels of atmospheric CO{sub 2} as a function of greenhouse gas emissions are familiar examples of such unseeable phenomena. Over time, visualization techniques evolve in response to scientific need. Each scientific discipline has its ''own language,'' verbal and visual, used for communication. The visual language for depicting electrical circuits is much different than the visual language for depicting theoretical molecules or trends in the stock market. There is no ''one visualization too'' that can serve as a panacea for all science disciplines. Instead, visualization researchers work hand in hand with domain scientists as part of the scientific research process to define, create, adapt and refine software that ''speaks the visual language'' of each scientific domain.« less

Atomic Detail Visualization of Photosynthetic Membranes with GPU-Accelerated Ray Tracing

PubMed Central

Vandivort, Kirby L.; Barragan, Angela; Singharoy, Abhishek; Teo, Ivan; Ribeiro, João V.; Isralewitz, Barry; Liu, Bo; Goh, Boon Chong; Phillips, James C.; MacGregor-Chatwin, Craig; Johnson, Matthew P.; Kourkoutis, Lena F.; Hunter, C. Neil

2016-01-01

The cellular process responsible for providing energy for most life on Earth, namely photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers. PMID:27274603

Lattice QCD Calculations in Nuclear Physics towards the Exascale

NASA Astrophysics Data System (ADS)

Joo, Balint

2017-01-01

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.

Assessment of Molecular Modeling & Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

None

2002-01-03

This report reviews the development and applications of molecular and materials modeling in Europe and Japan in comparison to those in the United States. Topics covered include computational quantum chemistry, molecular simulations by molecular dynamics and Monte Carlo methods, mesoscale modeling of material domains, molecular-structure/macroscale property correlations like QSARs and QSPRs, and related information technologies like informatics and special-purpose molecular-modeling computers. The panel's findings include the following: The United States leads this field in many scientific areas. However, Canada has particular strengths in DFT methods and homogeneous catalysis; Europe in heterogeneous catalysis, mesoscale, and materials modeling; and Japan in materialsmore » modeling and special-purpose computing. Major government-industry initiatives are underway in Europe and Japan, notably in multi-scale materials modeling and in development of chemistry-capable ab-initio molecular dynamics codes.« less

Computationally efficient statistical differential equation modeling using homogenization

USGS Publications Warehouse

Hooten, Mevin B.; Garlick, Martha J.; Powell, James A.

2013-01-01

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

A Computational framework for telemedicine.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foster, I.; von Laszewski, G.; Thiruvathukal, G. K.

1998-07-01

Emerging telemedicine applications require the ability to exploit diverse and geographically distributed resources. Highspeed networks are used to integrate advanced visualization devices, sophisticated instruments, large databases, archival storage devices, PCs, workstations, and supercomputers. This form of telemedical environment is similar to networked virtual supercomputers, also known as metacomputers. Metacomputers are already being used in many scientific application areas. In this article, we analyze requirements necessary for a telemedical computing infrastructure and compare them with requirements found in a typical metacomputing environment. We will show that metacomputing environments can be used to enable a more powerful and unified computational infrastructure formore » telemedicine. The Globus metacomputing toolkit can provide the necessary low level mechanisms to enable a large scale telemedical infrastructure. The Globus toolkit components are designed in a modular fashion and can be extended to support the specific requirements for telemedicine.« less

Consortium for Mathematics in the Geosciences (CMG++): Promoting the application of mathematics, statistics, and computational sciences to the geosciences

NASA Astrophysics Data System (ADS)

Mead, J.; Wright, G. B.

2013-12-01

The collection of massive amounts of high quality data from new and greatly improved observing technologies and from large-scale numerical simulations are drastically improving our understanding and modeling of the earth system. However, these datasets are also revealing important knowledge gaps and limitations of our current conceptual models for explaining key aspects of these new observations. These limitations are impeding progress on questions that have both fundamental scientific and societal significance, including climate and weather, natural disaster mitigation, earthquake and volcano dynamics, earth structure and geodynamics, resource exploration, and planetary evolution. New conceptual approaches and numerical methods for characterizing and simulating these systems are needed - methods that can handle processes which vary through a myriad of scales in heterogeneous, complex environments. Additionally, as certain aspects of these systems may be observable only indirectly or not at all, new statistical methods are also needed. This type of research will demand integrating the expertise of geoscientist together with that of mathematicians, statisticians, and computer scientists. If the past is any indicator, this interdisciplinary research will no doubt lead to advances in all these fields in addition to vital improvements in our ability to predict the behavior of the planetary environment. The Consortium for Mathematics in the Geosciences (CMG++) arose from two scientific workshops held at Northwestern and Princeton in 2011 and 2012 with participants from mathematics, statistics, geoscience and computational science. The mission of CMG++ is to accelerate the traditional interaction between people in these disciplines through the promotion of both collaborative research and interdisciplinary education. We will discuss current activities, describe how people can get involved, and solicit input from the broader AGU community.

Semantic Interoperability for Computational Mineralogy: Experiences of the eMinerals Consortium

NASA Astrophysics Data System (ADS)

Walker, A. M.; White, T. O.; Dove, M. T.; Bruin, R. P.; Couch, P. A.; Tyer, R. P.

2006-12-01

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.

Instruction-Level Characterization of Scientific Computing Applications Using Hardware Performance Counters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Y.; Cameron, K.W.

1998-11-24

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

OMPC: an Open-Source MATLAB-to-Python Compiler.

PubMed

Jurica, Peter; van Leeuwen, Cees

2009-01-01

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.

IEEE International Symposium on Biomedical Imaging.

PubMed

2017-01-01

The IEEE International Symposium on Biomedical Imaging (ISBI) is a scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, across all scales of observation. It fosters knowledge transfer among different imaging communities and contributes to an integrative approach to biomedical imaging. ISBI is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS). The 2018 meeting will include tutorials, and a scientific program composed of plenary talks, invited special sessions, challenges, as well as oral and poster presentations of peer-reviewed papers. High-quality papers are requested containing original contributions to the topics of interest including image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological, and statistical modeling. Accepted 4-page regular papers will be published in the symposium proceedings published by IEEE and included in IEEE Xplore. To encourage attendance by a broader audience of imaging scientists and offer additional presentation opportunities, ISBI 2018 will continue to have a second track featuring posters selected from 1-page abstract submissions without subsequent archival publication.

Deterministic alternatives to the full configuration interaction quantum Monte Carlo method for strongly correlated systems

NASA Astrophysics Data System (ADS)

Tubman, Norm; Whaley, Birgitta

The development of exponential scaling methods has seen great progress in tackling larger systems than previously thought possible. One such technique, full configuration interaction quantum Monte Carlo, allows exact diagonalization through stochastically sampling of determinants. The method derives its utility from the information in the matrix elements of the Hamiltonian, together with a stochastic projected wave function, which are used to explore the important parts of Hilbert space. However, a stochastic representation of the wave function is not required to search Hilbert space efficiently and new deterministic approaches have recently been shown to efficiently find the important parts of determinant space. We shall discuss the technique of Adaptive Sampling Configuration Interaction (ASCI) and the related heat-bath Configuration Interaction approach for ground state and excited state simulations. We will present several applications for strongly correlated Hamiltonians. This work was supported through the Scientific Discovery through Advanced Computing (SciDAC) program funded by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences.

InSAR Scientific Computing Environment

NASA Astrophysics Data System (ADS)

Gurrola, E. M.; Rosen, P. A.; Sacco, G.; Zebker, H. A.; Simons, M.; Sandwell, D. T.

2010-12-01

The InSAR Scientific Computing Environment (ISCE) is a software development effort in its second year within the NASA Advanced Information Systems and Technology program. The ISCE will provide a new computing environment for geodetic image processing for InSAR sensors that will enable scientists to reduce measurements directly from radar satellites and aircraft to new geophysical products without first requiring them to develop detailed expertise in radar processing methods. The environment can serve as the core of a centralized processing center to bring Level-0 raw radar data up to Level-3 data products, but is adaptable to alternative processing approaches for science users interested in new and different ways to exploit mission data. The NRC Decadal Survey-recommended DESDynI mission will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystem. The InSAR Scientific Computing Environment is planned to become a key element in processing DESDynI data into higher level data products and it is expected to enable a new class of analyses that take greater advantage of the long time and large spatial scales of these new data, than current approaches. At the core of ISCE is both legacy processing software from the JPL/Caltech ROI_PAC repeat-pass interferometry package as well as a new InSAR processing package containing more efficient and more accurate processing algorithms being developed at Stanford for this project that is based on experience gained in developing processors for missions such as SRTM and UAVSAR. Around the core InSAR processing programs we are building object-oriented wrappers to enable their incorporation into a more modern, flexible, extensible software package that is informed by modern programming methods, including rigorous componentization of processing codes, abstraction and generalization of data models, and a robust, intuitive user interface with graduated exposure to the levels of sophistication, allowing novices to apply it readily for common tasks and experienced users to mine data with great facility and flexibility. The environment is designed to easily allow user contributions, enabling an open source community to extend the framework into the indefinite future. In this paper we briefly describe both the legacy and the new core processing algorithms and their integration into the new computing environment. We describe the ISCE component and application architecture and the features that permit the desired flexibility, extensibility and ease-of-use. We summarize the state of progress of the environment and the plans for completion of the environment and for its future introduction into the radar processing community.

InSAR Scientific Computing Environment - The Home Stretch

NASA Astrophysics Data System (ADS)

Rosen, P. A.; Gurrola, E. M.; Sacco, G.; Zebker, H. A.

2011-12-01

The Interferometric Synthetic Aperture Radar (InSAR) Scientific Computing Environment (ISCE) is a software development effort in its third and final year within the NASA Advanced Information Systems and Technology program. The ISCE is a new computing environment for geodetic image processing for InSAR sensors enabling scientists to reduce measurements directly from radar satellites to new geophysical products with relative ease. The environment can serve as the core of a centralized processing center to bring Level-0 raw radar data up to Level-3 data products, but is adaptable to alternative processing approaches for science users interested in new and different ways to exploit mission data. Upcoming international SAR missions will deliver data of unprecedented quantity and quality, making possible global-scale studies in climate research, natural hazards, and Earth's ecosystem. The InSAR Scientific Computing Environment has the functionality to become a key element in processing data from NASA's proposed DESDynI mission into higher level data products, supporting a new class of analyses that take advantage of the long time and large spatial scales of these new data. At the core of ISCE is a new set of efficient and accurate InSAR algorithms. These algorithms are placed into an object-oriented, flexible, extensible software package that is informed by modern programming methods, including rigorous componentization of processing codes, abstraction and generalization of data models. The environment is designed to easily allow user contributions, enabling an open source community to extend the framework into the indefinite future. ISCE supports data from nearly all of the available satellite platforms, including ERS, EnviSAT, Radarsat-1, Radarsat-2, ALOS, TerraSAR-X, and Cosmo-SkyMed. The code applies a number of parallelization techniques and sensible approximations for speed. It is configured to work on modern linux-based computers with gcc compilers and python. ISCE is now a complete, functional package, under configuration management, and with extensive documentation and tested use cases appropriate to geodetic imaging applications. The software has been tested with canonical simulated radar data ("point targets") as well as with a variety of existing satellite data, cross-compared with other software packages. Its extensibility has already been proven by the straightforward addition of polarimetric processing and calibration, and derived filtering and estimation routines associated with polarimetry that supplement the original InSAR geodetic functionality. As of October 2011, the software is available for non-commercial use through UNAVCO's WinSAR consortium.

Higher-order ice-sheet modelling accelerated by multigrid on graphics cards

NASA Astrophysics Data System (ADS)

Brædstrup, Christian; Egholm, David

2013-04-01

Higher-order ice flow modelling is a very computer intensive process owing primarily to the nonlinear influence of the horizontal stress coupling. When applied for simulating long-term glacial landscape evolution, the ice-sheet models must consider very long time series, while both high temporal and spatial resolution is needed to resolve small effects. The use of higher-order and full stokes models have therefore seen very limited usage in this field. However, recent advances in graphics card (GPU) technology for high performance computing have proven extremely efficient in accelerating many large-scale scientific computations. The general purpose GPU (GPGPU) technology is cheap, has a low power consumption and fits into a normal desktop computer. It could therefore provide a powerful tool for many glaciologists working on ice flow models. Our current research focuses on utilising the GPU as a tool in ice-sheet and glacier modelling. To this extent we have implemented the Integrated Second-Order Shallow Ice Approximation (iSOSIA) equations on the device using the finite difference method. To accelerate the computations, the GPU solver uses a non-linear Red-Black Gauss-Seidel iterator coupled with a Full Approximation Scheme (FAS) multigrid setup to further aid convergence. The GPU finite difference implementation provides the inherent parallelization that scales from hundreds to several thousands of cores on newer cards. We demonstrate the efficiency of the GPU multigrid solver using benchmark experiments.

Parallel block schemes for large scale least squares computations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Golub, G.H.; Plemmons, R.J.; Sameh, A.

1986-04-01

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

a Novel Discrete Optimal Transport Method for Bayesian Inverse Problems

NASA Astrophysics Data System (ADS)

Bui-Thanh, T.; Myers, A.; Wang, K.; Thiery, A.

2017-12-01

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.

Review of the synergies between computational modeling and experimental characterization of materials across length scales

DOE PAGES

Dingreville, Rémi; Karnesky, Richard A.; Puel, Guillaume; ...

2015-11-16

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends in which predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure–property relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanicsmore » community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to “simply” support experimental work. This is illustrated by examples from several application areas on structural materials. In conclusion this manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.« less

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

[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.

Improving the energy efficiency of sparse linear system solvers on multicore and manycore systems.

PubMed

Anzt, H; Quintana-Ortí, E S

2014-06-28

While most recent breakthroughs in scientific research rely on complex simulations carried out in large-scale supercomputers, the power draft and energy spent for this purpose is increasingly becoming a limiting factor to this trend. In this paper, we provide an overview of the current status in energy-efficient scientific computing by reviewing different technologies used to monitor power draft as well as power- and energy-saving mechanisms available in commodity hardware. For the particular domain of sparse linear algebra, we analyse the energy efficiency of a broad collection of hardware architectures and investigate how algorithmic and implementation modifications can improve the energy performance of sparse linear system solvers, without negatively impacting their performance. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Google Earth Engine

NASA Astrophysics Data System (ADS)

Gorelick, Noel

2013-04-01

The Google Earth Engine platform is a system designed to enable petabyte-scale, scientific analysis and visualization of geospatial datasets. Earth Engine provides a consolidated environment including a massive data catalog co-located with thousands of computers for analysis. The user-friendly front-end provides a workbench environment to allow interactive data and algorithm development and exploration and provides a convenient mechanism for scientists to share data, visualizations and analytic algorithms via URLs. The Earth Engine data catalog contains a wide variety of popular, curated datasets, including the world's largest online collection of Landsat scenes (> 2.0M), numerous MODIS collections, and many vector-based data sets. The platform provides a uniform access mechanism to a variety of data types, independent of their bands, projection, bit-depth, resolution, etc..., facilitating easy multi-sensor analysis. Additionally, a user is able to add and curate their own data and collections. Using a just-in-time, distributed computation model, Earth Engine can rapidly process enormous quantities of geo-spatial data. All computation is performed lazily; nothing is computed until it's required either for output or as input to another step. This model allows real-time feedback and preview during algorithm development, supporting a rapid algorithm development, test, and improvement cycle that scales seamlessly to large-scale production data processing. Through integration with a variety of other services, Earth Engine is able to bring to bear considerable analytic and technical firepower in a transparent fashion, including: AI-based classification via integration with Google's machine learning infrastructure, publishing and distribution at Google scale through integration with the Google Maps API, Maps Engine and Google Earth, and support for in-the-field activities such as validation, ground-truthing, crowd-sourcing and citizen science though the Android Open Data Kit.

Google Earth Engine

NASA Astrophysics Data System (ADS)

Gorelick, N.

2012-12-01

The Google Earth Engine platform is a system designed to enable petabyte-scale, scientific analysis and visualization of geospatial datasets. Earth Engine provides a consolidated environment including a massive data catalog co-located with thousands of computers for analysis. The user-friendly front-end provides a workbench environment to allow interactive data and algorithm development and exploration and provides a convenient mechanism for scientists to share data, visualizations and analytic algorithms via URLs. The Earth Engine data catalog contains a wide variety of popular, curated datasets, including the world's largest online collection of Landsat scenes (> 2.0M), numerous MODIS collections, and many vector-based data sets. The platform provides a uniform access mechanism to a variety of data types, independent of their bands, projection, bit-depth, resolution, etc..., facilitating easy multi-sensor analysis. Additionally, a user is able to add and curate their own data and collections. Using a just-in-time, distributed computation model, Earth Engine can rapidly process enormous quantities of geo-spatial data. All computation is performed lazily; nothing is computed until it's required either for output or as input to another step. This model allows real-time feedback and preview during algorithm development, supporting a rapid algorithm development, test, and improvement cycle that scales seamlessly to large-scale production data processing. Through integration with a variety of other services, Earth Engine is able to bring to bear considerable analytic and technical firepower in a transparent fashion, including: AI-based classification via integration with Google's machine learning infrastructure, publishing and distribution at Google scale through integration with the Google Maps API, Maps Engine and Google Earth, and support for in-the-field activities such as validation, ground-truthing, crowd-sourcing and citizen science though the Android Open Data Kit.

High performance computing aspects of a dimension independent semi-Lagrangian discontinuous Galerkin code

NASA Astrophysics Data System (ADS)

Einkemmer, Lukas

2016-05-01

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.

Computers and Computation. Readings from Scientific American.

ERIC Educational Resources Information Center

Fenichel, Robert R.; Weizenbaum, Joseph

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…

Simultaneous Multi-Scale Diffusion Estimation and Tractography Guided by Entropy Spectrum Pathways

PubMed Central

Galinsky, Vitaly L.; Frank, Lawrence R.

2015-01-01

We have developed a method for the simultaneous estimation of local diffusion and the global fiber tracts based upon the information entropy flow that computes the maximum entropy trajectories between locations and depends upon the global structure of the multi-dimensional and multi-modal diffusion field. Computation of the entropy spectrum pathways requires only solving a simple eigenvector problem for the probability distribution for which efficient numerical routines exist, and a straight forward integration of the probability conservation through ray tracing of the convective modes guided by a global structure of the entropy spectrum coupled with a small scale local diffusion. The intervoxel diffusion is sampled by multi b-shell multi q-angle DWI data expanded in spherical waves. This novel approach to fiber tracking incorporates global information about multiple fiber crossings in every individual voxel and ranks it in the most scientifically rigorous way. This method has potential significance for a wide range of applications, including studies of brain connectivity. PMID:25532167

Enabling scientific workflows in virtual reality

USGS Publications Warehouse

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.

2006-01-01

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.

Interactive Visualization of Large-Scale Hydrological Data using Emerging Technologies in Web Systems and Parallel Programming

NASA Astrophysics Data System (ADS)

Demir, I.; Krajewski, W. F.

