Processor Would Find Best Paths On Map

NASA Technical Reports Server (NTRS)

Eberhardt, Silvio P.

1990-01-01

Proposed very-large-scale integrated (VLSI) circuit image-data processor finds path of least cost from specified origin to any destination on map. Cost of traversal assigned to each picture element of map. Path of least cost from originating picture element to every other picture element computed as path that preserves as much as possible of signal transmitted by originating picture element. Dedicated microprocessor at each picture element stores cost of traversal and performs its share of computations of paths of least cost. Least-cost-path problem occurs in research, military maneuvers, and in planning routes of vehicles.

An economy of scale system's mensuration of large spacecraft

NASA Technical Reports Server (NTRS)

Deryder, L. J.

1981-01-01

The systems technology and cost particulars of using multipurpose platforms versus several sizes of bus type free flyer spacecraft to accomplish the same space experiment missions. Computer models of these spacecraft bus designs were created to obtain data relative to size, weight, power, performance, and cost. To answer the question of whether or not large scale does produce economy, the dominant cost factors were determined and the programmatic effect on individual experiment costs were evaluated.

Consumer Security Perceptions and the Perceived Influence on Adopting Cloud Computing: A Quantitative Study Using the Technology Acceptance Model

ERIC Educational Resources Information Center

Paquet, Katherine G.

2013-01-01

Cloud computing may provide cost benefits for organizations by eliminating the overhead costs of software, hardware, and maintenance (e.g., license renewals, upgrading software, servers and their physical storage space, administration along with funding a large IT department). In addition to the promised savings, the organization may require…

A Low Cost Micro-Computer Based Local Area Network for Medical Office and Medical Center Automation

PubMed Central

Epstein, Mel H.; Epstein, Lynn H.; Emerson, Ron G.

1984-01-01

A Low Cost Micro-computer based Local Area Network for medical office automation is described which makes use of an array of multiple and different personal computers interconnected by a local area network. Each computer on the network functions as fully potent workstations for data entry and report generation. The network allows each workstation complete access to the entire database. Additionally, designated computers may serve as access ports for remote terminals. Through “Gateways” the network may serve as a front end for a large mainframe, or may interface with another network. The system provides for the medical office environment the expandability and flexibility of a multi-terminal mainframe system at a far lower cost without sacrifice of performance.

The economics of time shared computing: Congestion, user costs and capacity

NASA Technical Reports Server (NTRS)

Agnew, C. E.

1982-01-01

Time shared systems permit the fixed costs of computing resources to be spread over large numbers of users. However, bottleneck results in the theory of closed queueing networks can be used to show that this economy of scale will be offset by the increased congestion that results as more users are added to the system. If one considers the total costs, including the congestion cost, there is an optimal number of users for a system which equals the saturation value usually used to define system capacity.

Challenges and opportunities of cloud computing for atmospheric sciences

NASA Astrophysics Data System (ADS)

Pérez Montes, Diego A.; Añel, Juan A.; Pena, Tomás F.; Wallom, David C. H.

2016-04-01

Cloud computing is an emerging technological solution widely used in many fields. Initially developed as a flexible way of managing peak demand it has began to make its way in scientific research. One of the greatest advantages of cloud computing for scientific research is independence of having access to a large cyberinfrastructure to fund or perform a research project. Cloud computing can avoid maintenance expenses for large supercomputers and has the potential to 'democratize' the access to high-performance computing, giving flexibility to funding bodies for allocating budgets for the computational costs associated with a project. Two of the most challenging problems in atmospheric sciences are computational cost and uncertainty in meteorological forecasting and climate projections. Both problems are closely related. Usually uncertainty can be reduced with the availability of computational resources to better reproduce a phenomenon or to perform a larger number of experiments. Here we expose results of the application of cloud computing resources for climate modeling using cloud computing infrastructures of three major vendors and two climate models. We show how the cloud infrastructure compares in performance to traditional supercomputers and how it provides the capability to complete experiments in shorter periods of time. The monetary cost associated is also analyzed. Finally we discuss the future potential of this technology for meteorological and climatological applications, both from the point of view of operational use and research.

Costs of fire suppression forces based on cost-aggregation approach

Treesearch

Gonz& aacute; lez-Cab& aacute; Armando n; Charles W. McKetta; Thomas J. Mills

1984-01-01

A cost-aggregation approach has been developed for determining the cost of Fire Management Inputs (FMls)-the direct fireline production units (personnel and equipment) used in initial attack and large-fire suppression activities. All components contributing to an FMI are identified, computed, and summed to estimate hourly costs. This approach can be applied to any FMI...

An efficient implementation of 3D high-resolution imaging for large-scale seismic data with GPU/CPU heterogeneous parallel computing

NASA Astrophysics Data System (ADS)

Xu, Jincheng; Liu, Wei; Wang, Jin; Liu, Linong; Zhang, Jianfeng

2018-02-01

De-absorption pre-stack time migration (QPSTM) compensates for the absorption and dispersion of seismic waves by introducing an effective Q parameter, thereby making it an effective tool for 3D, high-resolution imaging of seismic data. Although the optimal aperture obtained via stationary-phase migration reduces the computational cost of 3D QPSTM and yields 3D stationary-phase QPSTM, the associated computational efficiency is still the main problem in the processing of 3D, high-resolution images for real large-scale seismic data. In the current paper, we proposed a division method for large-scale, 3D seismic data to optimize the performance of stationary-phase QPSTM on clusters of graphics processing units (GPU). Then, we designed an imaging point parallel strategy to achieve an optimal parallel computing performance. Afterward, we adopted an asynchronous double buffering scheme for multi-stream to perform the GPU/CPU parallel computing. Moreover, several key optimization strategies of computation and storage based on the compute unified device architecture (CUDA) were adopted to accelerate the 3D stationary-phase QPSTM algorithm. Compared with the initial GPU code, the implementation of the key optimization steps, including thread optimization, shared memory optimization, register optimization and special function units (SFU), greatly improved the efficiency. A numerical example employing real large-scale, 3D seismic data showed that our scheme is nearly 80 times faster than the CPU-QPSTM algorithm. Our GPU/CPU heterogeneous parallel computing framework significant reduces the computational cost and facilitates 3D high-resolution imaging for large-scale seismic data.

On the Large-Scaling Issues of Cloud-based Applications for Earth Science Dat

NASA Astrophysics Data System (ADS)

Hua, H.

2016-12-01

Next generation science data systems are needed to address the incoming flood of data from new missions such as NASA's SWOT and NISAR where its SAR data volumes and data throughput rates are order of magnitude larger than present day missions. Existing missions, such as OCO-2, may also require high turn-around time for processing different science scenarios where on-premise and even traditional HPC computing environments may not meet the high processing needs. Additionally, traditional means of procuring hardware on-premise are already limited due to facilities capacity constraints for these new missions. Experiences have shown that to embrace efficient cloud computing approaches for large-scale science data systems requires more than just moving existing code to cloud environments. At large cloud scales, we need to deal with scaling and cost issues. We present our experiences on deploying multiple instances of our hybrid-cloud computing science data system (HySDS) to support large-scale processing of Earth Science data products. We will explore optimization approaches to getting best performance out of hybrid-cloud computing as well as common issues that will arise when dealing with large-scale computing. Novel approaches were utilized to do processing on Amazon's spot market, which can potentially offer 75%-90% costs savings but with an unpredictable computing environment based on market forces.

Principal Component Geostatistical Approach for large-dimensional inverse problems

PubMed Central

Kitanidis, P K; Lee, J

2014-01-01

The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m, and the number of observations, n, is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m2n, though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n. The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m2 as in the textbook approach. For problems of very large m, this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best. PMID:25558113

Principal Component Geostatistical Approach for large-dimensional inverse problems.

PubMed

Kitanidis, P K; Lee, J

2014-07-01

The quasi-linear geostatistical approach is for weakly nonlinear underdetermined inverse problems, such as Hydraulic Tomography and Electrical Resistivity Tomography. It provides best estimates as well as measures for uncertainty quantification. However, for its textbook implementation, the approach involves iterations, to reach an optimum, and requires the determination of the Jacobian matrix, i.e., the derivative of the observation function with respect to the unknown. Although there are elegant methods for the determination of the Jacobian, the cost is high when the number of unknowns, m , and the number of observations, n , is high. It is also wasteful to compute the Jacobian for points away from the optimum. Irrespective of the issue of computing derivatives, the computational cost of implementing the method is generally of the order of m 2 n , though there are methods to reduce the computational cost. In this work, we present an implementation that utilizes a matrix free in terms of the Jacobian matrix Gauss-Newton method and improves the scalability of the geostatistical inverse problem. For each iteration, it is required to perform K runs of the forward problem, where K is not just much smaller than m but can be smaller that n . The computational and storage cost of implementation of the inverse procedure scales roughly linearly with m instead of m 2 as in the textbook approach. For problems of very large m , this implementation constitutes a dramatic reduction in computational cost compared to the textbook approach. Results illustrate the validity of the approach and provide insight in the conditions under which this method perform best.

Low Cost, Scalable Proteomics Data Analysis Using Amazon's Cloud Computing Services and Open Source Search Algorithms

PubMed Central

Halligan, Brian D.; Geiger, Joey F.; Vallejos, Andrew K.; Greene, Andrew S.; Twigger, Simon N.

2009-01-01

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step by step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center website (http://proteomics.mcw.edu/vipdac). PMID:19358578

Low cost, scalable proteomics data analysis using Amazon's cloud computing services and open source search algorithms.

PubMed

Halligan, Brian D; Geiger, Joey F; Vallejos, Andrew K; Greene, Andrew S; Twigger, Simon N

2009-06-01

One of the major difficulties for many laboratories setting up proteomics programs has been obtaining and maintaining the computational infrastructure required for the analysis of the large flow of proteomics data. We describe a system that combines distributed cloud computing and open source software to allow laboratories to set up scalable virtual proteomics analysis clusters without the investment in computational hardware or software licensing fees. Additionally, the pricing structure of distributed computing providers, such as Amazon Web Services, allows laboratories or even individuals to have large-scale computational resources at their disposal at a very low cost per run. We provide detailed step-by-step instructions on how to implement the virtual proteomics analysis clusters as well as a list of current available preconfigured Amazon machine images containing the OMSSA and X!Tandem search algorithms and sequence databases on the Medical College of Wisconsin Proteomics Center Web site ( http://proteomics.mcw.edu/vipdac ).

Facilitating NASA Earth Science Data Processing Using Nebula Cloud Computing

NASA Technical Reports Server (NTRS)

Pham, Long; Chen, Aijun; Kempler, Steven; Lynnes, Christopher; Theobald, Michael; Asghar, Esfandiari; Campino, Jane; Vollmer, Bruce

2011-01-01

Cloud Computing has been implemented in several commercial arenas. The NASA Nebula Cloud Computing platform is an Infrastructure as a Service (IaaS) built in 2008 at NASA Ames Research Center and 2010 at GSFC. Nebula is an open source Cloud platform intended to: a) Make NASA realize significant cost savings through efficient resource utilization, reduced energy consumption, and reduced labor costs. b) Provide an easier way for NASA scientists and researchers to efficiently explore and share large and complex data sets. c) Allow customers to provision, manage, and decommission computing capabilities on an as-needed bases

Engaging Undergraduate Math Majors in Geoscience Research using Interactive Simulations and Computer Art

NASA Astrophysics Data System (ADS)

Matott, L. S.; Hymiak, B.; Reslink, C. F.; Baxter, C.; Aziz, S.

2012-12-01

As part of the NSF-sponsored 'URGE (Undergraduate Research Group Experiences) to Compute' program, Dr. Matott has been collaborating with talented Math majors to explore the design of cost-effective systems to safeguard groundwater supplies from contaminated sites. Such activity is aided by a combination of groundwater modeling, simulation-based optimization, and high-performance computing - disciplines largely unfamiliar to the students at the outset of the program. To help train and engage the students, a number of interactive and graphical software packages were utilized. Examples include: (1) a tutorial for exploring the behavior of evolutionary algorithms and other heuristic optimizers commonly used in simulation-based optimization; (2) an interactive groundwater modeling package for exploring alternative pump-and-treat containment scenarios at a contaminated site in Billings, Montana; (3) the R software package for visualizing various concepts related to subsurface hydrology; and (4) a job visualization tool for exploring the behavior of numerical experiments run on a large distributed computing cluster. Further engagement and excitement in the program was fostered by entering (and winning) a computer art competition run by the Coalition for Academic Scientific Computation (CASC). The winning submission visualizes an exhaustively mapped optimization cost surface and dramatically illustrates the phenomena of artificial minima - valley locations that correspond to designs whose costs are only partially optimal.