2013-12-01

As geoscientists are confronted with increasingly massive datasets from environmental observations to simulations, one of the biggest challenges is having the right tools to gain scientific insight from the data and communicate the understanding to stakeholders. Recent developments in web technologies make it easy to manage, visualize and share large data sets with general public. Novel visualization techniques and dynamic user interfaces allow users to interact with data, and modify the parameters to create custom views of the data to gain insight from simulations and environmental observations. This requires developing new data models and intelligent knowledge discovery techniques to explore and extract information from complex computational simulations or large data repositories. Scientific visualization will be an increasingly important component to build comprehensive environmental information platforms. This presentation provides an overview of the trends and challenges in the field of scientific visualization, and demonstrates information visualization and communication tools developed within the light of these challenges.

The End of the Rainbow? Color Schemes for Improved Data Graphics

NASA Astrophysics Data System (ADS)

Light, Adam; Bartlein, Patrick J.

2004-10-01

Modern computer displays and printers enable the widespread use of color in scientific communication, but the expertise for designing effective graphics has not kept pace with the technology for producing them. Historically, even the most prestigious publications have tolerated high defect rates in figures and illustrations, and technological advances that make creating and reproducing graphics easier do not appear to have decreased the frequency of errors. Flawed graphics consequently beget more flawed graphics as authors emulate published examples. Color has the potential to enhance communication, but design mistakes can result in color figures that are less effective than gray scale displays of the same data. Empirical research on human subjects can build a fundamental understanding of visual perception and scientific methods can be used to evaluate existing designs, but creating effective data graphics is a design task and not fundamentally a scientific pursuit. Like writing well, creating good data graphics requires a combination of formal knowledge and artistic sensibility tempered by experience: a combination of ``substance, statistics, and design''.

Google Earth Engine: a new cloud-computing platform for global-scale earth observation data and analysis

NASA Astrophysics Data System (ADS)

Moore, R. T.; Hansen, M. C.

2011-12-01

Google Earth Engine is a new technology platform that enables monitoring and measurement of changes in the earth's environment, at planetary scale, on a large catalog of earth observation data. The platform offers intrinsically-parallel computational access to thousands of computers in Google's data centers. Initial efforts have focused primarily on global forest monitoring and measurement, in support of REDD+ activities in the developing world. The intent is to put this platform into the hands of scientists and developing world nations, in order to advance the broader operational deployment of existing scientific methods, and strengthen the ability for public institutions and civil society to better understand, manage and report on the state of their natural resources. Earth Engine currently hosts online nearly the complete historical Landsat archive of L5 and L7 data collected over more than twenty-five years. Newly-collected Landsat imagery is downloaded from USGS EROS Center into Earth Engine on a daily basis. Earth Engine also includes a set of historical and current MODIS data products. The platform supports generation, on-demand, of spatial and temporal mosaics, "best-pixel" composites (for example to remove clouds and gaps in satellite imagery), as well as a variety of spectral indices. Supervised learning methods are available over the Landsat data catalog. The platform also includes a new application programming framework, or "API", that allows scientists access to these computational and data resources, to scale their current algorithms or develop new ones. Under the covers of the Google Earth Engine API is an intrinsically-parallel image-processing system. Several forest monitoring applications powered by this API are currently in development and expected to be operational in 2011. Combining science with massive data and technology resources in a cloud-computing framework can offer advantages of computational speed, ease-of-use and collaboration, as well as transparency in data and methods. Methods developed for global processing of MODIS data to map land cover are being adopted for use with Landsat data. Specifically, the MODIS Vegetation Continuous Field product methodology has been applied for mapping forest extent and change at national scales using Landsat time-series data sets. Scaling this method to continental and global scales is enabled by Google Earth Engine computing capabilities. By combining the supervised learning VCF approach with the Landsat archive and cloud computing, unprecedented monitoring of land cover dynamics is enabled.

Bypassing the Kohn-Sham equations with machine learning.

PubMed

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Computational Infrastructure for Geodynamics (CIG)

NASA Astrophysics Data System (ADS)

Gurnis, M.; Kellogg, L. H.; Bloxham, J.; Hager, B. H.; Spiegelman, M.; Willett, S.; Wysession, M. E.; Aivazis, M.

2004-12-01

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.

Development and Validation of Scientific Literacy Scale for College Preparedness in STEM with Freshmen from Diverse Institutions

ERIC Educational Resources Information Center

Benjamin, Thomas E.; Marks, Bryant; Demetrikopoulos, Melissa K.; Rose, Jordan; Pollard, Ethen; Thomas, Alicia; Muldrow, Lycurgus L.

2017-01-01

Although a major goal of Science, Technology, Engineering, and Mathematics (STEM) education is to develop scientific literacy, prior efforts at measuring scientific literacy have not attempted to link scientific literacy with success in STEM fields. The current Scientific Literacy Survey for College Preparedness in STEM (SLSCP-STEM) scale was…

Inferring cortical function in the mouse visual system through large-scale systems neuroscience.

PubMed

Hawrylycz, Michael; Anastassiou, Costas; Arkhipov, Anton; Berg, Jim; Buice, Michael; Cain, Nicholas; Gouwens, Nathan W; Gratiy, Sergey; Iyer, Ramakrishnan; Lee, Jung Hoon; Mihalas, Stefan; Mitelut, Catalin; Olsen, Shawn; Reid, R Clay; Teeter, Corinne; de Vries, Saskia; Waters, Jack; Zeng, Hongkui; Koch, Christof

2016-07-05

The scientific mission of the Project MindScope is to understand neocortex, the part of the mammalian brain that gives rise to perception, memory, intelligence, and consciousness. We seek to quantitatively evaluate the hypothesis that neocortex is a relatively homogeneous tissue, with smaller functional modules that perform a common computational function replicated across regions. We here focus on the mouse as a mammalian model organism with genetics, physiology, and behavior that can be readily studied and manipulated in the laboratory. We seek to describe the operation of cortical circuitry at the computational level by comprehensively cataloging and characterizing its cellular building blocks along with their dynamics and their cell type-specific connectivities. The project is also building large-scale experimental platforms (i.e., brain observatories) to record the activity of large populations of cortical neurons in behaving mice subject to visual stimuli. A primary goal is to understand the series of operations from visual input in the retina to behavior by observing and modeling the physical transformations of signals in the corticothalamic system. We here focus on the contribution that computer modeling and theory make to this long-term effort.

Trinity to Trinity 1945-2015

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moniz, Ernest; Carr, Alan; Bethe, Hans

The Trinity Test of July 16, 1945 was the first full-scale, real-world test of a nuclear weapon; with the new Trinity supercomputer Los Alamos National Laboratory's goal is to do this virtually, in 3D. Trinity was the culmination of a fantastic effort of groundbreaking science and engineering by hundreds of men and women at Los Alamos and other Manhattan Project sites. It took them less than two years to change the world. The Laboratory is marking the 70th anniversary of the Trinity Test because it not only ushered in the Nuclear Age, but with it the origin of today’s advancedmore » supercomputing. We live in the Age of Supercomputers due in large part to nuclear weapons science here at Los Alamos. National security science, and nuclear weapons science in particular, at Los Alamos National Laboratory have provided a key motivation for the evolution of large-scale scientific computing. Beginning with the Manhattan Project there has been a constant stream of increasingly significant, complex problems in nuclear weapons science whose timely solutions demand larger and faster computers. The relationship between national security science at Los Alamos and the evolution of computing is one of interdependence.« less

The Cell Collective: Toward an open and collaborative approach to systems biology

PubMed Central

2012-01-01

Background Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety. Results The Cell Collective is a platform that allows the world-wide scientific community to create these models collectively. Its interface enables users to build and use models without specifying any mathematical equations or computer code - addressing one of the major hurdles with computational research. In addition, this platform allows scientists to simulate and analyze the models in real-time on the web, including the ability to simulate loss/gain of function and test what-if scenarios in real time. Conclusions The Cell Collective is a web-based platform that enables laboratory scientists from across the globe to collaboratively build large-scale models of various biological processes, and simulate/analyze them in real time. In this manuscript, we show examples of its application to a large-scale model of signal transduction. PMID:22871178

Trinity to Trinity 1945-2015

ScienceCinema

Moniz, Ernest; Carr, Alan; Bethe, Hans; Morrison, Phillip; Ramsay, Norman; Teller, Edward; Brixner, Berlyn; Archer, Bill; Agnew, Harold; Morrison, John

2018-01-16

The Trinity Test of July 16, 1945 was the first full-scale, real-world test of a nuclear weapon; with the new Trinity supercomputer Los Alamos National Laboratory's goal is to do this virtually, in 3D. Trinity was the culmination of a fantastic effort of groundbreaking science and engineering by hundreds of men and women at Los Alamos and other Manhattan Project sites. It took them less than two years to change the world. The Laboratory is marking the 70th anniversary of the Trinity Test because it not only ushered in the Nuclear Age, but with it the origin of today’s advanced supercomputing. We live in the Age of Supercomputers due in large part to nuclear weapons science here at Los Alamos. National security science, and nuclear weapons science in particular, at Los Alamos National Laboratory have provided a key motivation for the evolution of large-scale scientific computing. Beginning with the Manhattan Project there has been a constant stream of increasingly significant, complex problems in nuclear weapons science whose timely solutions demand larger and faster computers. The relationship between national security science at Los Alamos and the evolution of computing is one of interdependence.

Tri-Laboratory Linux Capacity Cluster 2007 SOW

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seager, M

2007-03-22

The Advanced Simulation and Computing (ASC) Program (formerly know as Accelerated Strategic Computing Initiative, ASCI) has led the world in capability computing for the last ten years. Capability computing is defined as a world-class platform (in the Top10 of the Top500.org list) with scientific simulations running at scale on the platform. Example systems are ASCI Red, Blue-Pacific, Blue-Mountain, White, Q, RedStorm, and Purple. ASC applications have scaled to multiple thousands of CPUs and accomplished a long list of mission milestones on these ASC capability platforms. However, the computing demands of the ASC and Stockpile Stewardship programs also include a vastmore » number of smaller scale runs for day-to-day simulations. Indeed, every 'hero' capability run requires many hundreds to thousands of much smaller runs in preparation and post processing activities. In addition, there are many aspects of the Stockpile Stewardship Program (SSP) that can be directly accomplished with these so-called 'capacity' calculations. The need for capacity is now so great within the program that it is increasingly difficult to allocate the computer resources required by the larger capability runs. To rectify the current 'capacity' computing resource shortfall, the ASC program has allocated a large portion of the overall ASC platforms budget to 'capacity' systems. In addition, within the next five to ten years the Life Extension Programs (LEPs) for major nuclear weapons systems must be accomplished. These LEPs and other SSP programmatic elements will further drive the need for capacity calculations and hence 'capacity' systems as well as future ASC capability calculations on 'capability' systems. To respond to this new workload analysis, the ASC program will be making a large sustained strategic investment in these capacity systems over the next ten years, starting with the United States Government Fiscal Year 2007 (GFY07). However, given the growing need for 'capability' systems as well, the budget demands are extreme and new, more cost effective ways of fielding these systems must be developed. This Tri-Laboratory Linux Capacity Cluster (TLCC) procurement represents the ASC first investment vehicle in these capacity systems. It also represents a new strategy for quickly building, fielding and integrating many Linux clusters of various sizes into classified and unclassified production service through a concept of Scalable Units (SU). The programmatic objective is to dramatically reduce the overall Total Cost of Ownership (TCO) of these 'capacity' systems relative to the best practices in Linux Cluster deployments today. This objective only makes sense in the context of these systems quickly becoming very robust and useful production clusters under the crushing load that will be inflicted on them by the ASC and SSP scientific simulation capacity workload.« less

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

NASA Astrophysics Data System (ADS)

Bergey, Bradley W.; Ketelhut, Diane Jass; Liang, Senfeng; Natarajan, Uma; Karakus, Melissa

2015-10-01

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.

Handling the Diversity in the Coming Flood of InSAR Data with the InSAR Scientific Computing Environment

NASA Astrophysics Data System (ADS)

Rosen, P. A.; Gurrola, E. M.; Sacco, G. F.; Agram, P. S.; Lavalle, M.; Zebker, H. A.

2014-12-01

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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hules, John

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.

Data Provenance Hybridization Supporting Extreme-Scale Scientific WorkflowApplications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elsethagen, Todd O.; Stephan, Eric G.; Raju, Bibi

As high performance computing (HPC) infrastructures continue to grow in capability and complexity, so do the applications that they serve. HPC and distributed-area computing (DAC) (e.g. grid and cloud) users are looking increasingly toward workflow solutions to orchestrate their complex application coupling, pre- and post-processing needs To gain insight and a more quantitative understanding of a workflow’s performance our method includes not only the capture of traditional provenance information, but also the capture and integration of system environment metrics helping to give context and explanation for a workflow’s execution. In this paper, we describe IPPD’s provenance management solution (ProvEn) andmore » its hybrid data store combining both of these data provenance perspectives.« less

JACOB: an enterprise framework for computational chemistry.

PubMed

Waller, Mark P; Dresselhaus, Thomas; Yang, Jack

2013-06-15

Here, we present just a collection of beans (JACOB): an integrated batch-based framework designed for the rapid development of computational chemistry applications. The framework expedites developer productivity by handling the generic infrastructure tier, and can be easily extended by user-specific scientific code. Paradigms from enterprise software engineering were rigorously applied to create a scalable, testable, secure, and robust framework. A centralized web application is used to configure and control the operation of the framework. The application-programming interface provides a set of generic tools for processing large-scale noninteractive jobs (e.g., systematic studies), or for coordinating systems integration (e.g., complex workflows). The code for the JACOB framework is open sourced and is available at: www.wallerlab.org/jacob. Copyright © 2013 Wiley Periodicals, Inc.

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…

The Versatile Terminal.

ERIC Educational Resources Information Center

Evans, C. D.

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…

Beowulf Distributed Processing and the United States Geological Survey

USGS Publications Warehouse

Maddox, Brian G.

2002-01-01

Introduction In recent years, the United States Geological Survey's (USGS) National Mapping Discipline (NMD) has expanded its scientific and research activities. Work is being conducted in areas such as emergency response research, scientific visualization, urban prediction, and other simulation activities. Custom-produced digital data have become essential for these types of activities. High-resolution, remotely sensed datasets are also seeing increased use. Unfortunately, the NMD is also finding that it lacks the resources required to perform some of these activities. Many of these projects require large amounts of computer processing resources. Complex urban-prediction simulations, for example, involve large amounts of processor-intensive calculations on large amounts of input data. This project was undertaken to learn and understand the concepts of distributed processing. Experience was needed in developing these types of applications. The idea was that this type of technology could significantly aid the needs of the NMD scientific and research programs. Porting a numerically intensive application currently being used by an NMD science program to run in a distributed fashion would demonstrate the usefulness of this technology. There are several benefits that this type of technology can bring to the USGS's research programs. Projects can be performed that were previously impossible due to a lack of computing resources. Other projects can be performed on a larger scale than previously possible. For example, distributed processing can enable urban dynamics research to perform simulations on larger areas without making huge sacrifices in resolution. The processing can also be done in a more reasonable amount of time than with traditional single-threaded methods (a scaled version of Chester County, Pennsylvania, took about fifty days to finish its first calibration phase with a single-threaded program). This paper has several goals regarding distributed processing technology. It will describe the benefits of the technology. Real data about a distributed application will be presented as an example of the benefits that this technology can bring to USGS scientific programs. Finally, some of the issues with distributed processing that relate to USGS work will be discussed.

Amplify scientific discovery with artificial intelligence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gil, Yolanda; Greaves, Mark T.; Hendler, James

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

A distributed computing environment with support for constraint-based task scheduling and scientific experimentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ahrens, J.P.; Shapiro, L.G.; Tanimoto, S.L.

1997-04-01

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

An engineering closure for heavily under-resolved coarse-grid CFD in large applications

NASA Astrophysics Data System (ADS)

Class, Andreas G.; Yu, Fujiang; Jordan, Thomas

2016-11-01

Even though high performance computation allows very detailed description of a wide range of scales in scientific computations, engineering simulations used for design studies commonly merely resolve the large scales thus speeding up simulation time. The coarse-grid CFD (CGCFD) methodology is developed for flows with repeated flow patterns as often observed in heat exchangers or porous structures. It is proposed to use inviscid Euler equations on a very coarse numerical mesh. This coarse mesh needs not to conform to the geometry in all details. To reinstall physics on all smaller scales cheap subgrid models are employed. Subgrid models are systematically constructed by analyzing well-resolved generic representative simulations. By varying the flow conditions in these simulations correlations are obtained. These comprehend for each individual coarse mesh cell a volume force vector and volume porosity. Moreover, for all vertices, surface porosities are derived. CGCFD is related to the immersed boundary method as both exploit volume forces and non-body conformal meshes. Yet, CGCFD differs with respect to the coarser mesh and the use of Euler equations. We will describe the methodology based on a simple test case and the application of the method to a 127 pin wire-wrap fuel bundle.

Final Scientific Report: A Scalable Development Environment for Peta-Scale Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Karbach, Carsten; Frings, Wolfgang

2013-02-22

This document is the final scientific report of the project DE-SC000120 (A scalable Development Environment for Peta-Scale Computing). The objective of this project is the extension of the Parallel Tools Platform (PTP) for applying it to peta-scale systems. PTP is an integrated development environment for parallel applications. It comprises code analysis, performance tuning, parallel debugging and system monitoring. The contribution of the Juelich Supercomputing Centre (JSC) aims to provide a scalable solution for system monitoring of supercomputers. This includes the development of a new communication protocol for exchanging status data between the target remote system and the client running PTP.more » The communication has to work for high latency. PTP needs to be implemented robustly and should hide the complexity of the supercomputer's architecture in order to provide a transparent access to various remote systems via a uniform user interface. This simplifies the porting of applications to different systems, because PTP functions as abstraction layer between parallel application developer and compute resources. The common requirement for all PTP components is that they have to interact with the remote supercomputer. E.g. applications are built remotely and performance tools are attached to job submissions and their output data resides on the remote system. Status data has to be collected by evaluating outputs of the remote job scheduler and the parallel debugger needs to control an application executed on the supercomputer. The challenge is to provide this functionality for peta-scale systems in real-time. The client server architecture of the established monitoring application LLview, developed by the JSC, can be applied to PTP's system monitoring. LLview provides a well-arranged overview of the supercomputer's current status. A set of statistics, a list of running and queued jobs as well as a node display mapping running jobs to their compute resources form the user display of LLview. These monitoring features have to be integrated into the development environment. Besides showing the current status PTP's monitoring also needs to allow for submitting and canceling user jobs. Monitoring peta-scale systems especially deals with presenting the large amount of status data in a useful manner. Users require to select arbitrary levels of detail. The monitoring views have to provide a quick overview of the system state, but also need to allow for zooming into specific parts of the system, into which the user is interested in. At present, the major batch systems running on supercomputers are PBS, TORQUE, ALPS and LoadLeveler, which have to be supported by both the monitoring and the job controlling component. Finally, PTP needs to be designed as generic as possible, so that it can be extended for future batch systems.« less

Computer-Supported Aids to Making Sense of Scientific Articles: Cognitive, Motivational, and Attitudinal Effects

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…

Scientific Computing for Chemists: An Undergraduate Course in Simulations, Data Processing, and Visualization

ERIC Educational Resources Information Center

Weiss, Charles J.

2017-01-01

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…

Computational chemistry in pharmaceutical research: at the crossroads.