Scaling predictive modeling in drug development with cloud computing.

PubMed

Moghadam, Behrooz Torabi; Alvarsson, Jonathan; Holm, Marcus; Eklund, Martin; Carlsson, Lars; Spjuth, Ola

2015-01-26

Growing data sets with increased time for analysis is hampering predictive modeling in drug discovery. Model building can be carried out on high-performance computer clusters, but these can be expensive to purchase and maintain. We have evaluated ligand-based modeling on cloud computing resources where computations are parallelized and run on the Amazon Elastic Cloud. We trained models on open data sets of varying sizes for the end points logP and Ames mutagenicity and compare with model building parallelized on a traditional high-performance computing cluster. We show that while high-performance computing results in faster model building, the use of cloud computing resources is feasible for large data sets and scales well within cloud instances. An additional advantage of cloud computing is that the costs of predictive models can be easily quantified, and a choice can be made between speed and economy. The easy access to computational resources with no up-front investments makes cloud computing an attractive alternative for scientists, especially for those without access to a supercomputer, and our study shows that it enables cost-efficient modeling of large data sets on demand within reasonable time.

Computer Programs for Library Operations; Results of a Survey Conducted Between Fall 1971 and Spring 1972.

ERIC Educational Resources Information Center

Liberman, Eva; And Others

Many library operations involving large data banks lend themselves readily to computer operation. In setting up library computer programs, in changing or expanding programs, cost in programming and time delays could be substantially reduced if the programmers had access to library computer programs being used by other libraries, providing similar…

S-1 project. Volume I. Architecture. 1979 annual report

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

Not Available

1979-01-01

The US Navy is one of the world's largest users of digital computing equipment having a procurement cost of at least $50,000, and is the single largest such computer customer in the Department of Defense. Its projected acquisition plan for embedded computer systems during the first half of the 80s contemplates the installation of over 10,000 such systems at an estimated cost of several billions of dollars. This expenditure, though large, is dwarfed by the 85 billion dollars which DOD is projected to spend during the next half-decade on computer software, the near-majority of which will be spent by themore » Navy; the life-cycle costs of the 700,000+ lines of software for a single large Navy weapons systems application (e.g., AEGIS) have been conservatively estimated at most of a billion dollars. The S-1 Project is dedicated to realizing potentially large improvements in the efficiency with which such very large sums may be spent, so that greater military effectiveness may be secured earlier, and with smaller expenditures. The fundamental objectives of the S-1 Project's work are first to enable the Navy to be able to quickly, reliably and inexpensively evaluate at any time what is available from the state-of-the-art in digital processing systems and what the relevance of such systems may be to Navy data processing applications: and second to provide reference prototype systems to support possible competitive procurement action leading to deployment of such systems.« less

Design, processing and testing of LSI arrays, hybrid microelectronics task

NASA Technical Reports Server (NTRS)

Himmel, R. P.; Stuhlbarg, S. M.; Ravetti, R. G.; Zulueta, P. J.; Rothrock, C. W.

1979-01-01

Mathematical cost models previously developed for hybrid microelectronic subsystems were refined and expanded. Rework terms related to substrate fabrication, nonrecurring developmental and manufacturing operations, and prototype production are included. Sample computer programs were written to demonstrate hybrid microelectric applications of these cost models. Computer programs were generated to calculate and analyze values for the total microelectronics costs. Large scale integrated (LST) chips utilizing tape chip carrier technology were studied. The feasibility of interconnecting arrays of LSU chips utilizing tape chip carrier and semiautomatic wire bonding technology was demonstrated.

Modeling and simulation of ocean wave propagation using lattice Boltzmann method

NASA Astrophysics Data System (ADS)

Nuraiman, Dian

2017-10-01

In this paper, we present on modeling and simulation of ocean wave propagation from the deep sea to the shoreline. This requires high computational cost for simulation with large domain. We propose to couple a 1D shallow water equations (SWE) model with a 2D incompressible Navier-Stokes equations (NSE) model in order to reduce the computational cost. The coupled model is solved using the lattice Boltzmann method (LBM) with the lattice Bhatnagar-Gross-Krook (BGK) scheme. Additionally, a special method is implemented to treat the complex behavior of free surface close to the shoreline. The result shows the coupled model can reduce computational cost significantly compared to the full NSE model.

2000 Numerical Propulsion System Simulation Review

NASA Technical Reports Server (NTRS)

Lytle, John; Follen, Greg; Naiman, Cynthia; Veres, Joseph; Owen, Karl; Lopez, Isaac

2001-01-01

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with industry, academia, and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This concept is called the Numerical Propulsion System Simulation (NPSS). NPSS consists of three main elements: (1) engineering models that enable multidisciplinary analysis of large subsystems and systems at various levels of detail, (2) a simulation environment that maximizes designer productivity, and (3) a cost-effective. high-performance computing platform. A fundamental requirement of the concept is that the simulations must be capable of overnight execution on easily accessible computing platforms. This will greatly facilitate the use of large-scale simulations in a design environment. This paper describes the current status of the NPSS with specific emphasis on the progress made over the past year on air breathing propulsion applications. Major accomplishments include the first formal release of the NPSS object-oriented architecture (NPSS Version 1) and the demonstration of a one order of magnitude reduction in computing cost-to-performance ratio using a cluster of personal computers. The paper also describes the future NPSS milestones, which include the simulation of space transportation propulsion systems in response to increased emphasis on safe, low cost access to space within NASA'S Aerospace Technology Enterprise. In addition, the paper contains a summary of the feedback received from industry partners on the fiscal year 1999 effort and the actions taken over the past year to respond to that feedback. NPSS was supported in fiscal year 2000 by the High Performance Computing and Communications Program.