PubMed

Bajorath, Jürgen

2012-01-01

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.

[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.

Scholarly literature and the press: scientific impact and social perception of physics computing

NASA Astrophysics Data System (ADS)

Pia, M. G.; Basaglia, T.; Bell, Z. W.; Dressendorfer, P. V.

2014-06-01

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.

Software Reuse Methods to Improve Technological Infrastructure for e-Science

NASA Technical Reports Server (NTRS)

Marshall, James J.; Downs, Robert R.; Mattmann, Chris A.

2011-01-01

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.

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Sohn, Andrew

1996-01-01

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.

Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stone, John E.; Sener, Melih; Vandivort, Kirby L.

The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. In this paper, we present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. Finally, we describemore » the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers.« less

Computational intelligence in bioinformatics: SNP/haplotype data in genetic association study for common diseases.

PubMed

Kelemen, Arpad; Vasilakos, Athanasios V; Liang, Yulan

2009-09-01

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.

Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stone, John E.; Sener, Melih; Vandivort, Kirby L.

The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. We present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. We describe the techniques that weremore » used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers.« less

Atomic detail visualization of photosynthetic membranes with GPU-accelerated ray tracing

DOE PAGES

Stone, John E.; Sener, Melih; Vandivort, Kirby L.; ...

2015-12-12

The cellular process responsible for providing energy for most life on Earth, namely, photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological and computational challenges. In this paper, we present, in two accompanying movies, light-harvesting in the photosynthetic apparatus found in purple bacteria, the so-called chromatophore. The movies are the culmination of three decades of modeling efforts, featuring the collaboration of theoretical, experimental, and computational scientists. Finally, we describemore » the techniques that were used to build, simulate, analyze, and visualize the structures shown in the movies, and we highlight cases where scientific needs spurred the development of new parallel algorithms that efficiently harness GPU accelerators and petascale computers.« less

Statistical methods and computing for big data.

PubMed

Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth; Wu, Jing; Yan, Jun

2016-01-01

Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay.

Statistical methods and computing for big data

PubMed Central

Wang, Chun; Chen, Ming-Hui; Schifano, Elizabeth; Wu, Jing

2016-01-01

Big data are data on a massive scale in terms of volume, intensity, and complexity that exceed the capacity of standard analytic tools. They present opportunities as well as challenges to statisticians. The role of computational statisticians in scientific discovery from big data analyses has been under-recognized even by peer statisticians. This article summarizes recent methodological and software developments in statistics that address the big data challenges. Methodologies are grouped into three classes: subsampling-based, divide and conquer, and online updating for stream data. As a new contribution, the online updating approach is extended to variable selection with commonly used criteria, and their performances are assessed in a simulation study with stream data. Software packages are summarized with focuses on the open source R and R packages, covering recent tools that help break the barriers of computer memory and computing power. Some of the tools are illustrated in a case study with a logistic regression for the chance of airline delay. PMID:27695593

From Interfaces to Bulk: Experimental-Computational Studies Across Time and Length Scales of Multi-Functional Ionic Polymers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perahia, Dvora; Grest, Gary S.

Neutron experiments coupled with computational components have resulted in unprecedented understanding of the factors that impact the behavior of ionic structured polymers. Additionally, new computational tools to study macromolecules, were developed. In parallel, this DOE funding have enabled the education of the next generation of material researchers who are able to take the advantage neutron tools offer to the understanding and design of advanced materials. Our research has provided unprecedented insight into one of the major factors that limits the use of ionizable polymers, combining the macroscopic view obtained from the experimental techniques with molecular insight extracted from computational studiesmore » leading to transformative knowledge that will impact the design of nano-structured, materials. With the focus on model systems, of broad interest to the scientific community and to industry, the research addressed challenges that cut across a large number of polymers, independent of the specific chemical structure or the transported species.« less

Hydrodynamic Simulations and Tomographic Reconstructions of the Intergalactic Medium

NASA Astrophysics Data System (ADS)

Stark, Casey William

The Intergalactic Medium (IGM) is the dominant reservoir of matter in the Universe from which the cosmic web and galaxies form. The structure and physical state of the IGM provides insight into the cosmological model of the Universe, the origin and timeline of the reionization of the Universe, as well as being an essential ingredient in our understanding of galaxy formation and evolution. Our primary handle on this information is a signal known as the Lyman-alpha forest (or Ly-alpha forest) -- the collection of absorption features in high-redshift sources due to intervening neutral hydrogen, which scatters HI Ly-alpha photons out of the line of sight. The Ly-alpha forest flux traces density fluctuations at high redshift and at moderate overdensities, making it an excellent tool for mapping large-scale structure and constraining cosmological parameters. Although the computational methodology for simulating the Ly-alpha forest has existed for over a decade, we are just now approaching the scale of computing power required to simultaneously capture large cosmological scales and the scales of the smallest absorption systems. My thesis focuses on using simulations at the edge of modern computing to produce precise predictions of the statistics of the Ly-alpha forest and to better understand the structure of the IGM. In the first part of my thesis, I review the state of hydrodynamic simulations of the IGM, including pitfalls of the existing under-resolved simulations. Our group developed a new cosmological hydrodynamics code to tackle the computational challenge, and I developed a distributed analysis framework to compute flux statistics from our simulations. I present flux statistics derived from a suite of our large hydrodynamic simulations and demonstrate convergence to the per cent level. I also compare flux statistics derived from simulations using different discretizations and hydrodynamic schemes (Eulerian finite volume vs. smoothed particle hydrodynamics) and discuss differences in their convergence behavior, their overall agreement, and the implications for cosmological constraints. In the second part of my thesis, I present a tomographic reconstruction method that allows us to make 3D maps of the IGM with Mpc resolution. In order to make reconstructions of large surveys computationally feasible, I developed a new Wiener Filter application with an algorithm specialized to our problem, which significantly reduces the space and time complexity compared to previous implementations. I explore two scientific applications of the maps: finding protoclusters by searching the maps for large, contiguous regions of low flux and finding cosmic voids by searching the maps for regions of high flux. Using a large N-body simulation, I identify and characterize both protoclusters and voids at z = 2.5, in the middle of the redshift range being mapped by ongoing surveys. I provide simple methods for identifying protocluster and void candidates in the tomographic flux maps, and then test them on mock surveys and reconstructions. I present forecasts for sample purity and completeness and other scientific applications of these large, high-redshift objects.

Convective phenomena at high resolution over Europe and the Mediterranean. The join EURO-CORDEX and Med-CORDEX flagship pilot study

NASA Astrophysics Data System (ADS)

Coppola, Erika; Sobolowski, Stefan

2017-04-01

The join EURO-CORDEX and Med-CORDEX Flagship Pilot Study dedicated to the frontier research of using convective permitting models to address the impact of human induced climate change on convection, has been recently approved and the scientific community behind the project is made of 30 different scientific institutes distributed all around Europe. The motivations for such a challenge is the availability of large field campaigns dedicated to the study of heavy precipitation events such as HyMeX and high resolution dense observation networks like WegnerNet, RdisaggH (CH),COMEPHORE (Fr), SAFRAN (Fr), EURO4M-APGD (CH); the increased computing capacity and model developments; the emerging trend signals in extreme precipitation at daily and mainly sub-daily time scale in the Mediterranean and Alpine regions and the priority of convective extreme events under the WCRP Grand Challenge on climate extremes, because they carry both society-relevant and scientific challenges. The main objective of this effort are to investigate convective-scale events, their processes and their changes in a few key regions of Europe and the Mediterranean using convection-permitting RCMs, statistical models and available observations. To provide a collective assessment of the modeling capacity at convection-permitting scale and to shape a coherent and collective assessment of the consequences of climate change on convective event impacts at local to regional scales. The scientific aims of this research are to investigate how the convective events and the damaging phenomena associated with them will respond to changing climate conditions in several European regions with different climates. To understand if an improved representation of convective phenomena at convective permitting scales will lead to upscaled added value and finally to assess the possibility to replace these costly convection-permitting experiments with statistical approaches like "convection emulators". The common initial domain will be an extended Alpine domain and all the groups will simulate a minimum of 10 years period with ERA-interim boundary conditions, with the possibility of other two sub-domains one in the Northwest continental Europe and another in the Southeast Mediterranean. The scenario simulations will be completed for three different 10 years time slices one in the historical period, one in the near future and the last one in the far future for the RCP8.5 scenario. The first target of this scientific community is to have an ensemble of 1-2 years ERA-interim simulations ready by next summer.

Multidimensional Environmental Data Resource Brokering on Computational Grids and Scientific Clouds

NASA Astrophysics Data System (ADS)

Montella, Raffaele; Giunta, Giulio; Laccetti, Giuliano

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.

78 FR 6087 - Advanced Scientific Computing Advisory Committee

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-29

... 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...

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.

Highly parallel computation

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.

Transitioning Rationally Designed Catalytic Materials to Real 'Working' Catalysts Produced at Commercial Scale: Nanoparticle Materials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schaidle, Joshua A.; Habas, Susan E.; Baddour, Frederick G.

Catalyst design, from idea to commercialization, requires multi-disciplinary scientific and engineering research and development over 10-20 year time periods. Historically, the identification of new or improved catalyst materials has largely been an empirical trial-and-error process. However, advances in computational capabilities (new tools and increased processing power) coupled with new synthetic techniques have started to yield rationally-designed catalysts with controlled nano-structures and tailored properties. This technological advancement represents an opportunity to accelerate the catalyst development timeline and to deliver new materials that outperform existing industrial catalysts or enable new applications, once a number of unique challenges associated with the scale-up ofmore » nano-structured materials are overcome.« less

Building Cognition: The Construction of Computational Representations for Scientific Discovery

ERIC Educational Resources Information Center

Chandrasekharan, Sanjay; Nersessian, Nancy J.

2015-01-01

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…

Message from the ISCB: ISCB Ebola award for important future research on the computational biology of Ebola virus.

PubMed

Karp, Peter D; Berger, Bonnie; Kovats, Diane; Lengauer, Thomas; Linial, Michal; Sabeti, Pardis; Hide, Winston; Rost, Burkhard

2015-02-15

Speed is of the essence in combating Ebola; thus, computational approaches should form a significant component of Ebola research. As for the development of any modern drug, computational biology is uniquely positioned to contribute through comparative analysis of the genome sequences of Ebola strains and three-dimensional protein modeling. Other computational approaches to Ebola may include large-scale docking studies of Ebola proteins with human proteins and with small-molecule libraries, computational modeling of the spread of the virus, computational mining of the Ebola literature and creation of a curated Ebola database. Taken together, such computational efforts could significantly accelerate traditional scientific approaches. In recognition of the need for important and immediate solutions from the field of computational biology against Ebola, the International Society for Computational Biology (ISCB) announces a prize for an important computational advance in fighting the Ebola virus. ISCB will confer the ISCB Fight against Ebola Award, along with a prize of US$2000, at its July 2016 annual meeting (ISCB Intelligent Systems for Molecular Biology 2016, Orlando, FL). dkovats@iscb.org or rost@in.tum.de. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

DOE Advanced Scientific Computing Advisory Committee (ASCAC) Subcommittee Report on Scientific and Technical Information

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hey, Tony; Agarwal, Deborah; Borgman, Christine

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.

Interactive Scripting for Analysis and Visualization of Arbitrarily Large, Disparately Located Climate Data Ensembles Using a Progressive Runtime Server

NASA Astrophysics Data System (ADS)

Christensen, C.; Summa, B.; Scorzelli, G.; Lee, J. W.; Venkat, A.; Bremer, P. T.; Pascucci, V.

2017-12-01

Massive datasets are becoming more common due to increasingly detailed simulations and higher resolution acquisition devices. Yet accessing and processing these huge data collections for scientific analysis is still a significant challenge. Solutions that rely on extensive data transfers are increasingly untenable and often impossible due to lack of sufficient storage at the client side as well as insufficient bandwidth to conduct such large transfers, that in some cases could entail petabytes of data. Large-scale remote computing resources can be useful, but utilizing such systems typically entails some form of offline batch processing with long delays, data replications, and substantial cost for any mistakes. Both types of workflows can severely limit the flexible exploration and rapid evaluation of new hypotheses that are crucial to the scientific process and thereby impede scientific discovery. In order to facilitate interactivity in both analysis and visualization of these massive data ensembles, we introduce a dynamic runtime system suitable for progressive computation and interactive visualization of arbitrarily large, disparately located spatiotemporal datasets. Our system includes an embedded domain-specific language (EDSL) that allows users to express a wide range of data analysis operations in a simple and abstract manner. The underlying runtime system transparently resolves issues such as remote data access and resampling while at the same time maintaining interactivity through progressive and interruptible processing. Computations involving large amounts of data can be performed remotely in an incremental fashion that dramatically reduces data movement, while the client receives updates progressively thereby remaining robust to fluctuating network latency or limited bandwidth. This system facilitates interactive, incremental analysis and visualization of massive remote datasets up to petabytes in size. Our system is now available for general use in the community through both docker and anaconda.

From computer-assisted intervention research to clinical impact: The need for a holistic approach.

PubMed

Ourselin, Sébastien; Emberton, Mark; Vercauteren, Tom

2016-10-01

The early days of the field of medical image computing (MIC) and computer-assisted intervention (CAI), when publishing a strong self-contained methodological algorithm was enough to produce impact, are over. As a community, we now have substantial responsibility to translate our scientific progresses into improved patient care. In the field of computer-assisted interventions, the emphasis is also shifting from the mere use of well-known established imaging modalities and position trackers to the design and combination of innovative sensing, elaborate computational models and fine-grained clinical workflow analysis to create devices with unprecedented capabilities. The barriers to translating such devices in the complex and understandably heavily regulated surgical and interventional environment can seem daunting. Whether we leave the translation task mostly to our industrial partners or welcome, as researchers, an important share of it is up to us. We argue that embracing the complexity of surgical and interventional sciences is mandatory to the evolution of the field. Being able to do so requires large-scale infrastructure and a critical mass of expertise that very few research centres have. In this paper, we emphasise the need for a holistic approach to computer-assisted interventions where clinical, scientific, engineering and regulatory expertise are combined as a means of moving towards clinical impact. To ensure that the breadth of infrastructure and expertise required for translational computer-assisted intervention research does not lead to a situation where the field advances only thanks to a handful of exceptionally large research centres, we also advocate that solutions need to be designed to lower the barriers to entry. Inspired by fields such as particle physics and astronomy, we claim that centralised very large innovation centres with state of the art technology and health technology assessment capabilities backed by core support staff and open interoperability standards need to be accessible to the wider computer-assisted intervention research community. Copyright © 2016. Published by Elsevier B.V.

Computational Scientific Inquiry with Virtual Worlds and Agent-Based Models: New Ways of Doing Science to Learn Science

ERIC Educational Resources Information Center

Jacobson, Michael J.; Taylor, Charlotte E.; Richards, Deborah

2016-01-01

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…

Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

ERIC Educational Resources Information Center

Hulshof, Casper D.; de Jong, Ton

2006-01-01

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…

Modeling Primary Atomization of Liquid Fuels using a Multiphase DNS/LES Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arienti, Marco; Oefelein, Joe; Doisneau, Francois

2016-08-01

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.

Graphics processing units in bioinformatics, computational biology and systems biology.

PubMed

Nobile, Marco S; Cazzaniga, Paolo; Tangherloni, Andrea; Besozzi, Daniela

2017-09-01

Several studies in Bioinformatics, Computational Biology and Systems Biology rely on the definition of physico-chemical or mathematical models of biological systems at different scales and levels of complexity, ranging from the interaction of atoms in single molecules up to genome-wide interaction networks. Traditional computational methods and software tools developed in these research fields share a common trait: they can be computationally demanding on Central Processing Units (CPUs), therefore limiting their applicability in many circumstances. To overcome this issue, general-purpose Graphics Processing Units (GPUs) are gaining an increasing attention by the scientific community, as they can considerably reduce the running time required by standard CPU-based software, and allow more intensive investigations of biological systems. In this review, we present a collection of GPU tools recently developed to perform computational analyses in life science disciplines, emphasizing the advantages and the drawbacks in the use of these parallel architectures. The complete list of GPU-powered tools here reviewed is available at http://bit.ly/gputools. © The Author 2016. Published by Oxford University Press.

OPENING REMARKS: SciDAC: Scientific Discovery through Advanced Computing

NASA Astrophysics Data System (ADS)

Strayer, Michael

2005-01-01

Good morning. Welcome to SciDAC 2005 and San Francisco. SciDAC is all about computational science and scientific discovery. In a large sense, computational science characterizes SciDAC and its intent is change. It transforms both our approach and our understanding of science. It opens new doors and crosses traditional boundaries while seeking discovery. In terms of twentieth century methodologies, computational science may be said to be transformational. There are a number of examples to this point. First are the sciences that encompass climate modeling. The application of computational science has in essence created the field of climate modeling. This community is now international in scope and has provided precision results that are challenging our understanding of our environment. A second example is that of lattice quantum chromodynamics. Lattice QCD, while adding precision and insight to our fundamental understanding of strong interaction dynamics, has transformed our approach to particle and nuclear science. The individual investigator approach has evolved to teams of scientists from different disciplines working side-by-side towards a common goal. SciDAC is also undergoing a transformation. This meeting is a prime example. Last year it was a small programmatic meeting tracking progress in SciDAC. This year, we have a major computational science meeting with a variety of disciplines and enabling technologies represented. SciDAC 2005 should position itself as a new corner stone for Computational Science and its impact on science. As we look to the immediate future, FY2006 will bring a new cycle to SciDAC. Most of the program elements of SciDAC will be re-competed in FY2006. The re-competition will involve new instruments for computational science, new approaches for collaboration, as well as new disciplines. There will be new opportunities for virtual experiments in carbon sequestration, fusion, and nuclear power and nuclear waste, as well as collaborations with industry and virtual prototyping. New instruments of collaboration will include institutes and centers while summer schools, workshops and outreach will invite new talent and expertise. Computational science adds new dimensions to science and its practice. Disciplines of fusion, accelerator science, and combustion are poised to blur the boundaries between pure and applied science. As we open the door into FY2006 we shall see a landscape of new scientific challenges: in biology, chemistry, materials, and astrophysics to name a few. The enabling technologies of SciDAC have been transformational as drivers of change. Planning for major new software systems assumes a base line employing Common Component Architectures and this has become a household word for new software projects. While grid algorithms and mesh refinement software have transformed applications software, data management and visualization have transformed our understanding of science from data. The Gordon Bell prize now seems to be dominated by computational science and solvers developed by TOPS ISIC. The priorities of the Office of Science in the Department of Energy are clear. The 20 year facilities plan is driven by new science. High performance computing is placed amongst the two highest priorities. Moore's law says that by the end of the next cycle of SciDAC we shall have peta-flop computers. The challenges of petascale computing are enormous. These and the associated computational science are the highest priorities for computing within the Office of Science. Our effort in Leadership Class computing is just a first step towards this goal. Clearly, computational science at this scale will face enormous challenges and possibilities. Performance evaluation and prediction will be critical to unraveling the needed software technologies. We must not lose sight of our overarching goal—that of scientific discovery. Science does not stand still and the landscape of science discovery and computing holds immense promise. In this environment, I believe it is necessary to institute a system of science based performance metrics to help quantify our progress towards science goals and scientific computing. As a final comment I would like to reaffirm that the shifting landscapes of science will force changes to our computational sciences, and leave you with the quote from Richard Hamming, 'The purpose of computing is insight, not numbers'.