2001 Numerical Propulsion System Simulation Review

NASA Technical Reports Server (NTRS)

Lytle, John; Follen, Gregory; Naiman, Cynthia; Veres, Joseph; Owen, Karl; Lopez, Isaac

2002-01-01

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with industry, academia and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This concept is called the Numerical Propulsion System Simulation (NPSS). NPSS consists of three main elements: (1) engineering models that enable multidisciplinary analysis of large subsystems and systems at various levels of detail, (2) a simulation environment that maximizes designer productivity, and (3) a cost-effective, high-performance computing platform. A fundamental requirement of the concept is that the simulations must be capable of overnight execution on easily accessible computing platforms. This will greatly facilitate the use of large-scale simulations in a design environment. This paper describes the current status of the NPSS with specific emphasis on the progress made over the past year on air breathing propulsion applications. Major accomplishments include the first formal release of the NPSS object-oriented architecture (NPSS Version 1) and the demonstration of a one order of magnitude reduction in computing cost-to-performance ratio using a cluster of personal computers. The paper also describes the future NPSS milestones, which include the simulation of space transportation propulsion systems in response to increased emphasis on safe, low cost access to space within NASA's Aerospace Technology Enterprise. In addition, the paper contains a summary of the feedback received from industry partners on the fiscal year 2000 effort and the actions taken over the past year to respond to that feedback. NPSS was supported in fiscal year 2001 by the High Performance Computing and Communications Program.

Computer software to estimate timber harvesting system production, cost, and revenue

Treesearch

Dr. John E. Baumgras; Dr. Chris B. LeDoux

1992-01-01

Large variations in timber harvesting cost and revenue can result from the differences between harvesting systems, the variable attributes of harvesting sites and timber stands, or changing product markets. Consequently, system and site specific estimates of production rates and costs are required to improve estimates of harvesting revenue. This paper describes...

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

PubMed

Chen, Shizhi; Yang, Xiaodong; Tian, Yingli

2015-09-01

A key challenge in large-scale image classification is how to achieve efficiency in terms of both computation and memory without compromising classification accuracy. The learning-based classifiers achieve the state-of-the-art accuracies, but have been criticized for the computational complexity that grows linearly with the number of classes. The nonparametric nearest neighbor (NN)-based classifiers naturally handle large numbers of categories, but incur prohibitively expensive computation and memory costs. In this brief, we present a novel classification scheme, i.e., discriminative hierarchical K-means tree (D-HKTree), which combines the advantages of both learning-based and NN-based classifiers. The complexity of the D-HKTree only grows sublinearly with the number of categories, which is much better than the recent hierarchical support vector machines-based methods. The memory requirement is the order of magnitude less than the recent Naïve Bayesian NN-based approaches. The proposed D-HKTree classification scheme is evaluated on several challenging benchmark databases and achieves the state-of-the-art accuracies, while with significantly lower computation cost and memory requirement.

Using technology to support investigations in the electronic age: tracking hackers to large scale international computer fraud

NASA Astrophysics Data System (ADS)

McFall, Steve

1994-03-01

With the increase in business automation and the widespread availability and low cost of computer systems, law enforcement agencies have seen a corresponding increase in criminal acts involving computers. The examination of computer evidence is a new field of forensic science with numerous opportunities for research and development. Research is needed to develop new software utilities to examine computer storage media, expert systems capable of finding criminal activity in large amounts of data, and to find methods of recovering data from chemically and physically damaged computer storage media. In addition, defeating encryption and password protection of computer files is also a topic requiring more research and development.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME.

PubMed

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2016-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.

Autonomic Closure for Turbulent Flows Using Approximate Bayesian Computation

NASA Astrophysics Data System (ADS)

Doronina, Olga; Christopher, Jason; Hamlington, Peter; Dahm, Werner

2017-11-01

Autonomic closure is a new technique for achieving fully adaptive and physically accurate closure of coarse-grained turbulent flow governing equations, such as those solved in large eddy simulations (LES). Although autonomic closure has been shown in recent a priori tests to more accurately represent unclosed terms than do dynamic versions of traditional LES models, the computational cost of the approach makes it challenging to implement for simulations of practical turbulent flows at realistically high Reynolds numbers. The optimization step used in the approach introduces large matrices that must be inverted and is highly memory intensive. In order to reduce memory requirements, here we propose to use approximate Bayesian computation (ABC) in place of the optimization step, thereby yielding a computationally-efficient implementation of autonomic closure that trades memory-intensive for processor-intensive computations. The latter challenge can be overcome as co-processors such as general purpose graphical processing units become increasingly available on current generation petascale and exascale supercomputers. In this work, we outline the formulation of ABC-enabled autonomic closure and present initial results demonstrating the accuracy and computational cost of the approach.

Memory reduction through higher level language hardware

NASA Technical Reports Server (NTRS)

Kerner, H.; Gellman, L.

1972-01-01

Application of large scale integration in computers to reduce size and manufacturing costs and to produce improvements in logic function is discussed. Use of FORTRAN 4 as computer language for this purpose is described. Effectiveness of method in storing information is illustrated.

Dynamic remapping of parallel computations with varying resource demands

NASA Technical Reports Server (NTRS)

Nicol, D. M.; Saltz, J. H.

1986-01-01

A large class of computational problems is characterized by frequent synchronization, and computational requirements which change as a function of time. When such a problem must be solved on a message passing multiprocessor machine, the combination of these characteristics lead to system performance which decreases in time. Performance can be improved with periodic redistribution of computational load; however, redistribution can exact a sometimes large delay cost. We study the issue of deciding when to invoke a global load remapping mechanism. Such a decision policy must effectively weigh the costs of remapping against the performance benefits. We treat this problem by constructing two analytic models which exhibit stochastically decreasing performance. One model is quite tractable; we are able to describe the optimal remapping algorithm, and the optimal decision policy governing when to invoke that algorithm. However, computational complexity prohibits the use of the optimal remapping decision policy. We then study the performance of a general remapping policy on both analytic models. This policy attempts to minimize a statistic W(n) which measures the system degradation (including the cost of remapping) per computation step over a period of n steps. We show that as a function of time, the expected value of W(n) has at most one minimum, and that when this minimum exists it defines the optimal fixed-interval remapping policy. Our decision policy appeals to this result by remapping when it estimates that W(n) is minimized. Our performance data suggests that this policy effectively finds the natural frequency of remapping. We also use the analytic models to express the relationship between performance and remapping cost, number of processors, and the computation's stochastic activity.

Computation of Sensitivity Derivatives of Navier-Stokes Equations using Complex Variables

NASA Technical Reports Server (NTRS)

Vatsa, Veer N.