Knowledge Discovery from Climate Data using Graph-Based Methods

NASA Astrophysics Data System (ADS)

Steinhaeuser, K.

2012-04-01

Climate and Earth sciences have recently experienced a rapid transformation from a historically data-poor to a data-rich environment, thus bringing them into the realm of the Fourth Paradigm of scientific discovery - a term coined by the late Jim Gray (Hey et al. 2009), the other three being theory, experimentation and computer simulation. In particular, climate-related observations from remote sensors on satellites and weather radars, in situ sensors and sensor networks, as well as outputs of climate or Earth system models from large-scale simulations, provide terabytes of spatio-temporal data. These massive and information-rich datasets offer a significant opportunity for advancing climate science and our understanding of the global climate system, yet current analysis techniques are not able to fully realize their potential benefits. We describe a class of computational approaches, specifically from the data mining and machine learning domains, which may be novel to the climate science domain and can assist in the analysis process. Computer scientists have developed spatial and spatio-temporal analysis techniques for a number of years now, and many of them may be applicable and/or adaptable to problems in climate science. We describe a large-scale, NSF-funded project aimed at addressing climate science question using computational analysis methods; team members include computer scientists, statisticians, and climate scientists from various backgrounds. One of the major thrusts is in the development of graph-based methods, and several illustrative examples of recent work in this area will be presented.

Convective phenomena at high resolution over Europe and the Mediterranean. The join EURO-CORDEX and Med-CORDEX flagship pilot study

NASA Astrophysics Data System (ADS)

Coppola, E.; Sobolowski, S.

2017-12-01

The join EURO-CORDEX and Med-CORDEX Flagship Pilot Study dedicated to the frontier research of using convective permitting (CP) models to address the impact of human induced climate change on convection, has been recently approved and the scientific community behind the project is made of 30 different scientific European institutes. The motivations for such a challenge is the availability of large field campaigns dedicated to the study of heavy precipitation events; the increased computing capacity and model developments; the emerging trend signals in extreme precipitation at daily and mainly sub-daily time scale in the Mediterranean and Alpine regions and the priority of convective extreme events under the WCRP Grand Challenge on climate extremes. The main objective of this effort are to investigate convective-scale events, their processes and changes in a few key regions of Europe and the Mediterranean using CP RCMs, statistical models and available observations. To provide a collective assessment of the modeling capacity at CP scale and to shape a coherent and collective assessment of the consequences of climate change on convective event impacts at local to regional scales. The scientific aims of this research are to investigate how the convective events and the damaging phenomena associated with them will respond to changing climate conditions in different European climates zone. To understand if an improved representation of convective phenomena at convective permitting scales will lead to upscaled added value and finally to assess the possibility to replace these costly convection-permitting experiments with statistical approaches like "convection emulators". The common initial domain will be an extended Alpine domain and all the groups will simulate a minimum of 10 years period with ERA-interim boundary conditions, with the possibility of other two sub-domains one in the Northwest continental Europe and another in the Southeast Mediterranean. The scenario simulations will be completed for three different 10 years time slices one in the historical period, one in the near future and the last one in the far future for the RCP8.5 scenario. The first target of this scientific community is to have an ensemble of 1-2 years ERA-interim simulations ready by late 2017 and a set of test cases to use as a pilot study.

An Interdisciplinary Guided Inquiry on Estuarine Transport Using a Computer Model in High School Classrooms

ERIC Educational Resources Information Center

Chan, Kit Yu Karen; Yang, Sylvia; Maliska, Max E.; Grunbaum, Daniel

2012-01-01

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…

Architectural Principles and Experimentation of Distributed High Performance Virtual Clusters

ERIC Educational Resources Information Center

Younge, Andrew J.

2016-01-01

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…

An Analysis on the Effect of Computer Self-Efficacy over Scientific Research Self-Efficacy and Information Literacy Self-Efficacy

ERIC Educational Resources Information Center

Tuncer, Murat

2013-01-01

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…

The Impact of Three-Dimensional Computational Modeling on Student Understanding of Astronomical Concepts: A Quantitative Analysis

ERIC Educational Resources Information Center

Hansen, John; Barnett, Michael; MaKinster, James; Keating, Thomas

2004-01-01

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…

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.

Materials and structure synergistic with in-space materials utilization. [as means of reducing costs of space missions, colonization, and settlements

NASA Technical Reports Server (NTRS)

Ramohalli, Kumar; Shadman, Farhang; Sridhar, K. R.

1992-01-01

The significant advances made recently toward actual hardware realizations of various concepts for the application of in-space materials utilization (ISMU) are demonstrated. The overall plan for taking innovative concepts through technical feasibility, small-scale tests, scale-up, computer modeling, and larger-scale execution is outlined. Two specific fields of endeavor are surveyed: one has direct applications to construction on the moon, while the other has more basic implications, in addition to the practical aspects of lunar colonies. Several fundamental scientific advances made in the characterization of the physical and chemical processes that need to be elucidated for any intelligent application of the ISMU concepts in future space missions are described. A rigorous quantitative technique for the unambiguous evaluation of various components and component technology that form any space (or terrestrial mission) is also described.

A Survey of Collectives

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Wolpert, David

2004-01-01

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.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perumalla, Kalyan S.; Yoginath, Srikanth B.

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

USRA/RIACS

NASA Technical Reports Server (NTRS)

Oliger, Joseph

1992-01-01

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 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.

Using Computer Simulations for Promoting Model-based Reasoning. Epistemological and Educational Dimensions

NASA Astrophysics Data System (ADS)

Develaki, Maria

2017-11-01

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.

A toolbox and a record for scientific model development

NASA Technical Reports Server (NTRS)

Ellman, Thomas

1994-01-01

Scientific computation can benefit from software tools that facilitate construction of computational models, control the application of models, and aid in revising models to handle new situations. Existing environments for scientific programming provide only limited means of handling these tasks. This paper describes a two pronged approach for handling these tasks: (1) designing a 'Model Development Toolbox' that includes a basic set of model constructing operations; and (2) designing a 'Model Development Record' that is automatically generated during model construction. The record is subsequently exploited by tools that control the application of scientific models and revise models to handle new situations. Our two pronged approach is motivated by our belief that the model development toolbox and record should be highly interdependent. In particular, a suitable model development record can be constructed only when models are developed using a well defined set of operations. We expect this research to facilitate rapid development of new scientific computational models, to help ensure appropriate use of such models and to facilitate sharing of such models among working computational scientists. We are testing this approach by extending SIGMA, and existing knowledge-based scientific software design tool.

MememxGATE: Unearthing Latent Content Features for Improved Search and Relevancy Ranking Across Scientific Literature

NASA Astrophysics Data System (ADS)

Wilson, B. D.; McGibbney, L. J.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Whitehall, K. D.

2015-12-01

Quantifying scientific relevancy is of increasing importance to NASA and the research community. Scientific relevancy may be defined by mapping the impacts of a particular NASA mission, instrument, and/or retrieved variables to disciplines such as climate predictions, natural hazards detection and mitigation processes, education, and scientific discoveries. Related to relevancy, is the ability to expose data with similar attributes. This in turn depends upon the ability for us to extract latent, implicit document features from scientific data and resources and make them explicit, accessible and useable for search activities amongst others. This paper presents MemexGATE; a server side application, command line interface and computing environment for running large scale metadata extraction, general architecture text engineering, document classification and indexing tasks over document resources such as social media streams, scientific literature archives, legal documentation, etc. This work builds on existing experiences using MemexGATE (funded, developed and validated through the DARPA Memex Progrjam PI Mattmann) for extracting and leveraging latent content features from document resources within the Materials Research domain. We extend the software functionality capability to the domain of scientific literature with emphasis on the expansion of gazetteer lists, named entity rules, natural language construct labeling (e.g. synonym, antonym, hyponym, etc.) efforts to enable extraction of latent content features from data hosted by wide variety of scientific literature vendors (AGU Meeting Abstract Database, Springer, Wiley Online, Elsevier, etc.) hosting earth science literature. Such literature makes both implicit and explicit references to NASA datasets and relationships between such concepts stored across EOSDIS DAAC's hence we envisage that a significant part of this effort will also include development and understanding of relevancy signals which can ultimately be utilized for improved search and relevancy ranking across scientific literature.

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.

The need for scientific software engineering in the pharmaceutical industry

NASA Astrophysics Data System (ADS)

Luty, Brock; Rose, Peter W.

2017-03-01

Scientific software engineering is a distinct discipline from both computational chemistry project support and research informatics. A scientific software engineer not only has a deep understanding of the science of drug discovery but also the desire, skills and time to apply good software engineering practices. A good team of scientific software engineers can create a software foundation that is maintainable, validated and robust. If done correctly, this foundation enable the organization to investigate new and novel computational ideas with a very high level of efficiency.

The need for scientific software engineering in the pharmaceutical industry.

PubMed

Luty, Brock; Rose, Peter W

2017-03-01

Scientific software engineering is a distinct discipline from both computational chemistry project support and research informatics. A scientific software engineer not only has a deep understanding of the science of drug discovery but also the desire, skills and time to apply good software engineering practices. A good team of scientific software engineers can create a software foundation that is maintainable, validated and robust. If done correctly, this foundation enable the organization to investigate new and novel computational ideas with a very high level of efficiency.

Integration and Exposure of Large Scale Computational Resources Across the Earth System Grid Federation (ESGF)

NASA Astrophysics Data System (ADS)

Duffy, D.; Maxwell, T. P.; Doutriaux, C.; Williams, D. N.; Chaudhary, A.; Ames, S.

2015-12-01

As the size of remote sensing observations and model output data grows, the volume of the data has become overwhelming, even to many scientific experts. As societies are forced to better understand, mitigate, and adapt to climate changes, the combination of Earth observation data and global climate model projects is crucial to not only scientists but to policy makers, downstream applications, and even the public. Scientific progress on understanding climate is critically dependent on the availability of a reliable infrastructure that promotes data access, management, and provenance. The Earth System Grid Federation (ESGF) has created such an environment for the Intergovernmental Panel on Climate Change (IPCC). ESGF provides a federated global cyber infrastructure for data access and management of model outputs generated for the IPCC Assessment Reports (AR). The current generation of the ESGF federated grid allows consumers of the data to find and download data with limited capabilities for server-side processing. Since the amount of data for future AR is expected to grow dramatically, ESGF is working on integrating server-side analytics throughout the federation. The ESGF Compute Working Team (CWT) has created a Web Processing Service (WPS) Application Programming Interface (API) to enable access scalable computational resources. The API is the exposure point to high performance computing resources across the federation. Specifically, the API allows users to execute simple operations, such as maximum, minimum, average, and anomalies, on ESGF data without having to download the data. These operations are executed at the ESGF data node site with access to large amounts of parallel computing capabilities. This presentation will highlight the WPS API, its capabilities, provide implementation details, and discuss future developments.

75 FR 65639 - Center for Scientific Review; Notice of Closed Meetings

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-26

...: Computational Biology Special Emphasis Panel A. Date: October 29, 2010. Time: 2 p.m. to 3:30 p.m. Agenda: To.... Name of Committee: Center for Scientific Review Special Emphasis Panel; Member Conflict: Computational...

High Performance Computing Facility Operational Assessment, FY 2011 Oak Ridge Leadership Computing Facility

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Ann E; Bland, Arthur S Buddy; Hack, James J

Oak Ridge National Laboratory's Leadership Computing Facility (OLCF) continues to deliver the most powerful resources in the U.S. for open science. At 2.33 petaflops peak performance, the Cray XT Jaguar delivered more than 1.5 billion core hours in calendar year (CY) 2010 to researchers around the world for computational simulations relevant to national and energy security; advancing the frontiers of knowledge in physical sciences and areas of biological, medical, environmental, and computer sciences; and providing world-class research facilities for the nation's science enterprise. Scientific achievements by OLCF users range from collaboration with university experimentalists to produce a working supercapacitor thatmore » uses atom-thick sheets of carbon materials to finely determining the resolution requirements for simulations of coal gasifiers and their components, thus laying the foundation for development of commercial-scale gasifiers. OLCF users are pushing the boundaries with software applications sustaining more than one petaflop of performance in the quest to illuminate the fundamental nature of electronic devices. Other teams of researchers are working to resolve predictive capabilities of climate models, to refine and validate genome sequencing, and to explore the most fundamental materials in nature - quarks and gluons - and their unique properties. Details of these scientific endeavors - not possible without access to leadership-class computing resources - are detailed in Section 4 of this report and in the INCITE in Review. Effective operations of the OLCF play a key role in the scientific missions and accomplishments of its users. This Operational Assessment Report (OAR) will delineate the policies, procedures, and innovations implemented by the OLCF to continue delivering a petaflop-scale resource for cutting-edge research. The 2010 operational assessment of the OLCF yielded recommendations that have been addressed (Reference Section 1) and where appropriate, changes in Center metrics were introduced. This report covers CY 2010 and CY 2011 Year to Date (YTD) that unless otherwise specified, denotes January 1, 2011 through June 30, 2011. User Support remains an important element of the OLCF operations, with the philosophy 'whatever it takes' to enable successful research. Impact of this center-wide activity is reflected by the user survey results that show users are 'very satisfied.' The OLCF continues to aggressively pursue outreach and training activities to promote awareness - and effective use - of U.S. leadership-class resources (Reference Section 2). The OLCF continues to meet and in many cases exceed DOE metrics for capability usage (35% target in CY 2010, delivered 39%; 40% target in CY 2011, 54% January 1, 2011 through June 30, 2011). The Schedule Availability (SA) and Overall Availability (OA) for Jaguar were exceeded in CY2010. Given the solution to the VRM problem the SA and OA for Jaguar in CY 2011 are expected to exceed the target metrics of 95% and 90%, respectively (Reference Section 3). Numerous and wide-ranging research accomplishments, scientific support, and technological innovations are more fully described in Sections 4 and 6 and reflect OLCF leadership in enabling high-impact science solutions and vision in creating an exascale-ready center. Financial Management (Section 5) and Risk Management (Section 7) are carried out using best practices approved of by DOE. The OLCF has a valid cyber security plan and Authority to Operate (Section 8). The proposed metrics for 2012 are reflected in Section 9.« less

Energy reduction through voltage scaling and lightweight checking

NASA Astrophysics Data System (ADS)

Kadric, Edin

As the semiconductor roadmap reaches smaller feature sizes and the end of Dennard Scaling, design goals change, and managing the power envelope often dominates delay minimization. Voltage scaling remains a powerful tool to reduce energy. We find that it results in about 60% geomean energy reduction on top of other common low-energy optimizations with 22nm CMOS technology. However, when voltage is reduced, it becomes easier for noise and particle strikes to upset a node, potentially causing Silent Data Corruption (SDC). The 60% energy reduction, therefore, comes with a significant drop in reliability. Duplication with checking and triple-modular redundancy are traditional approaches used to combat transient errors, but spending 2--3x the energy for redundant computation can diminish or reverse the benefits of voltage scaling. As an alternative, we explore the opportunity to use checking operations that are cheaper than the base computation they are guarding. We devise a classification system for applications and their lightweight checking characteristics. In particular, we identify and evaluate the effectiveness of lightweight checks in a broad set of common tasks in scientific computing and signal processing. We find that the lightweight checks cost only a fraction of the base computation (0-25%) and allow us to recover the reliability losses from voltage scaling. Overall, we show about 50% net energy reduction without compromising reliability compared to operation at the nominal voltage. We use FPGAs (Field-Programmable Gate Arrays) in our work, although the same ideas can be applied to different systems. On top of voltage scaling, we explore other common low-energy techniques for FPGAs: transmission gates, gate boosting, power gating, low-leakage (high-Vth) processes, and dual-V dd architectures. We do not scale voltage for memories, so lower voltages help us reduce logic and interconnect energy, but not memory energy. At lower voltages, memories become dominant, and we get diminishing returns from continuing to scale voltage. To ensure that memories do not become a bottleneck, we also design an energy-robust FPGA memory architecture, which attempts to minimize communication energy due to mismatches between application and architecture. We do this alongside application parallelism tuning. We show our techniques on a wide range of applications, including a large real-time system used for Wide-Area Motion Imaging (WAMI).

The Effects of Inquiry-Based Computer Simulation with Cooperative Learning on Scientific Thinking and Conceptual Understanding of Gas Laws

ERIC Educational Resources Information Center

Abdullah, Sopiah; Shariff, Adilah

2008-01-01

The purpose of the study was to investigate the effects of inquiry-based computer simulation with heterogeneous-ability cooperative learning (HACL) and inquiry-based computer simulation with friendship cooperative learning (FCL) on (a) scientific reasoning (SR) and (b) conceptual understanding (CU) among Form Four students in Malaysian Smart…

Interoperability of GADU in using heterogeneous Grid resources for bioinformatics applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sulakhe, D.; Rodriguez, A.; Wilde, M.

2008-03-01

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

Uvf - Unified Volume Format: A General System for Efficient Handling of Large Volumetric Datasets.

PubMed

Krüger, Jens; Potter, Kristin; Macleod, Rob S; Johnson, Christopher

2008-01-01

With the continual increase in computing power, volumetric datasets with sizes ranging from only a few megabytes to petascale are generated thousands of times per day. Such data may come from an ordinary source such as simple everyday medical imaging procedures, while larger datasets may be generated from cluster-based scientific simulations or measurements of large scale experiments. In computer science an incredible amount of work worldwide is put into the efficient visualization of these datasets. As researchers in the field of scientific visualization, we often have to face the task of handling very large data from various sources. This data usually comes in many different data formats. In medical imaging, the DICOM standard is well established, however, most research labs use their own data formats to store and process data. To simplify the task of reading the many different formats used with all of the different visualization programs, we present a system for the efficient handling of many types of large scientific datasets (see Figure 1 for just a few examples). While primarily targeted at structured volumetric data, UVF can store just about any type of structured and unstructured data. The system is composed of a file format specification with a reference implementation of a reader. It is not only a common, easy to implement format but also allows for efficient rendering of most datasets without the need to convert the data in memory.

Data Scientists ARE coming of age: but WHERE are they coming from?

NASA Astrophysics Data System (ADS)

Evans, N.; Bastrakova, I.; Connor, N.; Raymond, O.; Wyborn, L. A.

2013-12-01

The fourth paradigm of data intensive science is upon us: a new fundamental scientific methodology has emerged which is underpinned by the capability to analyse large volumes of data using advanced computational capacities. This combination is enabling earth and space scientists to respond to decadal challenges on issues such as the sustainable development of our natural resources, impacts of climate change and protection from national hazards. Fundamental to the data intensive paradigm is data that are readily accessible and capable of being integrated and amalgamated with other data often from multiple sources. For many years Earth and Space science practitioners have been drowning in a data deluge. In many cases, either lacking confidence in their capability and/or not having the time or capacity to manage these data assets they have called in the data professionals. However, such people rarely had domain knowledge of the data they were dealing with and before long it emerged that although the ';containers' of data were now much better managed and documented, in reality the content was locked up and difficult to access, particularly for HPC environments where national to global scale problems were being addressed. Geoscience Australia (GA) is the custodian of over 4 PB of Geoscientific data and is a key provider of evidence-based, scientific advice to government on national issues. Since 2011, in collaboration with CSIRO Minerals Down Under Program, and the National Computational Infrastructure, GA has begun a series of data intensive scientific research pilots that focussed on applying advanced ICT tools and technologies to enhance scientific outcomes for the agency, in particular, national scale analysis of data sets that can be up to 500 TB in size. As in any change program, a small group of innovators and early adopters took up the challenge of data intensive science and quickly showed that GA was able to use new ICT technologies to exploit an information-rich world to undertake applied research and to deliver new business outcomes in ways that current technologies do not allow. The innovators clearly had the necessary skills to rapidly adapt to data intensive techniques. However, if we were to scale out to the rest of the organisation, we needed to quantify these skills. The Strategic People Development Section of GA agreed to: * Conduct a capability analysis of the scientific staff that participated in the pilot projects including a review of university training and post graduate training; and * Conduct capability analysis of the technical groups involved in the pilot projects. The analysis identified the need for multi-disciplinary teams across the spectrum from pure scientists to pure ICT staff along with a key hybrid role - the Data Scientist, who has a greater capacity in mathematical, numerical modelling, statistics, computational skills, software engineering and spatial skills and the ability to integrate data across multiple domains. To fill the emerging gap, GA is asking the questions; how do we find or develop this capability, can we successfully transform the Scientist or the ICT Professional, are our educational facilities modifying their training - but it is certainly leading GA to acknowledge, formalise, and promote a continuum of skills and roles, changing our recruitment, re-assignment and Learning and Development strategic decisions.

Computational data sciences for assessment and prediction of climate extremes

NASA Astrophysics Data System (ADS)

Ganguly, A. R.

2011-12-01

Climate extremes may be defined inclusively as severe weather events or large shifts in global or regional weather patterns which may be caused or exacerbated by natural climate variability or climate change. This area of research arguably represents one of the largest knowledge-gaps in climate science which is relevant for informing resource managers and policy makers. While physics-based climate models are essential in view of non-stationary and nonlinear dynamical processes, their current pace of uncertainty reduction may not be adequate for urgent stakeholder needs. The structure of the models may in some cases preclude reduction of uncertainty for critical processes at scales or for the extremes of interest. On the other hand, methods based on complex networks, extreme value statistics, machine learning, and space-time data mining, have demonstrated significant promise to improve scientific understanding and generate enhanced predictions. When combined with conceptual process understanding at multiple spatiotemporal scales and designed to handle massive data, interdisciplinary data science methods and algorithms may complement or supplement physics-based models. Specific examples from the prior literature and our ongoing work suggests how data-guided improvements may be possible, for example, in the context of ocean meteorology, climate oscillators, teleconnections, and atmospheric process understanding, which in turn can improve projections of regional climate, precipitation extremes and tropical cyclones in an useful and interpretable fashion. A community-wide effort is motivated to develop and adapt computational data science tools for translating climate model simulations to information relevant for adaptation and policy, as well as for improving our scientific understanding of climate extremes from both observed and model-simulated data.

Simulating Earthquakes for Science and Society: New Earthquake Visualizations Ideal for Use in Science Communication

NASA Astrophysics Data System (ADS)

de Groot, R. M.; Benthien, M. L.

2006-12-01

The Southern California Earthquake Center (SCEC) has been developing groundbreaking computer modeling capabilities for studying earthquakes. These visualizations were initially shared within the scientific community but have recently have gained visibility via television news coverage in Southern California. These types of visualizations are becoming pervasive in the teaching and learning of concepts related to earth science. Computers have opened up a whole new world for scientists working with large data sets, and students can benefit from the same opportunities (Libarkin &Brick, 2002). Earthquakes are ideal candidates for visualization products: they cannot be predicted, are completed in a matter of seconds, occur deep in the earth, and the time between events can be on a geologic time scale. For example, the southern part of the San Andreas fault has not seen a major earthquake since about 1690, setting the stage for an earthquake as large as magnitude 7.7 -- the "big one." Since no one has experienced such an earthquake, visualizations can help people understand the scale of such an event. Accordingly, SCEC has developed a revolutionary simulation of this earthquake, with breathtaking visualizations that are now being distributed. According to Gordin and Pea (1995), theoretically visualization should make science accessible, provide means for authentic inquiry, and lay the groundwork to understand and critique scientific issues. This presentation will discuss how the new SCEC visualizations and other earthquake imagery achieve these results, how they fit within the context of major themes and study areas in science communication, and how the efficacy of these tools can be improved.

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, Wucherl; Sim, Alex

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Time-Series Forecast Modeling on High-Bandwidth Network Measurements

DOE PAGES

Yoo, Wucherl; Sim, Alex

2016-06-24

With the increasing number of geographically distributed scientific collaborations and the growing sizes of scientific data, it has become challenging for users to achieve the best possible network performance on a shared network. In this paper, we have developed a model to forecast expected bandwidth utilization on high-bandwidth wide area networks. The forecast model can improve the efficiency of the resource utilization and scheduling of data movements on high-bandwidth networks to accommodate ever increasing data volume for large-scale scientific data applications. A univariate time-series forecast model is developed with the Seasonal decomposition of Time series by Loess (STL) and themore » AutoRegressive Integrated Moving Average (ARIMA) on Simple Network Management Protocol (SNMP) path utilization measurement data. Compared with the traditional approach such as Box-Jenkins methodology to train the ARIMA model, our forecast model reduces computation time up to 92.6 %. It also shows resilience against abrupt network usage changes. Finally, our forecast model conducts the large number of multi-step forecast, and the forecast errors are within the mean absolute deviation (MAD) of the monitored measurements.« less

Development of a SaaS application probe to the physical properties of the Earth's interior: An attempt at moving HPC to the cloud

NASA Astrophysics Data System (ADS)

Huang, Qian

2014-09-01

Scientific computing often requires the availability of a massive number of computers for performing large-scale simulations, and computing in mineral physics is no exception. In order to investigate physical properties of minerals at extreme conditions in computational mineral physics, parallel computing technology is used to speed up the performance by utilizing multiple computer resources to process a computational task simultaneously thereby greatly reducing computation time. Traditionally, parallel computing has been addressed by using High Performance Computing (HPC) solutions and installed facilities such as clusters and super computers. Today, it has been seen that there is a tremendous growth in cloud computing. Infrastructure as a Service (IaaS), the on-demand and pay-as-you-go model, creates a flexible and cost-effective mean to access computing resources. In this paper, a feasibility report of HPC on a cloud infrastructure is presented. It is found that current cloud services in IaaS layer still need to improve performance to be useful to research projects. On the other hand, Software as a Service (SaaS), another type of cloud computing, is introduced into an HPC system for computing in mineral physics, and an application of which is developed. In this paper, an overall description of this SaaS application is presented. This contribution can promote cloud application development in computational mineral physics, and cross-disciplinary studies.

RIACS/USRA

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.

IDEAL: Images Across Domains, Experiments, Algorithms and Learning

NASA Astrophysics Data System (ADS)

Ushizima, Daniela M.; Bale, Hrishikesh A.; Bethel, E. Wes; Ercius, Peter; Helms, Brett A.; Krishnan, Harinarayan; Grinberg, Lea T.; Haranczyk, Maciej; Macdowell, Alastair A.; Odziomek, Katarzyna; Parkinson, Dilworth Y.; Perciano, Talita; Ritchie, Robert O.; Yang, Chao

2016-11-01

Research across science domains is increasingly reliant on image-centric data. Software tools are in high demand to uncover relevant, but hidden, information in digital images, such as those coming from faster next generation high-throughput imaging platforms. The challenge is to analyze the data torrent generated by the advanced instruments efficiently, and provide insights such as measurements for decision-making. In this paper, we overview work performed by an interdisciplinary team of computational and materials scientists, aimed at designing software applications and coordinating research efforts connecting (1) emerging algorithms for dealing with large and complex datasets; (2) data analysis methods with emphasis in pattern recognition and machine learning; and (3) advances in evolving computer architectures. Engineering tools around these efforts accelerate the analyses of image-based recordings, improve reusability and reproducibility, scale scientific procedures by reducing time between experiments, increase efficiency, and open opportunities for more users of the imaging facilities. This paper describes our algorithms and software tools, showing results across image scales, demonstrating how our framework plays a role in improving image understanding for quality control of existent materials and discovery of new compounds.

Final Project Report. Scalable fault tolerance runtime technology for petascale computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnamoorthy, Sriram; Sadayappan, P

With the massive number of components comprising the forthcoming petascale computer systems, hardware failures will be routinely encountered during execution of large-scale applications. Due to the multidisciplinary, multiresolution, and multiscale nature of scientific problems that drive the demand for high end systems, applications place increasingly differing demands on the system resources: disk, network, memory, and CPU. In addition to MPI, future applications are expected to use advanced programming models such as those developed under the DARPA HPCS program as well as existing global address space programming models such as Global Arrays, UPC, and Co-Array Fortran. While there has been amore » considerable amount of work in fault tolerant MPI with a number of strategies and extensions for fault tolerance proposed, virtually none of advanced models proposed for emerging petascale systems is currently fault aware. To achieve fault tolerance, development of underlying runtime and OS technologies able to scale to petascale level is needed. This project has evaluated range of runtime techniques for fault tolerance for advanced programming models.« less

The emergence of spatial cyberinfrastructure.

PubMed

Wright, Dawn J; Wang, Shaowen

2011-04-05

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.

The emergence of spatial cyberinfrastructure

PubMed Central

Wright, Dawn J.; Wang, Shaowen

2011-01-01

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

The MPO system for automatic workflow documentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abla, G.; Coviello, E. N.; Flanagan, S. M.

Data from large-scale experiments and extreme-scale computing is expensive to produce and may be used for critical applications. However, it is not the mere existence of data that is important, but our ability to make use of it. Experience has shown that when metadata is better organized and more complete, the underlying data becomes more useful. Traditionally, capturing the steps of scientific workflows and metadata was the role of the lab notebook, but the digital era has resulted instead in the fragmentation of data, processing, and annotation. Here, this article presents the Metadata, Provenance, and Ontology (MPO) System, the softwaremore » that can automate the documentation of scientific workflows and associated information. Based on recorded metadata, it provides explicit information about the relationships among the elements of workflows in notebook form augmented with directed acyclic graphs. A set of web-based graphical navigation tools and Application Programming Interface (API) have been created for searching and browsing, as well as programmatically accessing the workflows and data. We describe the MPO concepts and its software architecture. We also report the current status of the software as well as the initial deployment experience.« less

The MPO system for automatic workflow documentation

DOE PAGES

Abla, G.; Coviello, E. N.; Flanagan, S. M.; ...

2016-04-18

Data from large-scale experiments and extreme-scale computing is expensive to produce and may be used for critical applications. However, it is not the mere existence of data that is important, but our ability to make use of it. Experience has shown that when metadata is better organized and more complete, the underlying data becomes more useful. Traditionally, capturing the steps of scientific workflows and metadata was the role of the lab notebook, but the digital era has resulted instead in the fragmentation of data, processing, and annotation. Here, this article presents the Metadata, Provenance, and Ontology (MPO) System, the softwaremore » that can automate the documentation of scientific workflows and associated information. Based on recorded metadata, it provides explicit information about the relationships among the elements of workflows in notebook form augmented with directed acyclic graphs. A set of web-based graphical navigation tools and Application Programming Interface (API) have been created for searching and browsing, as well as programmatically accessing the workflows and data. We describe the MPO concepts and its software architecture. We also report the current status of the software as well as the initial deployment experience.« less

Mountain hydrology, snow color, and the fourth paradigm

NASA Astrophysics Data System (ADS)

Dozier, Jeff

2011-10-01

The world's mountain ranges accumulate substantial snow, whose melt produces the bulk of runoff and often combines with rain to cause floods. Worldwide, inadequate understanding and a reliance on sparsely distributed observations limit our ability to predict seasonal and paroxysmal runoff as climate changes, ecosystems adapt, populations grow, land use evolves, and societies make choices. To improve assessments of snow accumulation, melt, and runoff, scientists and community planners can take advantage of two emerging trends: (1) an ability to remotely sense snow properties from satellites at a spatial scale appropriate for mountain regions (10- to 100-meter resolution, coverage of the order of 100,000 square kilometers) and a daily temporal scale appropriate for the dynamic nature of snow and (2) The Fourth Paradigm [Hey et al., 2009], which posits a new scientific approach in which insight is discovered through the manipulation of large data sets as the evolutionary step in scientific thinking beyond the first three paradigms: empiricism, analyses, and simulation. The inspiration for the book's title comes from pioneering computer scientist Jim Gray, based on a lecture he gave at the National Academy of Sciences 3 weeks before he disappeared at sea.

An Asynchronous Many-Task Implementation of In-Situ Statistical Analysis using Legion.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pebay, Philippe Pierre; Bennett, Janine Camille

2015-11-01

In this report, we propose a framework for the design and implementation of in-situ analy- ses using an asynchronous many-task (AMT) model, using the Legion programming model together with the MiniAero mini-application as a surrogate for full-scale parallel scientific computing applications. The bulk of this work consists of converting the Learn/Derive/Assess model which we had initially developed for parallel statistical analysis using MPI [PTBM11], from a SPMD to an AMT model. In this goal, we propose an original use of the concept of Legion logical regions as a replacement for the parallel communication schemes used for the only operation ofmore » the statistics engines that require explicit communication. We then evaluate this proposed scheme in a shared memory environment, using the Legion port of MiniAero as a proxy for a full-scale scientific application, as a means to provide input data sets of variable size for the in-situ statistical analyses in an AMT context. We demonstrate in particular that the approach has merit, and warrants further investigation, in collaboration with ongoing efforts to improve the overall parallel performance of the Legion system.« less

Adsorption Behavior, Thermodynamics, and Kinetics of the Methanol Decomposition Reaction on defective graphene-supported Pt13

NASA Astrophysics Data System (ADS)

Gasper, Raymond; Ramasubramaniam, Ashwin

Defective graphene has been shown experimentally to be an excellent support for transition-metal electrocatalysts in direct methanol fuel cells. Prior computational modeling has shown that the improved catalytic activity of graphene-supported metal clusters is in part due to increased resistance to catalyst sintering and CO poisoning, but the increased reaction rate for the methanol decomposition reaction (MDR) is not yet fully explained. Using DFT, we investigate the adsorption of MDR intermediates and reaction thermodynamics on defective graphene-supported Pt13 nanoclusters with realistic, low-symmetry morphologies. We find that the support-induced shifts in Pt13 electronic structure correlate well with a rigid shift in adsorption of MDR intermediates, and that adsorption energy scaling relationships perform well on the low-symmetry surface. We investigate the reaction kinetics and thermodynamics, including testing the effectiveness of scaling relationships for predicting reaction barriers on the nanoclusters. Using these fundamental data, we perform microkinetic modeling to quantify the effect of the support on the MDR, and to understand how the support influences surface coverages, CO poisoning, and the relationships between reaction pathways. Funded by U.S. Department of Energy under Award Number DE-SC0010610. Computational resources were provided by National Energy Research Scientific Computing Center.

Mathematical Analysis of Vehicle Delivery Scale of Bike-Sharing Rental Nodes

NASA Astrophysics Data System (ADS)

Zhai, Y.; Liu, J.; Liu, L.

2018-04-01

Aiming at the lack of scientific and reasonable judgment of vehicles delivery scale and insufficient optimization of scheduling decision, based on features of the bike-sharing usage, this paper analyses the applicability of the discrete time and state of the Markov chain, and proves its properties to be irreducible, aperiodic and positive recurrent. Based on above analysis, the paper has reached to the conclusion that limit state (steady state) probability of the bike-sharing Markov chain only exists and is independent of the initial probability distribution. Then this paper analyses the difficulty of the transition probability matrix parameter statistics and the linear equations group solution in the traditional solving algorithm of the bike-sharing Markov chain. In order to improve the feasibility, this paper proposes a "virtual two-node vehicle scale solution" algorithm which considered the all the nodes beside the node to be solved as a virtual node, offered the transition probability matrix, steady state linear equations group and the computational methods related to the steady state scale, steady state arrival time and scheduling decision of the node to be solved. Finally, the paper evaluates the rationality and accuracy of the steady state probability of the proposed algorithm by comparing with the traditional algorithm. By solving the steady state scale of the nodes one by one, the proposed algorithm is proved to have strong feasibility because it lowers the level of computational difficulty and reduces the number of statistic, which will help the bike-sharing companies to optimize the scale and scheduling of nodes.

Spatial characterization of the meltwater field from icebergs in the Weddell Sea.

PubMed

Helly, John J; Kaufmann, Ronald S; Vernet, Maria; Stephenson, Gordon R

2011-04-05

We describe the results from a spatial cyberinfrastructure developed to characterize the meltwater field around individual icebergs and integrate the results with regional- and global-scale data. During the course of the cyberinfrastructure development, it became clear that we were also building an integrated sampling planning capability across multidisciplinary teams that provided greater agility in allocating expedition resources resulting in new scientific insights. The cyberinfrastructure-enabled method is a complement to the conventional methods of hydrographic sampling in which the ship provides a static platform on a station-by-station basis. We adapted a sea-floor mapping method to more rapidly characterize the sea surface geophysically and biologically. By jointly analyzing the multisource, continuously sampled biological, chemical, and physical parameters, using Global Positioning System time as the data fusion key, this surface-mapping method enables us to examine the relationship between the meltwater field of the iceberg to the larger-scale marine ecosystem of the Southern Ocean. Through geospatial data fusion, we are able to combine very fine-scale maps of dynamic processes with more synoptic but lower-resolution data from satellite systems. Our results illustrate the importance of spatial cyberinfrastructure in the overall scientific enterprise and identify key interfaces and sources of error that require improved controls for the development of future Earth observing systems as we move into an era of peta- and exascale, data-intensive computing.

Spatial characterization of the meltwater field from icebergs in the Weddell Sea

PubMed Central

Helly, John J.; Kaufmann, Ronald S.; Vernet, Maria; Stephenson, Gordon R.

2011-01-01

We describe the results from a spatial cyberinfrastructure developed to characterize the meltwater field around individual icebergs and integrate the results with regional- and global-scale data. During the course of the cyberinfrastructure development, it became clear that we were also building an integrated sampling planning capability across multidisciplinary teams that provided greater agility in allocating expedition resources resulting in new scientific insights. The cyberinfrastructure-enabled method is a complement to the conventional methods of hydrographic sampling in which the ship provides a static platform on a station-by-station basis. We adapted a sea-floor mapping method to more rapidly characterize the sea surface geophysically and biologically. By jointly analyzing the multisource, continuously sampled biological, chemical, and physical parameters, using Global Positioning System time as the data fusion key, this surface-mapping method enables us to examine the relationship between the meltwater field of the iceberg to the larger-scale marine ecosystem of the Southern Ocean. Through geospatial data fusion, we are able to combine very fine-scale maps of dynamic processes with more synoptic but lower-resolution data from satellite systems. Our results illustrate the importance of spatial cyberinfrastructure in the overall scientific enterprise and identify key interfaces and sources of error that require improved controls for the development of future Earth observing systems as we move into an era of peta- and exascale, data-intensive computing. PMID:21444769

Moving image analysis to the cloud: A case study with a genome-scale tomographic study

NASA Astrophysics Data System (ADS)

Mader, Kevin; Stampanoni, Marco

2016-01-01

Over the last decade, the time required to measure a terabyte of microscopic imaging data has gone from years to minutes. This shift has moved many of the challenges away from experimental design and measurement to scalable storage, organization, and analysis. As many scientists and scientific institutions lack training and competencies in these areas, major bottlenecks have arisen and led to substantial delays and gaps between measurement, understanding, and dissemination. We present in this paper a framework for analyzing large 3D datasets using cloud-based computational and storage resources. We demonstrate its applicability by showing the setup and costs associated with the analysis of a genome-scale study of bone microstructure. We then evaluate the relative advantages and disadvantages associated with local versus cloud infrastructures.