2004-01-01

Accurate computation of sensitivity derivatives is becoming an important item in Computational Fluid Dynamics (CFD) because of recent emphasis on using nonlinear CFD methods in aerodynamic design, optimization, stability and control related problems. Several techniques are available to compute gradients or sensitivity derivatives of desired flow quantities or cost functions with respect to selected independent (design) variables. Perhaps the most common and oldest method is to use straightforward finite-differences for the evaluation of sensitivity derivatives. Although very simple, this method is prone to errors associated with choice of step sizes and can be cumbersome for geometric variables. The cost per design variable for computing sensitivity derivatives with central differencing is at least equal to the cost of three full analyses, but is usually much larger in practice due to difficulty in choosing step sizes. Another approach gaining popularity is the use of Automatic Differentiation software (such as ADIFOR) to process the source code, which in turn can be used to evaluate the sensitivity derivatives of preselected functions with respect to chosen design variables. In principle, this approach is also very straightforward and quite promising. The main drawback is the large memory requirement because memory use increases linearly with the number of design variables. ADIFOR software can also be cumber-some for large CFD codes and has not yet reached a full maturity level for production codes, especially in parallel computing environments.

Failure Bounding And Sensitivity Analysis Applied To Monte Carlo Entry, Descent, And Landing Simulations

NASA Technical Reports Server (NTRS)

Gaebler, John A.; Tolson, Robert H.

2010-01-01

In the study of entry, descent, and landing, Monte Carlo sampling methods are often employed to study the uncertainty in the designed trajectory. The large number of uncertain inputs and outputs, coupled with complicated non-linear models, can make interpretation of the results difficult. Three methods that provide statistical insights are applied to an entry, descent, and landing simulation. The advantages and disadvantages of each method are discussed in terms of the insights gained versus the computational cost. The first method investigated was failure domain bounding which aims to reduce the computational cost of assessing the failure probability. Next a variance-based sensitivity analysis was studied for the ability to identify which input variable uncertainty has the greatest impact on the uncertainty of an output. Finally, probabilistic sensitivity analysis is used to calculate certain sensitivities at a reduced computational cost. These methods produce valuable information that identifies critical mission parameters and needs for new technology, but generally at a significant computational cost.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

PubMed

Devlaminck, Dieter; Waegeman, Willem; Wyns, Bart; Otte, Georges; Santens, Patrick

2010-06-01

Brain-computer interfaces (BCIs) present an alternative way of communication for people with severe disabilities. One of the shortcomings in current BCI systems, recently put forward in the fourth BCI competition, is the asynchronous detection of motor imagery versus resting state. We investigated this extension to the three-class case, in which the resting state is considered virtually lying between two motor classes, resulting in a large penalty when one motor task is misclassified into the other motor class. We particularly focus on the behavior of different machine-learning techniques and on the role of multi-class cost-sensitive learning in such a context. To this end, four different kernel methods are empirically compared, namely pairwise multi-class support vector machines (SVMs), two cost-sensitive multi-class SVMs and kernel-based ordinal regression. The experimental results illustrate that ordinal regression performs better than the other three approaches when a cost-sensitive performance measure such as the mean-squared error is considered. By contrast, multi-class cost-sensitive learning enables us to control the number of large errors made between two motor tasks.

APL: An Alternative to the Multi-Language Environment for Education. Systems Research Memo Number Four.

ERIC Educational Resources Information Center

Lippert, Henry T.; Harris, Edward V.

The diverse requirements for computing facilities in education place heavy demands upon available resources. Although multiple or very large computers can supply such diverse needs, their cost makes them impractical for many institutions. Small computers which serve a few specific needs may be an economical answer. However, to serve operationally…

Benchmarking undedicated cloud computing providers for analysis of genomic datasets.

PubMed

Yazar, Seyhan; Gooden, George E C; Mackey, David A; Hewitt, Alex W

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5-78.2) for E.coli and 53.5% (95% CI: 34.4-72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5-303.1) and 173.9% (95% CI: 134.6-213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE.

Benchmarking Undedicated Cloud Computing Providers for Analysis of Genomic Datasets

PubMed Central

Yazar, Seyhan; Gooden, George E. C.; Mackey, David A.; Hewitt, Alex W.

2014-01-01

A major bottleneck in biological discovery is now emerging at the computational level. Cloud computing offers a dynamic means whereby small and medium-sized laboratories can rapidly adjust their computational capacity. We benchmarked two established cloud computing services, Amazon Web Services Elastic MapReduce (EMR) on Amazon EC2 instances and Google Compute Engine (GCE), using publicly available genomic datasets (E.coli CC102 strain and a Han Chinese male genome) and a standard bioinformatic pipeline on a Hadoop-based platform. Wall-clock time for complete assembly differed by 52.9% (95% CI: 27.5–78.2) for E.coli and 53.5% (95% CI: 34.4–72.6) for human genome, with GCE being more efficient than EMR. The cost of running this experiment on EMR and GCE differed significantly, with the costs on EMR being 257.3% (95% CI: 211.5–303.1) and 173.9% (95% CI: 134.6–213.1) more expensive for E.coli and human assemblies respectively. Thus, GCE was found to outperform EMR both in terms of cost and wall-clock time. Our findings confirm that cloud computing is an efficient and potentially cost-effective alternative for analysis of large genomic datasets. In addition to releasing our cost-effectiveness comparison, we present available ready-to-use scripts for establishing Hadoop instances with Ganglia monitoring on EC2 or GCE. PMID:25247298

On-Line Systems: Promise and Pitfalls

ERIC Educational Resources Information Center

Cuadra, Carlos A.

1971-01-01

The virtues of interactive systems are speed, intimacy, and - if time-sharing is involved - economy. The major problems are the cost of the large computers and files necessary for bibliographic data, the still-high cost of communications, and the generally poor design of the user-system interfaces. (Author)

48 CFR 9904.413-60 - Illustrations.

Code of Federal Regulations, 2010 CFR

2010-10-01

... nine segments is primarily commercial. Employee turnover at the segments performing commercial work is... employees at five segments. Pension cost is computed by use of an immediate-gain actuarial cost method. One... period, Segment X had a large and unforeseeable reduction of employees because of a contract termination...