Evolution of the Virtualized HPC Infrastructure of Novosibirsk Scientific Center

NASA Astrophysics Data System (ADS)

Adakin, A.; Anisenkov, A.; Belov, S.; Chubarov, D.; Kalyuzhny, V.; Kaplin, V.; Korol, A.; Kuchin, N.; Lomakin, S.; Nikultsev, V.; Skovpen, K.; Sukharev, A.; Zaytsev, A.

2012-12-01

Novosibirsk Scientific Center (NSC), also known worldwide as Akademgorodok, is one of the largest Russian scientific centers hosting Novosibirsk State University (NSU) and more than 35 research organizations of the Siberian Branch of Russian Academy of Sciences including Budker Institute of Nuclear Physics (BINP), Institute of Computational Technologies, and Institute of Computational Mathematics and Mathematical Geophysics (ICM&MG). Since each institute has specific requirements on the architecture of computing farms involved in its research field, currently we've got several computing facilities hosted by NSC institutes, each optimized for a particular set of tasks, of which the largest are the NSU Supercomputer Center, Siberian Supercomputer Center (ICM&MG), and a Grid Computing Facility of BINP. A dedicated optical network with the initial bandwidth of 10 Gb/s connecting these three facilities was built in order to make it possible to share the computing resources among the research communities, thus increasing the efficiency of operating the existing computing facilities and offering a common platform for building the computing infrastructure for future scientific projects. Unification of the computing infrastructure is achieved by extensive use of virtualization technology based on XEN and KVM platforms. This contribution gives a thorough review of the present status and future development prospects for the NSC virtualized computing infrastructure and the experience gained while using it for running production data analysis jobs related to HEP experiments being carried out at BINP, especially the KEDR detector experiment at the VEPP-4M electron-positron collider.

Addressing capability computing challenges of high-resolution global climate modelling at the Oak Ridge Leadership Computing Facility

NASA Astrophysics Data System (ADS)

Anantharaj, Valentine; Norman, Matthew; Evans, Katherine; Taylor, Mark; Worley, Patrick; Hack, James; Mayer, Benjamin

2014-05-01

During 2013, high-resolution climate model simulations accounted for over 100 million "core hours" using Titan at the Oak Ridge Leadership Computing Facility (OLCF). The suite of climate modeling experiments, primarily using the Community Earth System Model (CESM) at nearly 0.25 degree horizontal resolution, generated over a petabyte of data and nearly 100,000 files, ranging in sizes from 20 MB to over 100 GB. Effective utilization of leadership class resources requires careful planning and preparation. The application software, such as CESM, need to be ported, optimized and benchmarked for the target platform in order to meet the computational readiness requirements. The model configuration needs to be "tuned and balanced" for the experiments. This can be a complicated and resource intensive process, especially for high-resolution configurations using complex physics. The volume of I/O also increases with resolution; and new strategies may be required to manage I/O especially for large checkpoint and restart files that may require more frequent output for resiliency. It is also essential to monitor the application performance during the course of the simulation exercises. Finally, the large volume of data needs to be analyzed to derive the scientific results; and appropriate data and information delivered to the stakeholders. Titan is currently the largest supercomputer available for open science. The computational resources, in terms of "titan core hours" are allocated primarily via the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) and ASCR Leadership Computing Challenge (ALCC) programs, both sponsored by the U.S. Department of Energy (DOE) Office of Science. Titan is a Cray XK7 system, capable of a theoretical peak performance of over 27 PFlop/s, consists of 18,688 compute nodes, with a NVIDIA Kepler K20 GPU and a 16-core AMD Opteron CPU in every node, for a total of 299,008 Opteron cores and 18,688 GPUs offering a cumulative 560,640 equivalent cores. Scientific applications, such as CESM, are also required to demonstrate a "computational readiness capability" to efficiently scale across and utilize 20% of the entire system. The 0,25 deg configuration of the spectral element dynamical core of the Community Atmosphere Model (CAM-SE), the atmospheric component of CESM, has been demonstrated to scale efficiently across more than 5,000 nodes (80,000 CPU cores) on Titan. The tracer transport routines of CAM-SE have also been ported to take advantage of the hybrid many-core architecture of Titan using GPUs [see EGU2014-4233], yielding over 2X speedup when transporting over 100 tracers. The high throughput I/O in CESM, based on the Parallel IO Library (PIO), is being further augmented to support even higher resolutions and enhance resiliency. The application performance of the individual runs are archived in a database and routinely analyzed to identify and rectify performance degradation during the course of the experiments. The various resources available at the OLCF now support a scientific workflow to facilitate high-resolution climate modelling. A high-speed center-wide parallel file system, called ATLAS, capable of 1 TB/s, is available on Titan as well as on the clusters used for analysis (Rhea) and visualization (Lens/EVEREST). Long-term archive is facilitated by the HPSS storage system. The Earth System Grid (ESG), featuring search & discovery, is also used to deliver data. The end-to-end workflow allows OLCF users to efficiently share data and publish results in a timely manner.

Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy

PubMed Central

Ienca, Marcello; Vayena, Effy; Blasimme, Alessandro

2018-01-01

Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy. PMID:29468161

White House announces “big data” initiative

NASA Astrophysics Data System (ADS)

Showstack, Randy

2012-04-01

The world is now generating zetabytes—which is 10 to the 21st power, or a billion trillion bytess—of information every year, according to John Holdren, director of the White House Office of Science and Technology Policy. With data volumes growing exponentially from a variety of sources such as computers running large-scale models, scientific instruments including telescopes and particle accelerators, and even online retail transactions, a key challenge is to better manage and utilize the data. The Big Data Research and Development Initiative, launched by the White House at a 29 March briefing, initially includes six federal departments and agencies providing more than $200 million in new commitments to improve tools and techniques for better accessing, organizing, and using data for scientific advances. The agencies and departments include the National Science Foundation (NSF), Department of Energy, U.S. Geological Survey (USGS), National Institutes of Health (NIH), Department of Defense, and Defense Advanced Research Projects Agency.

Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy.

PubMed

Ienca, Marcello; Vayena, Effy; Blasimme, Alessandro

2018-01-01

Emerging trends in pervasive computing and medical informatics are creating the possibility for large-scale collection, sharing, aggregation and analysis of unprecedented volumes of data, a phenomenon commonly known as big data. In this contribution, we review the existing scientific literature on big data approaches to dementia, as well as commercially available mobile-based applications in this domain. Our analysis suggests that big data approaches to dementia research and care hold promise for improving current preventive and predictive models, casting light on the etiology of the disease, enabling earlier diagnosis, optimizing resource allocation, and delivering more tailored treatments to patients with specific disease trajectories. Such promissory outlook, however, has not materialized yet, and raises a number of technical, scientific, ethical, and regulatory challenges. This paper provides an assessment of these challenges and charts the route ahead for research, ethics, and policy.

Effects of pore-scale physics on uranium geochemistry in Hanford sediments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Qinhong; Ewing, Robert P.

Overall, this work examines a key scientific issue, mass transfer limitations at the pore-scale, using both new instruments with high spatial resolution, and new conceptual and modeling paradigms. The complementary laboratory and numerical approaches connect pore-scale physics to macroscopic measurements, providing a previously elusive scale integration. This Exploratory research project produced five peer-reviewed journal publications and eleven scientific presentations. This work provides new scientific understanding, allowing the DOE to better incorporate coupled physical and chemical processes into decision making for environmental remediation and long-term stewardship.

Position Paper: Applying Machine Learning to Software Analysis to Achieve Trusted, Repeatable Scientific Computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prowell, Stacy J; Symons, Christopher T

2015-01-01

Producing trusted results from high-performance codes is essential for policy and has significant economic impact. We propose combining rigorous analytical methods with machine learning techniques to achieve the goal of repeatable, trustworthy scientific computing.

Advanced Scientific Computing Research Exascale Requirements Review. An Office of Science review sponsored by Advanced Scientific Computing Research, September 27-29, 2016, Rockville, Maryland

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almgren, Ann; DeMar, Phil; Vetter, Jeffrey

The widespread use of computing in the American economy would not be possible without a thoughtful, exploratory research and development (R&D) community pushing the performance edge of operating systems, computer languages, and software libraries. These are the tools and building blocks — the hammers, chisels, bricks, and mortar — of the smartphone, the cloud, and the computing services on which we rely. Engineers and scientists need ever-more specialized computing tools to discover new material properties for manufacturing, make energy generation safer and more efficient, and provide insight into the fundamentals of the universe, for example. The research division of themore » U.S. Department of Energy’s (DOE’s) Office of Advanced Scientific Computing and Research (ASCR Research) ensures that these tools and building blocks are being developed and honed to meet the extreme needs of modern science. See also http://exascaleage.org/ascr/ for additional information.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Ann E; Barker, Ashley D; Bland, Arthur S Buddy

Oak Ridge National Laboratory's Leadership Computing Facility (OLCF) continues to deliver the most powerful resources in the U.S. for open science. At 2.33 petaflops peak performance, the Cray XT Jaguar delivered more than 1.4 billion core hours in calendar year (CY) 2011 to researchers around the world for computational simulations relevant to national and energy security; advancing the frontiers of knowledge in physical sciences and areas of biological, medical, environmental, and computer sciences; and providing world-class research facilities for the nation's science enterprise. Users reported more than 670 publications this year arising from their use of OLCF resources. Of thesemore » we report the 300 in this review that are consistent with guidance provided. Scientific achievements by OLCF users cut across all range scales from atomic to molecular to large-scale structures. At the atomic scale, researchers discovered that the anomalously long half-life of Carbon-14 can be explained by calculating, for the first time, the very complex three-body interactions between all the neutrons and protons in the nucleus. At the molecular scale, researchers combined experimental results from LBL's light source and simulations on Jaguar to discover how DNA replication continues past a damaged site so a mutation can be repaired later. Other researchers combined experimental results from ORNL's Spallation Neutron Source and simulations on Jaguar to reveal the molecular structure of ligno-cellulosic material used in bioethanol production. This year, Jaguar has been used to do billion-cell CFD calculations to develop shock wave compression turbo machinery as a means to meet DOE goals for reducing carbon sequestration costs. General Electric used Jaguar to calculate the unsteady flow through turbo machinery to learn what efficiencies the traditional steady flow assumption is hiding from designers. Even a 1% improvement in turbine design can save the nation billions of gallons of fuel.« less

Quantum Testbeds Stakeholder Workshop (QTSW) Report meeting purpose and agenda.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hebner, Gregory A.

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

A Computing Environment to Support Repeatable Scientific Big Data Experimentation of World-Wide Scientific Literature

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schlicher, Bob G; Kulesz, James J; Abercrombie, Robert K

A principal tenant of the scientific method is that experiments must be repeatable and relies on ceteris paribus (i.e., all other things being equal). As a scientific community, involved in data sciences, we must investigate ways to establish an environment where experiments can be repeated. We can no longer allude to where the data comes from, we must add rigor to the data collection and management process from which our analysis is conducted. This paper describes a computing environment to support repeatable scientific big data experimentation of world-wide scientific literature, and recommends a system that is housed at the Oakmore » Ridge National Laboratory in order to provide value to investigators from government agencies, academic institutions, and industry entities. The described computing environment also adheres to the recently instituted digital data management plan mandated by multiple US government agencies, which involves all stages of the digital data life cycle including capture, analysis, sharing, and preservation. It particularly focuses on the sharing and preservation of digital research data. The details of this computing environment are explained within the context of cloud services by the three layer classification of Software as a Service , Platform as a Service , and Infrastructure as a Service .« less

Idle waves in high-performance computing

NASA Astrophysics Data System (ADS)

Markidis, Stefano; Vencels, Juris; Peng, Ivy Bo; Akhmetova, Dana; Laure, Erwin; Henri, Pierre

2015-01-01

The vast majority of parallel scientific applications distributes computation among processes that are in a busy state when computing and in an idle state when waiting for information from other processes. We identify the propagation of idle waves through processes in scientific applications with a local information exchange between the two processes. Idle waves are nondispersive and have a phase velocity inversely proportional to the average busy time. The physical mechanism enabling the propagation of idle waves is the local synchronization between two processes due to remote data dependency. This study provides a description of the large number of processes in parallel scientific applications as a continuous medium. This work also is a step towards an understanding of how localized idle periods can affect remote processes, leading to the degradation of global performance in parallel scientific applications.

Next Generation Workload Management System For Big Data on Heterogeneous Distributed Computing

NASA Astrophysics Data System (ADS)

Klimentov, A.; Buncic, P.; De, K.; Jha, S.; Maeno, T.; Mount, R.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Petrosyan, A.; Porter, R. J.; Read, K. F.; Vaniachine, A.; Wells, J. C.; Wenaus, T.

2015-05-01

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS and ALICE are the largest collaborations ever assembled in the sciences and are at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, both experiments rely on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System (WMS) for managing the workflow for all data processing on hundreds of data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. The scale is demonstrated by the following numbers: PanDA manages O(102) sites, O(105) cores, O(108) jobs per year, O(103) users, and ATLAS data volume is O(1017) bytes. In 2013 we started an ambitious program to expand PanDA to all available computing resources, including opportunistic use of commercial and academic clouds and Leadership Computing Facilities (LCF). The project titled ‘Next Generation Workload Management and Analysis System for Big Data’ (BigPanDA) is funded by DOE ASCR and HEP. Extending PanDA to clouds and LCF presents new challenges in managing heterogeneity and supporting workflow. The BigPanDA project is underway to setup and tailor PanDA at the Oak Ridge Leadership Computing Facility (OLCF) and at the National Research Center "Kurchatov Institute" together with ALICE distributed computing and ORNL computing professionals. Our approach to integration of HPC platforms at the OLCF and elsewhere is to reuse, as much as possible, existing components of the PanDA system. We will present our current accomplishments with running the PanDA WMS at OLCF and other supercomputers and demonstrate our ability to use PanDA as a portal independent of the computing facilities infrastructure for High Energy and Nuclear Physics as well as other data-intensive science applications.

High Performance Distributed Computing in a Supercomputer Environment: Computational Services and Applications Issues

NASA Technical Reports Server (NTRS)

Kramer, Williams T. C.; Simon, Horst D.

1994-01-01

This tutorial proposes to be a practical guide for the uninitiated to the main topics and themes of high-performance computing (HPC), with particular emphasis to distributed computing. The intent is first to provide some guidance and directions in the rapidly increasing field of scientific computing using both massively parallel and traditional supercomputers. Because of their considerable potential computational power, loosely or tightly coupled clusters of workstations are increasingly considered as a third alternative to both the more conventional supercomputers based on a small number of powerful vector processors, as well as high massively parallel processors. Even though many research issues concerning the effective use of workstation clusters and their integration into a large scale production facility are still unresolved, such clusters are already used for production computing. In this tutorial we will utilize the unique experience made at the NAS facility at NASA Ames Research Center. Over the last five years at NAS massively parallel supercomputers such as the Connection Machines CM-2 and CM-5 from Thinking Machines Corporation and the iPSC/860 (Touchstone Gamma Machine) and Paragon Machines from Intel were used in a production supercomputer center alongside with traditional vector supercomputers such as the Cray Y-MP and C90.

MODIS algorithm development and data visualization using ACTS

NASA Technical Reports Server (NTRS)

Abbott, Mark R.

1992-01-01

The study of the Earth as a system will require the merger of scientific and data resources on a much larger scale than has been done in the past. New methods of scientific research, particularly in the development of geographically dispersed, interdisciplinary teams, are necessary if we are to understand the complexity of the Earth system. Even the planned satellite missions themselves, such as the Earth Observing System, will require much more interaction between researchers and engineers if they are to produce scientifically useful data products. A key component in these activities is the development of flexible, high bandwidth data networks that can be used to move large amounts of data as well as allow researchers to communicate in new ways, such as through video. The capabilities of the Advanced Communications Technology Satellite (ACTS) will allow the development of such networks. The Pathfinder global AVHRR data set and the upcoming SeaWiFS Earthprobe mission would serve as a testbed in which to develop the tools to share data and information among geographically distributed researchers. Our goal is to develop a 'Distributed Research Environment' that can be used as a model for scientific collaboration in the EOS era. The challenge is to unite the advances in telecommunications with the parallel advances in computing and networking.

Large-scale, high-performance and cloud-enabled multi-model analytics experiments in the context of the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Fiore, S.; Płóciennik, M.; Doutriaux, C.; Blanquer, I.; Barbera, R.; Williams, D. N.; Anantharaj, V. G.; Evans, B. J. K.; Salomoni, D.; Aloisio, G.

2017-12-01

The increased models resolution in the development of comprehensive Earth System Models is rapidly leading to very large climate simulations output that pose significant scientific data management challenges in terms of data sharing, processing, analysis, visualization, preservation, curation, and archiving.Large scale global experiments for Climate Model Intercomparison Projects (CMIP) have led to the development of the Earth System Grid Federation (ESGF), a federated data infrastructure which has been serving the CMIP5 experiment, providing access to 2PB of data for the IPCC Assessment Reports. In such a context, running a multi-model data analysis experiment is very challenging, as it requires the availability of a large amount of data related to multiple climate models simulations and scientific data management tools for large-scale data analytics. To address these challenges, a case study on climate models intercomparison data analysis has been defined and implemented in the context of the EU H2020 INDIGO-DataCloud project. The case study has been tested and validated on CMIP5 datasets, in the context of a large scale, international testbed involving several ESGF sites (LLNL, ORNL and CMCC), one orchestrator site (PSNC) and one more hosting INDIGO PaaS services (UPV). Additional ESGF sites, such as NCI (Australia) and a couple more in Europe, are also joining the testbed. The added value of the proposed solution is summarized in the following: it implements a server-side paradigm which limits data movement; it relies on a High-Performance Data Analytics (HPDA) stack to address performance; it exploits the INDIGO PaaS layer to support flexible, dynamic and automated deployment of software components; it provides user-friendly web access based on the INDIGO Future Gateway; and finally it integrates, complements and extends the support currently available through ESGF. Overall it provides a new "tool" for climate scientists to run multi-model experiments. At the time this contribution is being written, the proposed testbed represents the first implementation of a distributed large-scale, multi-model experiment in the ESGF/CMIP context, joining together server-side approaches for scientific data analysis, HPDA frameworks, end-to-end workflow management, and cloud computing.