An efficient Bayesian data-worth analysis using a multilevel Monte Carlo method

NASA Astrophysics Data System (ADS)

Lu, Dan; Ricciuto, Daniel; Evans, Katherine

2018-03-01

Improving the understanding of subsurface systems and thus reducing prediction uncertainty requires collection of data. As the collection of subsurface data is costly, it is important that the data collection scheme is cost-effective. Design of a cost-effective data collection scheme, i.e., data-worth analysis, requires quantifying model parameter, prediction, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface hydrological model simulations using standard Monte Carlo (MC) sampling or surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose an efficient Bayesian data-worth analysis using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce computational costs using multifidelity approximations. Since the Bayesian data-worth analysis involves a great deal of expectation estimation, the cost saving of the MLMC in the assessment can be outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it for a highly heterogeneous two-phase subsurface flow simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the standard MC estimation. But compared to the standard MC, the MLMC greatly reduces the computational costs.

Defense Acquisitions Acronyms and Terms

DTIC Science & Technology

2012-12-01

Computer-Aided Design CADD Computer-Aided Design and Drafting CAE Component Acquisition Executive; Computer-Aided Engineering CAIV Cost As an...Radiation to Ordnance HFE Human Factors Engineering HHA Health Hazard Assessment HNA Host-Nation Approval HNS Host-Nation Support HOL High -Order...Engineering Change Proposal VHSIC Very High Speed Integrated Circuit VLSI Very Large Scale Integration VOC Volatile Organic Compound W WAN Wide

Research on OpenStack of open source cloud computing in colleges and universities’ computer room

NASA Astrophysics Data System (ADS)

Wang, Lei; Zhang, Dandan

2017-06-01

In recent years, the cloud computing technology has a rapid development, especially open source cloud computing. Open source cloud computing has attracted a large number of user groups by the advantages of open source and low cost, have now become a large-scale promotion and application. In this paper, firstly we briefly introduced the main functions and architecture of the open source cloud computing OpenStack tools, and then discussed deeply the core problems of computer labs in colleges and universities. Combining with this research, it is not that the specific application and deployment of university computer rooms with OpenStack tool. The experimental results show that the application of OpenStack tool can efficiently and conveniently deploy cloud of university computer room, and its performance is stable and the functional value is good.

Design quadrilateral apertures in binary computer-generated holograms of large space bandwidth product.

PubMed

Wang, Jing; Sheng, Yunlong

2016-09-20

A new approach for designing the binary computer-generated hologram (CGH) of a very large number of pixels is proposed. Diffraction of the CGH apertures is computed by the analytical Abbe transform and by considering the aperture edges as the basic diffracting elements. The computation cost is independent of the CGH size. The arbitrary-shaped polygonal apertures in the CGH consist of quadrilateral apertures, which are designed by assigning the binary phases using the parallel genetic algorithm with a local search, followed by optimizing the locations of the co-vertices with a direct search. The design results in high performance with low image reconstruction error.

Adjoint-Based Aerodynamic Design of Complex Aerospace Configurations

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.

2016-01-01

An overview of twenty years of adjoint-based aerodynamic design research at NASA Langley Research Center is presented. Adjoint-based algorithms provide a powerful tool for efficient sensitivity analysis of complex large-scale computational fluid dynamics (CFD) simulations. Unlike alternative approaches for which computational expense generally scales with the number of design parameters, adjoint techniques yield sensitivity derivatives of a simulation output with respect to all input parameters at the cost of a single additional simulation. With modern large-scale CFD applications often requiring millions of compute hours for a single analysis, the efficiency afforded by adjoint methods is critical in realizing a computationally tractable design optimization capability for such applications.

Extraterrestrial processing and manufacturing of large space systems, volume 2, chapters 7-14 and appendices

NASA Technical Reports Server (NTRS)

Miller, R. H.; Smith, D. B. S.

1979-01-01

Production and support equipment specifications are described for the space manufacturing facility (SMF). Defined production equipment includes electromagnetic pumps for liquid metal, metal alloying furnaces, die casters, electron beam welders and cutters, glass forming for structural elements, and rolling. A cost analysis is presented which includes the development, the aquisition of all SMF elements, initial operating cost, maintenance and logistics cost, cost of terrestrial materials, and transportation cost for each major element. Computer program listings and outputs are appended.

The role of dedicated data computing centers in the age of cloud computing

NASA Astrophysics Data System (ADS)

Caramarcu, Costin; Hollowell, Christopher; Strecker-Kellogg, William; Wong, Antonio; Zaytsev, Alexandr

2017-10-01

Brookhaven National Laboratory (BNL) anticipates significant growth in scientific programs with large computing and data storage needs in the near future and has recently reorganized support for scientific computing to meet these needs. A key component is the enhanced role of the RHIC-ATLAS Computing Facility (RACF) in support of high-throughput and high-performance computing (HTC and HPC) at BNL. This presentation discusses the evolving role of the RACF at BNL, in light of its growing portfolio of responsibilities and its increasing integration with cloud (academic and for-profit) computing activities. We also discuss BNL’s plan to build a new computing center to support the new responsibilities of the RACF and present a summary of the cost benefit analysis done, including the types of computing activities that benefit most from a local data center vs. cloud computing. This analysis is partly based on an updated cost comparison of Amazon EC2 computing services and the RACF, which was originally conducted in 2012.

MOLNs: A CLOUD PLATFORM FOR INTERACTIVE, REPRODUCIBLE, AND SCALABLE SPATIAL STOCHASTIC COMPUTATIONAL EXPERIMENTS IN SYSTEMS BIOLOGY USING PyURDME

PubMed Central

Drawert, Brian; Trogdon, Michael; Toor, Salman; Petzold, Linda; Hellander, Andreas

2017-01-01

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments. PMID:28190948

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

PubMed

Zhao, Shanrong; Prenger, Kurt; Smith, Lance; Messina, Thomas; Fan, Hongtao; Jaeger, Edward; Stephens, Susan

2013-06-27

Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing

PubMed Central

2013-01-01

Background Technical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses. Results Here, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies. Conclusions Rainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html. PMID:23802613

Repository Planning, Design, and Engineering: Part II-Equipment and Costing.