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

NASA Astrophysics Data System (ADS)

Michalak, Anna M.; Randazzo, Nina A.; Chevallier, Frédéric

2017-06-01

The ability to predict the trajectory of climate change requires a clear understanding of the emissions and uptake (i.e., surface fluxes) of long-lived greenhouse gases (GHGs). Furthermore, the development of climate policies is driving a need to constrain the budgets of anthropogenic GHG emissions. Inverse problems that couple atmospheric observations of GHG concentrations with an atmospheric chemistry and transport model have increasingly been used to gain insights into surface fluxes. Given the inherent technical challenges associated with their solution, it is imperative that objective approaches exist for the evaluation of such inverse problems. Because direct observation of fluxes at compatible spatiotemporal scales is rarely possible, diagnostics tools must rely on indirect measures. Here we review diagnostics that have been implemented in recent studies and discuss their use in informing adjustments to model setup. We group the diagnostics along a continuum starting with those that are most closely related to the scientific question being targeted, and ending with those most closely tied to the statistical and computational setup of the inversion. We thus begin with diagnostics based on assessments against independent information (e.g., unused atmospheric observations, large-scale scientific constraints), followed by statistical diagnostics of inversion results, diagnostics based on sensitivity tests, and analyses of robustness (e.g., tests focusing on the chemistry and transport model, the atmospheric observations, or the statistical and computational framework), and close with the use of synthetic data experiments (i.e., observing system simulation experiments, OSSEs). We find that existing diagnostics provide a crucial toolbox for evaluating and improving flux estimates but, not surprisingly, cannot overcome the fundamental challenges associated with limited atmospheric observations or the lack of direct flux measurements at compatible scales. As atmospheric inversions are increasingly expected to contribute to national reporting of GHG emissions, the need for developing and implementing robust and transparent evaluation approaches will only grow.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Heroux, Michael; Lethin, Richard

Programming models and environments play the essential roles in high performance computing of enabling the conception, design, implementation and execution of science and engineering application codes. Programmer productivity is strongly influenced by the effectiveness of our programming models and environments, as is software sustainability since our codes have lifespans measured in decades, so the advent of new computing architectures, increased concurrency, concerns for resilience, and the increasing demands for high-fidelity, multi-physics, multi-scale and data-intensive computations mean that we have new challenges to address as part of our fundamental R&D requirements. Fortunately, we also have new tools and environments that makemore » design, prototyping and delivery of new programming models easier than ever. The combination of new and challenging requirements and new, powerful toolsets enables significant synergies for the next generation of programming models and environments R&D. This report presents the topics discussed and results from the 2014 DOE Office of Science Advanced Scientific Computing Research (ASCR) Programming Models & Environments Summit, and subsequent discussions among the summit participants and contributors to topics in this report.« less

Climate Analytics as a Service. Chapter 11

NASA Technical Reports Server (NTRS)

Schnase, John L.

2016-01-01

Exascale computing, big data, and cloud computing are driving the evolution of large-scale information systems toward a model of data-proximal analysis. In response, we are developing a concept of climate analytics as a service (CAaaS) that represents a convergence of data analytics and archive management. With this approach, high-performance compute-storage implemented as an analytic system is part of a dynamic archive comprising both static and computationally realized objects. It is a system whose capabilities are framed as behaviors over a static data collection, but where queries cause results to be created, not found and retrieved. Those results can be the product of a complex analysis, but, importantly, they also can be tailored responses to the simplest of requests. NASA's MERRA Analytic Service and associated Climate Data Services API provide a real-world example of climate analytics delivered as a service in this way. Our experiences reveal several advantages to this approach, not the least of which is orders-of-magnitude time reduction in the data assembly task common to many scientific workflows.

Workflow based framework for life science informatics.

PubMed

Tiwari, Abhishek; Sekhar, Arvind K T

2007-10-01

Workflow technology is a generic mechanism to integrate diverse types of available resources (databases, servers, software applications and different services) which facilitate knowledge exchange within traditionally divergent fields such as molecular biology, clinical research, computational science, physics, chemistry and statistics. Researchers can easily incorporate and access diverse, distributed tools and data to develop their own research protocols for scientific analysis. Application of workflow technology has been reported in areas like drug discovery, genomics, large-scale gene expression analysis, proteomics, and system biology. In this article, we have discussed the existing workflow systems and the trends in applications of workflow based systems.

Comparisons of some large scientific computers

NASA Technical Reports Server (NTRS)

Credeur, K. R.

1981-01-01

In 1975, the National Aeronautics and Space Administration (NASA) began studies to assess the technical and economic feasibility of developing a computer having sustained computational speed of one billion floating point operations per second and a working memory of at least 240 million words. Such a powerful computer would allow computational aerodynamics to play a major role in aeronautical design and advanced fluid dynamics research. Based on favorable results from these studies, NASA proceeded with developmental plans. The computer was named the Numerical Aerodynamic Simulator (NAS). To help insure that the estimated cost, schedule, and technical scope were realistic, a brief study was made of past large scientific computers. Large discrepancies between inception and operation in scope, cost, or schedule were studied so that they could be minimized with NASA's proposed new compter. The main computers studied were the ILLIAC IV, STAR 100, Parallel Element Processor Ensemble (PEPE), and Shuttle Mission Simulator (SMS) computer. Comparison data on memory and speed were also obtained on the IBM 650, 704, 7090, 360-50, 360-67, 360-91, and 370-195; the CDC 6400, 6600, 7600, CYBER 203, and CYBER 205; CRAY 1; and the Advanced Scientific Computer (ASC). A few lessons learned conclude the report.

Parallel Multivariate Spatio-Temporal Clustering of Large Ecological Datasets on Hybrid Supercomputers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sreepathi, Sarat; Kumar, Jitendra; Mills, Richard T.

A proliferation of data from vast networks of remote sensing platforms (satellites, unmanned aircraft systems (UAS), airborne etc.), observational facilities (meteorological, eddy covariance etc.), state-of-the-art sensors, and simulation models offer unprecedented opportunities for scientific discovery. Unsupervised classification is a widely applied data mining approach to derive insights from such data. However, classification of very large data sets is a complex computational problem that requires efficient numerical algorithms and implementations on high performance computing (HPC) platforms. Additionally, increasing power, space, cooling and efficiency requirements has led to the deployment of hybrid supercomputing platforms with complex architectures and memory hierarchies like themore » Titan system at Oak Ridge National Laboratory. The advent of such accelerated computing architectures offers new challenges and opportunities for big data analytics in general and specifically, large scale cluster analysis in our case. Although there is an existing body of work on parallel cluster analysis, those approaches do not fully meet the needs imposed by the nature and size of our large data sets. Moreover, they had scaling limitations and were mostly limited to traditional distributed memory computing platforms. We present a parallel Multivariate Spatio-Temporal Clustering (MSTC) technique based on k-means cluster analysis that can target hybrid supercomputers like Titan. We developed a hybrid MPI, CUDA and OpenACC implementation that can utilize both CPU and GPU resources on computational nodes. We describe performance results on Titan that demonstrate the scalability and efficacy of our approach in processing large ecological data sets.« less

USSR Report: Cybernetics, Computers and Automation Technology. No. 69.

DTIC Science & Technology

1983-05-06

computers in multiprocessor and multistation design , control and scientific research automation systems. The results of comparing the efficiency of...Podvizhnaya, Scientific Research Institute of Control Computers, Severodonetsk] [Text] The most significant change in the design of the SM-2M compared to...UPRAVLYAYUSHCHIYE SISTEMY I MASHINY, Nov-Dec 82) 95 APPLICATIONS Kiev Automated Control System, Design Features and Prospects for Development (V. A

Advanced Simulation and Computing Fiscal Year 14 Implementation Plan, Rev. 0.5

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meisner, Robert; McCoy, Michel; Archer, Bill

2013-09-11

The Stockpile Stewardship Program (SSP) is a single, highly integrated technical program for maintaining the surety and reliability of the U.S. nuclear stockpile. The SSP uses nuclear test data, computational modeling and simulation, and experimental facilities to advance understanding of nuclear weapons. It includes stockpile surveillance, experimental research, development and engineering programs, and an appropriately scaled production capability to support stockpile requirements. This integrated national program requires the continued use of experimental facilities and programs, and the computational enhancements to support these programs. The Advanced Simulation and Computing Program (ASC) is a cornerstone of the SSP, providing simulation capabilities andmore » computational resources that support annual stockpile assessment and certification, study advanced nuclear weapons design and manufacturing processes, analyze accident scenarios and weapons aging, and provide the tools to enable stockpile Life Extension Programs (LEPs) and the resolution of Significant Finding Investigations (SFIs). This requires a balanced resource, including technical staff, hardware, simulation software, and computer science solutions. In its first decade, the ASC strategy focused on demonstrating simulation capabilities of unprecedented scale in three spatial dimensions. In its second decade, ASC is now focused on increasing predictive capabilities in a three-dimensional (3D) simulation environment while maintaining support to the SSP. The program continues to improve its unique tools for solving progressively more difficult stockpile problems (sufficient resolution, dimensionality, and scientific details), quantify critical margins and uncertainties, and resolve increasingly difficult analyses needed for the SSP. Moreover, ASC’s business model is integrated and focused on requirements-driven products that address long-standing technical questions related to enhanced predictive capability in the simulation tools.« less

Computational Materials Science and Chemistry: Accelerating Discovery and Innovation through Simulation-Based Engineering and Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Crabtree, George; Glotzer, Sharon; McCurdy, Bill

This report is based on a SC Workshop on Computational Materials Science and Chemistry for Innovation on July 26-27, 2010, to assess the potential of state-of-the-art computer simulations to accelerate understanding and discovery in materials science and chemistry, with a focus on potential impacts in energy technologies and innovation. The urgent demand for new energy technologies has greatly exceeded the capabilities of today's materials and chemical processes. To convert sunlight to fuel, efficiently store energy, or enable a new generation of energy production and utilization technologies requires the development of new materials and processes of unprecedented functionality and performance. Newmore » materials and processes are critical pacing elements for progress in advanced energy systems and virtually all industrial technologies. Over the past two decades, the United States has developed and deployed the world's most powerful collection of tools for the synthesis, processing, characterization, and simulation and modeling of materials and chemical systems at the nanoscale, dimensions of a few atoms to a few hundred atoms across. These tools, which include world-leading x-ray and neutron sources, nanoscale science facilities, and high-performance computers, provide an unprecedented view of the atomic-scale structure and dynamics of materials and the molecular-scale basis of chemical processes. For the first time in history, we are able to synthesize, characterize, and model materials and chemical behavior at the length scale where this behavior is controlled. This ability is transformational for the discovery process and, as a result, confers a significant competitive advantage. Perhaps the most spectacular increase in capability has been demonstrated in high performance computing. Over the past decade, computational power has increased by a factor of a million due to advances in hardware and software. This rate of improvement, which shows no sign of abating, has enabled the development of computer simulations and models of unprecedented fidelity. We are at the threshold of a new era where the integrated synthesis, characterization, and modeling of complex materials and chemical processes will transform our ability to understand and design new materials and chemistries with predictive power. In turn, this predictive capability will transform technological innovation by accelerating the development and deployment of new materials and processes in products and manufacturing. Harnessing the potential of computational science and engineering for the discovery and development of materials and chemical processes is essential to maintaining leadership in these foundational fields that underpin energy technologies and industrial competitiveness. Capitalizing on the opportunities presented by simulation-based engineering and science in materials and chemistry will require an integration of experimental capabilities with theoretical and computational modeling; the development of a robust and sustainable infrastructure to support the development and deployment of advanced computational models; and the assembly of a community of scientists and engineers to implement this integration and infrastructure. This community must extend to industry, where incorporating predictive materials science and chemistry into design tools can accelerate the product development cycle and drive economic competitiveness. The confluence of new theories, new materials synthesis capabilities, and new computer platforms has created an unprecedented opportunity to implement a "materials-by-design" paradigm with wide-ranging benefits in technological innovation and scientific discovery. The Workshop on Computational Materials Science and Chemistry for Innovation was convened in Bethesda, Maryland, on July 26-27, 2010. Sponsored by the Department of Energy (DOE) Offices of Advanced Scientific Computing Research and Basic Energy Sciences, the workshop brought together 160 experts in materials science, chemistry, and computational science representing more than 65 universities, laboratories, and industries, and four agencies. The workshop examined seven foundational challenge areas in materials science and chemistry: materials for extreme conditions, self-assembly, light harvesting, chemical reactions, designer fluids, thin films and interfaces, and electronic structure. Each of these challenge areas is critical to the development of advanced energy systems, and each can be accelerated by the integrated application of predictive capability with theory and experiment. The workshop concluded that emerging capabilities in predictive modeling and simulation have the potential to revolutionize the development of new materials and chemical processes. Coupled with world-leading materials characterization and nanoscale science facilities, this predictive capability provides the foundation for an innovation ecosystem that can accelerate the discovery, development, and deployment of new technologies, including advanced energy systems. Delivering on the promise of this innovation ecosystem requires the following: Integration of synthesis, processing, characterization, theory, and simulation and modeling. Many of the newly established Energy Frontier Research Centers and Energy Hubs are exploiting this integration. Achieving/strengthening predictive capability in foundational challenge areas. Predictive capability in the seven foundational challenge areas described in this report is critical to the development of advanced energy technologies. Developing validated computational approaches that span vast differences in time and length scales. This fundamental computational challenge crosscuts all of the foundational challenge areas. Similarly challenging is coupling of analytical data from multiple instruments and techniques that are required to link these length and time scales. Experimental validation and quantification of uncertainty in simulation and modeling. Uncertainty quantification becomes increasingly challenging as simulations become more complex. Robust and sustainable computational infrastructure, including software and applications. For modeling and simulation, software equals infrastructure. To validate the computational tools, software is critical infrastructure that effectively translates huge arrays of experimental data into useful scientific understanding. An integrated approach for managing this infrastructure is essential. Efficient transfer and incorporation of simulation-based engineering and science in industry. Strategies for bridging the gap between research and industrial applications and for widespread industry adoption of integrated computational materials engineering are needed.« less

Global Load Balancing with Parallel Mesh Adaption on Distributed-Memory Systems

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Sohn, Andrew

1996-01-01

Dynamic mesh adaptation on unstructured grids is a powerful tool for efficiently computing unsteady problems to resolve solution features of interest. Unfortunately, this causes load inbalances among processors on a parallel machine. This paper described 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 coast 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 percent of the mesh is randomly adapted. For large scale scientific computations, our load balancing strategy gives an almost sixfold reduction in solver execution times over non-balanced loads. Furthermore, our heuristic remappier yields processor assignments that are less than 3 percent of the optimal solutions, but requires only 1 percent of the computational time.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

PubMed Central

Kadima, Hubert; Granado, Bertrand

2013-01-01

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

A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures

NASA Astrophysics Data System (ADS)

Loring, B.; Karimabadi, H.; Rortershteyn, V.

2015-10-01

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not. We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.

Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

PubMed

Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

2013-01-01

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.

A Screen Space GPGPU Surface LIC Algorithm for Distributed Memory Data Parallel Sort Last Rendering Infrastructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Loring, Burlen; Karimabadi, Homa; Rortershteyn, Vadim

2014-07-01

The surface line integral convolution(LIC) visualization technique produces dense visualization of vector fields on arbitrary surfaces. We present a screen space surface LIC algorithm for use in distributed memory data parallel sort last rendering infrastructures. The motivations for our work are to support analysis of datasets that are too large to fit in the main memory of a single computer and compatibility with prevalent parallel scientific visualization tools such as ParaView and VisIt. By working in screen space using OpenGL we can leverage the computational power of GPUs when they are available and run without them when they are not.more » We address efficiency and performance issues that arise from the transformation of data from physical to screen space by selecting an alternate screen space domain decomposition. We analyze the algorithm's scaling behavior with and without GPUs on two high performance computing systems using data from turbulent plasma simulations.« less

Using Amazon's Elastic Compute Cloud to dynamically scale CMS computational resources

NASA Astrophysics Data System (ADS)

Evans, D.; Fisk, I.; Holzman, B.; Melo, A.; Metson, S.; Pordes, R.; Sheldon, P.; Tiradani, A.

2011-12-01

Large international scientific collaborations such as the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider have traditionally addressed their data reduction and analysis needs by building and maintaining dedicated computational infrastructure. Emerging cloud computing services such as Amazon's Elastic Compute Cloud (EC2) offer short-term CPU and storage resources with costs based on usage. These services allow experiments to purchase computing resources as needed, without significant prior planning and without long term investments in facilities and their management. We have demonstrated that services such as EC2 can successfully be integrated into the production-computing model of CMS, and find that they work very well as worker nodes. The cost-structure and transient nature of EC2 services makes them inappropriate for some CMS production services and functions. We also found that the resources are not truely "on-demand" as limits and caps on usage are imposed. Our trial workflows allow us to make a cost comparison between EC2 resources and dedicated CMS resources at a University, and conclude that it is most cost effective to purchase dedicated resources for the "base-line" needs of experiments such as CMS. However, if the ability to use cloud computing resources is built into an experiment's software framework before demand requires their use, cloud computing resources make sense for bursting during times when spikes in usage are required.

Towards reversible basic linear algebra subprograms: A performance study

DOE PAGES

Perumalla, Kalyan S.; Yoginath, Srikanth B.

2014-12-06

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

Investigating the impact of the cielo cray XE6 architecture on scientific application codes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rajan, Mahesh; Barrett, Richard; Pedretti, Kevin Thomas Tauke

2010-12-01

Cielo, a Cray XE6, is the Department of Energy NNSA Advanced Simulation and Computing (ASC) campaign's newest capability machine. Rated at 1.37 PFLOPS, it consists of 8,944 dual-socket oct-core AMD Magny-Cours compute nodes, linked using Cray's Gemini interconnect. Its primary mission objective is to enable a suite of the ASC applications implemented using MPI to scale to tens of thousands of cores. Cielo is an evolutionary improvement to a successful architecture previously available to many of our codes, thus enabling a basis for understanding the capabilities of this new architecture. Using three codes strategically important to the ASC campaign, andmore » supplemented with some micro-benchmarks that expose the fundamental capabilities of the XE6, we report on the performance characteristics and capabilities of Cielo.« less

Statistical processing of large image sequences.

PubMed

Khellah, F; Fieguth, P; Murray, M J; Allen, M

2005-01-01

The dynamic estimation of large-scale stochastic image sequences, as frequently encountered in remote sensing, is important in a variety of scientific applications. However, the size of such images makes conventional dynamic estimation methods, for example, the Kalman and related filters, impractical. In this paper, we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a 512 x 512 image sequence of ocean surface temperature. The static estimation step, the primary contribution here, uses a mixture of stationary models to accurately mimic the effect of a nonstationary prior, simplifying both computational complexity and modeling. Our approach provides an efficient, stable, positive-definite model which is consistent with the given correlation structure. Thus, the methods of this paper may find application in modeling and single-frame estimation.

What is bioinformatics? A proposed definition and overview of the field.