PubMed

Baird, Phillip M; Gunter, Elaine W

2016-08-01

Part II of this article discusses and provides guidance on the equipment and systems necessary to operate a repository. The various types of storage equipment and monitoring and support systems are presented in detail. While the material focuses on the large repository, the requirements for a small-scale startup are also presented. Cost estimates and a cost model for establishing a repository are presented. The cost model presents an expected range of acquisition costs for the large capital items in developing a repository. A range of 5,000-7,000 ft(2) constructed has been assumed, with 50 frozen storage units, to reflect a successful operation with growth potential. No design or engineering costs, permit or regulatory costs, or smaller items such as the computers, software, furniture, phones, and barcode readers required for operations have been included.

The study on servo-control system in the large aperture telescope

NASA Astrophysics Data System (ADS)

Hu, Wei; Zhenchao, Zhang; Daxing, Wang

2008-08-01

Large astronomical telescope or extremely enormous astronomical telescope servo tracking technique will be one of crucial technology that must be solved in researching and manufacturing. To control technique feature of large astronomical telescope or extremely enormous astronomical telescope, this paper design a sort of large astronomical telescope servo tracking control system. This system composes a principal and subordinate distributed control system, host computer sends steering instruction and receive slave computer functional mode, slave computer accomplish control algorithm and execute real-time control. Large astronomical telescope servo control use direct drive machine, and adopt DSP technology to complete direct torque control algorithm, Such design can not only increase control system performance, but also greatly reduced volume and costs of control system, which has a significant occurrence. The system design scheme can be proved reasonably by calculating and simulating. This system can be applied to large astronomical telescope.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing.

PubMed

Xu, Jason; Minin, Vladimir N

2015-07-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes.

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

PubMed Central

Xu, Jason; Minin, Vladimir N.

2016-01-01

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring integration over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multi-type branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for blood cell formation and evolution of self-replicating transposable elements in bacterial genomes. PMID:26949377

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

Optimize Resources and Help Reduce Cost of Ownership with Dell[TM] Systems Management

ERIC Educational Resources Information Center

Technology & Learning, 2008

2008-01-01

Maintaining secure, convenient administration of the PC system environment can be a significant drain on resources. Deskside visits can greatly increase the cost of supporting a large number of computers. Even simple tasks, such as tracking inventory or updating software, quickly become expensive when they require physically visiting every…

Fast Legendre moment computation for template matching

NASA Astrophysics Data System (ADS)

Li, Bing C.

2017-05-01

Normalized cross correlation (NCC) based template matching is insensitive to intensity changes and it has many applications in image processing, object detection, video tracking and pattern recognition. However, normalized cross correlation implementation is computationally expensive since it involves both correlation computation and normalization implementation. In this paper, we propose Legendre moment approach for fast normalized cross correlation implementation and show that the computational cost of this proposed approach is independent of template mask sizes which is significantly faster than traditional mask size dependent approaches, especially for large mask templates. Legendre polynomials have been widely used in solving Laplace equation in electrodynamics in spherical coordinate systems, and solving Schrodinger equation in quantum mechanics. In this paper, we extend Legendre polynomials from physics to computer vision and pattern recognition fields, and demonstrate that Legendre polynomials can help to reduce the computational cost of NCC based template matching significantly.

Extending Beowulf Clusters

USGS Publications Warehouse

Steinwand, Daniel R.; Maddox, Brian; Beckmann, Tim; Hamer, George

2003-01-01

Beowulf clusters can provide a cost-effective way to compute numerical models and process large amounts of remote sensing image data. Usually a Beowulf cluster is designed to accomplish a specific set of processing goals, and processing is very efficient when the problem remains inside the constraints of the original design. There are cases, however, when one might wish to compute a problem that is beyond the capacity of the local Beowulf system. In these cases, spreading the problem to multiple clusters or to other machines on the network may provide a cost-effective solution.

Accelerating statistical image reconstruction algorithms for fan-beam x-ray CT using cloud computing

NASA Astrophysics Data System (ADS)

Srivastava, Somesh; Rao, A. Ravishankar; Sheinin, Vadim

2011-03-01

Statistical image reconstruction algorithms potentially offer many advantages to x-ray computed tomography (CT), e.g. lower radiation dose. But, their adoption in practical CT scanners requires extra computation power, which is traditionally provided by incorporating additional computing hardware (e.g. CPU-clusters, GPUs, FPGAs etc.) into a scanner. An alternative solution is to access the required computation power over the internet from a cloud computing service, which is orders-of-magnitude more cost-effective. This is because users only pay a small pay-as-you-go fee for the computation resources used (i.e. CPU time, storage etc.), and completely avoid purchase, maintenance and upgrade costs. In this paper, we investigate the benefits and shortcomings of using cloud computing for statistical image reconstruction. We parallelized the most time-consuming parts of our application, the forward and back projectors, using MapReduce, the standard parallelization library on clouds. From preliminary investigations, we found that a large speedup is possible at a very low cost. But, communication overheads inside MapReduce can limit the maximum speedup, and a better MapReduce implementation might become necessary in the future. All the experiments for this paper, including development and testing, were completed on the Amazon Elastic Compute Cloud (EC2) for less than $20.

Distributed Name Servers: Naming and Caching in Large Distributed Computing Environments

DTIC Science & Technology

1985-12-01

transmission rate of the communication medium1, transmission over a 56K bps line costs approx- imately 54r, and similarly, communication over a 9.6K...memories for modem computer systems attempt to maximize the hit ratio for a fixed-size cache by utilizing intelligent cache replacement algorithms

Avoiding the Enumeration of Infeasible Elementary Flux Modes by Including Transcriptional Regulatory Rules in the Enumeration Process Saves Computational Costs

PubMed Central

Jungreuthmayer, Christian; Ruckerbauer, David E.; Gerstl, Matthias P.; Hanscho, Michael; Zanghellini, Jürgen

2015-01-01

Despite the significant progress made in recent years, the computation of the complete set of elementary flux modes of large or even genome-scale metabolic networks is still impossible. We introduce a novel approach to speed up the calculation of elementary flux modes by including transcriptional regulatory information into the analysis of metabolic networks. Taking into account gene regulation dramatically reduces the solution space and allows the presented algorithm to constantly eliminate biologically infeasible modes at an early stage of the computation procedure. Thereby, computational costs, such as runtime, memory usage, and disk space, are extremely reduced. Moreover, we show that the application of transcriptional rules identifies non-trivial system-wide effects on metabolism. Using the presented algorithm pushes the size of metabolic networks that can be studied by elementary flux modes to new and much higher limits without the loss of predictive quality. This makes unbiased, system-wide predictions in large scale metabolic networks possible without resorting to any optimization principle. PMID:26091045

An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs.