PubMed

Luscombe, N M; Greenbaum, D; Gerstein, M

2001-01-01

The recent flood of data from genome sequences and functional genomics has given rise to new field, bioinformatics, which combines elements of biology and computer science. Here we propose a definition for this new field and review some of the research that is being pursued, particularly in relation to transcriptional regulatory systems. Our definition is as follows: Bioinformatics is conceptualizing biology in terms of macromolecules (in the sense of physical-chemistry) and then applying "informatics" techniques (derived from disciplines such as applied maths, computer science, and statistics) to understand and organize the information associated with these molecules, on a large-scale. Analyses in bioinformatics predominantly focus on three types of large datasets available in molecular biology: macromolecular structures, genome sequences, and the results of functional genomics experiments (e.g. expression data). Additional information includes the text of scientific papers and "relationship data" from metabolic pathways, taxonomy trees, and protein-protein interaction networks. Bioinformatics employs a wide range of computational techniques including sequence and structural alignment, database design and data mining, macromolecular geometry, phylogenetic tree construction, prediction of protein structure and function, gene finding, and expression data clustering. The emphasis is on approaches integrating a variety of computational methods and heterogeneous data sources. Finally, bioinformatics is a practical discipline. We survey some representative applications, such as finding homologues, designing drugs, and performing large-scale censuses. Additional information pertinent to the review is available over the web at http://bioinfo.mbb.yale.edu/what-is-it.

Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System.

PubMed

Passerat-Palmbach, Jonathan; Reuillon, Romain; Leclaire, Mathieu; Makropoulos, Antonios; Robinson, Emma C; Parisot, Sarah; Rueckert, Daniel

2017-01-01

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI).

Reproducible Large-Scale Neuroimaging Studies with the OpenMOLE Workflow Management System

PubMed Central

Passerat-Palmbach, Jonathan; Reuillon, Romain; Leclaire, Mathieu; Makropoulos, Antonios; Robinson, Emma C.; Parisot, Sarah; Rueckert, Daniel

2017-01-01

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition. The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments. Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behavior of an application, or perform automatic parameter tuning. In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerization solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously. The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstration of the flexibility of our solution showcases three neuroimaging pipelines harnessing distributed computing environments as heterogeneous as local clusters or the European Grid Infrastructure (EGI). PMID:28381997

Performance of the engineering analysis and data system 2 common file system

NASA Technical Reports Server (NTRS)

Debrunner, Linda S.

1993-01-01

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.

RAPPORT: running scientific high-performance computing applications on the cloud.

PubMed

Cohen, Jeremy; Filippis, Ioannis; Woodbridge, Mark; Bauer, Daniela; Hong, Neil Chue; Jackson, Mike; Butcher, Sarah; Colling, David; Darlington, John; Fuchs, Brian; Harvey, Matt

2013-01-28

Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms. We conclude that, while there are many factors that need consideration, there is no fundamental impediment to the use of cloud infrastructure for running many types of HPC applications and, in some cases, there is potential for researchers to benefit significantly from the flexibility offered by cloud platforms.

High-performance scientific computing in the cloud

NASA Astrophysics Data System (ADS)

Jorissen, Kevin; Vila, Fernando; Rehr, John

2011-03-01

Cloud computing has the potential to open up high-performance computational science to a much broader class of researchers, owing to its ability to provide on-demand, virtualized computational resources. However, before such approaches can become commonplace, user-friendly tools must be developed that hide the unfamiliar cloud environment and streamline the management of cloud resources for many scientific applications. We have recently shown that high-performance cloud computing is feasible for parallelized x-ray spectroscopy calculations. We now present benchmark results for a wider selection of scientific applications focusing on electronic structure and spectroscopic simulation software in condensed matter physics. These applications are driven by an improved portable interface that can manage virtual clusters and run various applications in the cloud. We also describe a next generation of cluster tools, aimed at improved performance and a more robust cluster deployment. Supported by NSF grant OCI-1048052.

Preface: SciDAC 2007

NASA Astrophysics Data System (ADS)

Keyes, David E.

2007-09-01

It takes a village to perform a petascale computation—domain scientists, applied mathematicians, computer scientists, computer system vendors, program managers, and support staff—and the village was assembled during 24-28 June 2007 in Boston's Westin Copley Place for the third annual Scientific Discovery through Advanced Computing (SciDAC) 2007 Conference. Over 300 registered participants networked around 76 posters, focused on achievements and challenges in 36 plenary talks, and brainstormed in two panels. In addition, with an eye to spreading the vision for simulation at the petascale and to growing the workforce, 115 participants—mostly doctoral students and post-docs complementary to the conferees—were gathered on 29 June 2007 in classrooms of the Massachusetts Institute of Technology for a full day of tutorials on the use of SciDAC software. Eleven SciDAC-sponsored research groups presented their software at an introductory level, in both lecture and hands-on formats that included live runs on a local BlueGene/L. Computation has always been about garnering insight into the behavior of systems too complex to explore satisfactorily by theoretical means alone. Today, however, computation is about much more: scientists and decision makers expect quantitatively reliable predictions from simulations ranging in scale from that of the Earth's climate, down to quarks, and out to colliding black holes. Predictive simulation lies at the heart of policy choices in energy and environment affecting billions of lives and expenditures of trillions of dollars. It is also at the heart of scientific debates on the nature of matter and the origin of the universe. The petascale is barely adequate for such demands and we are barely established at the levels of resolution and throughput that this new scale of computation affords. However, no scientific agenda worldwide is pushing the petascale frontier on all its fronts as vigorously as SciDAC. The breadth of this conference archive reflects the philosophy of the SciDAC program, which was introduced as a collaboration of all of the program offices in the Office of Science of the U.S. Department of Energy (DOE) in Fall 2001 and was renewed for a second period of five years in Fall 2006, with additional support in certain areas from the DOE's National Nuclear Security Administration (NNSA) and the U.S. National Science Foundation (NSF). All of the projects in the SciDAC portfolio were represented at the conference and most are captured in this volume. In addition, the Organizing Committee incorporated into the technical program a number of computational science highlights from outside of SciDAC, and, indeed, from outside of the United States. As implied by the title, scientific discovery is the driving deliverable of the SciDAC program, spanning the full range of the DOE Office of Science: accelerator design, astrophysics, chemistry and materials science, climate science, combustion, life science, nuclear physics, plasma physics, and subsurface physics. As articulated in the eponymous report that launched SciDAC, the computational challenges of these diverse areas are remarkably common. Each is profoundly multiscale in space and time and therefore continues to benefit at any margin from access to the largest and fastest computers available. Optimality of representation and execution requires adaptive, scalable mathematical algorithms in both continuous (geometrically complex domain) and discrete (mesh and graph) aspects. Programmability and performance optimality require software environments that both manage the intricate details of the underlying hardware and abstract them for scientific users. Running effectively on remote specialized hardware requires transparent workflow systems. Comprehending the petascale data sets generated in such simulations requires automated tools for data exploration and visualization. Archiving and sharing access to this data within the inevitably distributed community of leading scientists requires networked collaborative environments. Each of these elements is a research and development project in its own right. SciDAC does not replace theoretical programs oriented towards long-term basic research, but harvests them for contemporary, complementary state-of-the-art computational campaigns. By clustering researchers from applications and enabling technologies into coordinated, mission-driven projects, SciDAC accomplishes two ends with remarkable effectiveness: (1) it enriches the scientific perspective of both applications and enabling communities through mutual interaction and (2) it leverages between applications solutions and effort encapsulated in software. Though SciDAC is unique, its objective of multiscale science at extreme computational scale is shared and approached through different programmatic mechanisms, notably NNSA's ASC program, NSF's Cyberinfrastructure program, and DoD's CREATE program in the U.S., and RIKEN's computational simulation programs in Japan. Representatives of each of these programs were given the podium at SciDAC 2007 and communication occurred that will be valuable towards the ends of complementarity, leverage, and promulgation of best practices. The 2007 conference was graced with additional welcome program announcements. Michael Strayer announced a new program of postdoctoral research fellowships in the enabling technologies. (The computer science post-docs will be named after the late Professor Ken Kennedy, who briefly led the SciDAC project Center for Scalable Application Development Software (CScADS) until his untimely death in February 2007.) IBM announced its petascale BlueGene/P system on June 26. Meanwhile, at ISC07 in Dresden, the semi-annual posting of a revised Top 500 list on June 27 showed several new Top 10 systems accessible to various SciDAC participants. While SciDAC is dominated in 2007 by the classical scientific pursuit of understanding through reduction to components and isolation of causes and effects, simulation at scale is beginning to offer something even more tantalizing: synthesis and integration of multiple interacting phenomena in complex systems. Indeed, the design-oriented elements of SciDAC, such as accelerator and tokamak modeling, area already emphasizing multiphysics coupling, and climate science has been doing so for years in the coupling of models of the ocean, atmosphere, ice, and land. In one of the panels at SciDAC 2007, leaders of a three-stage `progressive workshop' on exascale simulation for energy and environment (E3), considered prospects for whole-system modeling in a variety of scientific areas within the domain of DOE related to energy, environmental, and global security. Computer vendors were invited to comment on the prospects for delivering exascale computing systems in another panel. The daunting nature of this challenge is summarized with the observation that the peak processing power of the entire Top 500 list of June 2007 is only 0.0052 exaflop/s. It takes the combined power of most of the computers on the internet today worldwide to reach 1 exaflop/s or 1018 floating point operations per second. The program of SciDAC 2007 followed a template honed by its predecessor meetings in San Francisco in 2005 and Denver in 2006. The Boston venue permitted outreach to a number of universities in the immediate region and throughout southern New England, including SciDAC campuses of Boston University, Harvard, and MIT, and a dozen others including most of the Ivy League. Altogether 55 universities, 20 laboratories, 14 private companies, 5 agencies, and 4 countries were represented among the conference and tutorial workshop participants. Approximately 47% of the conference participants were from government laboratories, 37% from universities, 9% from federal program offices, and 7% from industry. Keys to the success of SciDAC 2007 were the informal poster receptions, coffee breaks, working breakfasts and lunches, and even the `Right-brain Night' featuring artistic statements, both reverent and irreverent, by computational scientists, inspired by their work. The organizers thank the sponsors for their generosity in attracting participants to these informal occasions with sumptuous snacks and beverages: AMD, Cray, DataDirect, IBM, SGI, SiCortex, and the Institute of Physics. A conference as logistically complex as SciDAC 2007 cannot possibly and should not be executed primarily by the scientists, themselves. It is a great pleasure to acknowledge the many talented staff that contributed to a productive time for all participants and nearperfect adherence to schedule. Chief among them is Betsy Riley, currently detailed from ORNL to the program office in Germantown, with degrees in mathematics and computer science, but a passion for organizing interdisciplinary scientific programs. Betsy staffed the organizing committee during the year of telecon meetings leading up to the conference and masterminded sponsorship, invitations, and the compilation of the proceedings. Assisting her from ORNL in managing the program were Daniel Pack, Angela Beach, and Angela Fincher. Cynthia Latham of ORNL performed admirably in website and graphic design for all aspects of the online and printed materials of the meeting. John Bui, John Smith, and Missy Smith of ORNL ran their customary tight ship with respect to audio-visual execution and capture, assisted by Eric Ecklund and Keith Quinn of the Westin. Pamelia Nixon-Hartje of Ambassador Services was personally invaluable in getting the most out of the hotel and its staff. We thank Jeff Nichols of ORNL for managing the primary subcontract for the meeting. The SciDAC tutorial program was a joint effort of Professor John Negele of MIT, David Skinner, PI of the SciDAC Outreach Center, and the SciDAC 2007 Chair. Sponsorship from the Outreach Center in the form of travel scholarships for students, and of the local area SciDAC university delegation of BU, Harvard, and MIT for food and facilities is gratefully acknowledged. Of course, the archival success of a scientific meeting rests with the willingness of the presenters to make the extra effort to package their field-leading science in a form suitable for interaction with colleagues from other disciplines rather than fellow specialists. This goal, oft-stated in the run up to the meeting, was achieved to an admirable degree, both in the live presentations and in these proceedings. This effort is its own reward, since it leads to enhanced communication and accelerated scientific progress. Our greatest thanks are reserved for Michael Strayer, Associate Director for OASCR and the Director of SciDAC, for envisioning this celebratory meeting three years ago, and sustaining it with his own enthusiasm, in order to provide a highly visible manifestation of the fruits of SciDAC. He and the other Office of Science program managers in attendance and working in Washington, DC to communicate the opportunities afforded by SciDAC deserve the gratitude of a new virtual scientific village created and cemented under the vision of scientific discovery through advanced computing. David E Keyes Fu Foundation Professor of Applied Mathematics

Activities of the Research Institute for Advanced Computer Science

NASA Technical Reports Server (NTRS)

Oliger, Joseph

1994-01-01

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 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. Research at RIACS is currently being done in the following areas: (1) parallel computing; (2) advanced methods for scientific computing; (3) high performance networks; and (4) learning systems. 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 January 1, 1994 through December 31, 1994 is in the Reports and Abstracts section of this report.

Climate Modeling with a Million CPUs

NASA Astrophysics Data System (ADS)

Tobis, M.; Jackson, C. S.

2010-12-01

Michael Tobis, Ph.D. Research Scientist Associate University of Texas Institute for Geophysics Charles S. Jackson Research Scientist University of Texas Institute for Geophysics Meteorological, oceanographic, and climatological applications have been at the forefront of scientific computing since its inception. The trend toward ever larger and more capable computing installations is unabated. However, much of the increase in capacity is accompanied by an increase in parallelism and a concomitant increase in complexity. An increase of at least four additional orders of magnitude in the computational power of scientific platforms is anticipated. It is unclear how individual climate simulations can continue to make effective use of the largest platforms. Conversion of existing community codes to higher resolution, or to more complex phenomenology, or both, presents daunting design and validation challenges. Our alternative approach is to use the expected resources to run very large ensembles of simulations of modest size, rather than to await the emergence of very large simulations. We are already doing this in exploring the parameter space of existing models using the Multiple Very Fast Simulated Annealing algorithm, which was developed for seismic imaging. Our experiments have the dual intentions of tuning the model and identifying ranges of parameter uncertainty. Our approach is less strongly constrained by the dimensionality of the parameter space than are competing methods. Nevertheless, scaling up remains costly. Much could be achieved by increasing the dimensionality of the search and adding complexity to the search algorithms. Such ensemble approaches scale naturally to very large platforms. Extensions of the approach are anticipated. For example, structurally different models can be tuned to comparable effectiveness. This can provide an objective test for which there is no realistic precedent with smaller computations. We find ourselves inventing new code to manage our ensembles. Component computations involve tens to hundreds of CPUs and tens to hundreds of hours. The results of these moderately large parallel jobs influence the scheduling of subsequent jobs, and complex algorithms may be easily contemplated for this. The operating system concept of a "thread" re-emerges at a very coarse level, where each thread manages atomic computations of thousands of CPU-hours. That is, rather than multiple threads operating on a processor, at this level, multiple processors operate within a single thread. In collaboration with the Texas Advanced Computing Center, we are developing a software library at the system level, which should facilitate the development of computations involving complex strategies which invoke large numbers of moderately large multi-processor jobs. While this may have applications in other sciences, our key intent is to better characterize the coupled behavior of a very large set of climate model configurations.

Neuromorphic Computing, Architectures, Models, and Applications. A Beyond-CMOS Approach to Future Computing, June 29-July 1, 2016, Oak Ridge, TN

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potok, Thomas; Schuman, Catherine; Patton, Robert

The White House and Department of Energy have been instrumental in driving the development of a neuromorphic computing program to help the United States continue its lead in basic research into (1) Beyond Exascale—high performance computing beyond Moore’s Law and von Neumann architectures, (2) Scientific Discovery—new paradigms for understanding increasingly large and complex scientific data, and (3) Emerging Architectures—assessing the potential of neuromorphic and quantum architectures. Neuromorphic computing spans a broad range of scientific disciplines from materials science to devices, to computer science, to neuroscience, all of which are required to solve the neuromorphic computing grand challenge. In our workshopmore » we focus on the computer science aspects, specifically from a neuromorphic device through an application. Neuromorphic devices present a very different paradigm to the computer science community from traditional von Neumann architectures, which raises six major questions about building a neuromorphic application from the device level. We used these fundamental questions to organize the workshop program and to direct the workshop panels and discussions. From the white papers, presentations, panels, and discussions, there emerged several recommendations on how to proceed.« less

Computational science: shifting the focus from tools to models

PubMed Central

Hinsen, Konrad

2014-01-01

Computational techniques have revolutionized many aspects of scientific research over the last few decades. Experimentalists use computation for data analysis, processing ever bigger data sets. Theoreticians compute predictions from ever more complex models. However, traditional articles do not permit the publication of big data sets or complex models. As a consequence, these crucial pieces of information no longer enter the scientific record. Moreover, they have become prisoners of scientific software: many models exist only as software implementations, and the data are often stored in proprietary formats defined by the software. In this article, I argue that this emphasis on software tools over models and data is detrimental to science in the long term, and I propose a means by which this can be reversed. PMID:25309728

Multi-threading: A new dimension to massively parallel scientific computation

NASA Astrophysics Data System (ADS)

Nielsen, Ida M. B.; Janssen, Curtis L.

2000-06-01

Multi-threading is becoming widely available for Unix-like operating systems, and the application of multi-threading opens new ways for performing parallel computations with greater efficiency. We here briefly discuss the principles of multi-threading and illustrate the application of multi-threading for a massively parallel direct four-index transformation of electron repulsion integrals. Finally, other potential applications of multi-threading in scientific computing are outlined.

Toward server-side, high performance climate change data analytics in the Earth System Grid Federation (ESGF) eco-system

NASA Astrophysics Data System (ADS)

Fiore, Sandro; Williams, Dean; Aloisio, Giovanni

2016-04-01

In many scientific domains such as climate, data is often n-dimensional and requires tools that support specialized data types and primitives to be properly stored, accessed, analysed and visualized. Moreover, new challenges arise in large-scale scenarios and eco-systems where petabytes (PB) of data can be available and data can be distributed and/or replicated (e.g., the Earth System Grid Federation (ESGF) serving the Coupled Model Intercomparison Project, Phase 5 (CMIP5) experiment, providing access to 2.5PB of data for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). Most of the tools currently available for scientific data analysis in the climate domain fail at large scale since they: (1) are desktop based and need the data locally; (2) are sequential, so do not benefit from available multicore/parallel machines; (3) do not provide declarative languages to express scientific data analysis tasks; (4) are domain-specific, which ties their adoption to a specific domain; and (5) do not provide a workflow support, to enable the definition of complex "experiments". The Ophidia project aims at facing most of the challenges highlighted above by providing a big data analytics framework for eScience. Ophidia provides declarative, server-side, and parallel data analysis, jointly with an internal storage model able to efficiently deal with multidimensional data and a hierarchical data organization to manage large data volumes ("datacubes"). The project relies on a strong background of high performance database management and OLAP systems to manage large scientific data sets. It also provides a native workflow management support, to define processing chains and workflows with tens to hundreds of data analytics operators to build real scientific use cases. With regard to interoperability aspects, the talk will present the contribution provided both to the RDA Working Group on Array Databases, and the Earth System Grid Federation (ESGF) Compute Working Team. Also highlighted will be the results of large scale climate model intercomparison data analysis experiments, for example: (1) defined in the context of the EU H2020 INDIGO-DataCloud project; (2) implemented in a real geographically distributed environment involving CMCC (Italy) and LLNL (US) sites; (3) exploiting Ophidia as server-side, parallel analytics engine; and (4) applied on real CMIP5 data sets available through ESGF.

Comment on "Most computational hydrology is not reproducible, so is it really science?" by Christopher Hutton et al.

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.