PubMed

Cormode, Graham; Dasgupta, Anirban; Goyal, Amit; Lee, Chi Hoon

2018-01-01

Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users' queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop). We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with "vanilla" LSH, even when using the same amount of space.

Structural performance analysis and redesign

NASA Technical Reports Server (NTRS)

Whetstone, W. D.

1978-01-01

Program performs stress buckling and vibrational analysis of large, linear, finite-element systems in excess of 50,000 degrees of freedom. Cost, execution time, and storage requirements are kept reasonable through use of sparse matrix solution techniques, and other computational and data management procedures designed for problems of very large size.

Parallel spatial direct numerical simulations on the Intel iPSC/860 hypercube

NASA Technical Reports Server (NTRS)

Joslin, Ronald D.; Zubair, Mohammad

1993-01-01

The implementation and performance of a parallel spatial direct numerical simulation (PSDNS) approach on the Intel iPSC/860 hypercube is documented. The direct numerical simulation approach is used to compute spatially evolving disturbances associated with the laminar-to-turbulent transition in boundary-layer flows. The feasibility of using the PSDNS on the hypercube to perform transition studies is examined. The results indicate that the direct numerical simulation approach can effectively be parallelized on a distributed-memory parallel machine. By increasing the number of processors nearly ideal linear speedups are achieved with nonoptimized routines; slower than linear speedups are achieved with optimized (machine dependent library) routines. This slower than linear speedup results because the Fast Fourier Transform (FFT) routine dominates the computational cost and because the routine indicates less than ideal speedups. However with the machine-dependent routines the total computational cost decreases by a factor of 4 to 5 compared with standard FORTRAN routines. The computational cost increases linearly with spanwise wall-normal and streamwise grid refinements. The hypercube with 32 processors was estimated to require approximately twice the amount of Cray supercomputer single processor time to complete a comparable simulation; however it is estimated that a subgrid-scale model which reduces the required number of grid points and becomes a large-eddy simulation (PSLES) would reduce the computational cost and memory requirements by a factor of 10 over the PSDNS. This PSLES implementation would enable transition simulations on the hypercube at a reasonable computational cost.

Effects of combined dimension reduction and tabulation on the simulations of a turbulent premixed flame using a large-eddy simulation/probability density function method

NASA Astrophysics Data System (ADS)

Kim, Jeonglae; Pope, Stephen B.

2014-05-01

A turbulent lean-premixed propane-air flame stabilised by a triangular cylinder as a flame-holder is simulated to assess the accuracy and computational efficiency of combined dimension reduction and tabulation of chemistry. The computational condition matches the Volvo rig experiments. For the reactive simulation, the Lagrangian Large-Eddy Simulation/Probability Density Function (LES/PDF) formulation is used. A novel two-way coupling approach between LES and PDF is applied to obtain resolved density to reduce its statistical fluctuations. Composition mixing is evaluated by the modified Interaction-by-Exchange with the Mean (IEM) model. A baseline case uses In Situ Adaptive Tabulation (ISAT) to calculate chemical reactions efficiently. Its results demonstrate good agreement with the experimental measurements in turbulence statistics, temperature, and minor species mass fractions. For dimension reduction, 11 and 16 represented species are chosen and a variant of Rate Controlled Constrained Equilibrium (RCCE) is applied in conjunction with ISAT to each case. All the quantities in the comparison are indistinguishable from the baseline results using ISAT only. The combined use of RCCE/ISAT reduces the computational time for chemical reaction by more than 50%. However, for the current turbulent premixed flame, chemical reaction takes only a minor portion of the overall computational cost, in contrast to non-premixed flame simulations using LES/PDF, presumably due to the restricted manifold of purely premixed flame in the composition space. Instead, composition mixing is the major contributor to cost reduction since the mean-drift term, which is computationally expensive, is computed for the reduced representation. Overall, a reduction of more than 15% in the computational cost is obtained.

Lost in Cloud

NASA Technical Reports Server (NTRS)

Maluf, David A.; Shetye, Sandeep D.; Chilukuri, Sri; Sturken, Ian

2012-01-01

Cloud computing can reduce cost significantly because businesses can share computing resources. In recent years Small and Medium Businesses (SMB) have used Cloud effectively for cost saving and for sharing IT expenses. With the success of SMBs, many perceive that the larger enterprises ought to move into Cloud environment as well. Government agency s stove-piped environments are being considered as candidates for potential use of Cloud either as an enterprise entity or pockets of small communities. Cloud Computing is the delivery of computing as a service rather than as a product, whereby shared resources, software, and information are provided to computers and other devices as a utility over a network. Underneath the offered services, there exists a modern infrastructure cost of which is often spread across its services or its investors. As NASA is considered as an Enterprise class organization, like other enterprises, a shift has been occurring in perceiving its IT services as candidates for Cloud services. This paper discusses market trends in cloud computing from an enterprise angle and then addresses the topic of Cloud Computing for NASA in two possible forms. First, in the form of a public Cloud to support it as an enterprise, as well as to share it with the commercial and public at large. Second, as a private Cloud wherein the infrastructure is operated solely for NASA, whether managed internally or by a third-party and hosted internally or externally. The paper addresses the strengths and weaknesses of both paradigms of public and private Clouds, in both internally and externally operated settings. The content of the paper is from a NASA perspective but is applicable to any large enterprise with thousands of employees and contractors.

Mutual potential between two rigid bodies with arbitrary shapes and mass distributions

NASA Astrophysics Data System (ADS)

Hou, Xiyun; Scheeres, Daniel J.; Xin, Xiaosheng

2017-03-01

Formulae to compute the mutual potential, force, and torque between two rigid bodies are given. These formulae are expressed in Cartesian coordinates using inertia integrals. They are valid for rigid bodies with arbitrary shapes and mass distributions. By using recursive relations, these formulae can be easily implemented on computers. Comparisons with previous studies show their superiority in computation speed. Using the algorithm as a tool, the planar problem of two ellipsoids is studied. Generally, potential truncated at the second order is good enough for a qualitative description of the mutual dynamics. However, for ellipsoids with very large non-spherical terms, higher order terms of the potential should be considered, at the cost of a higher computational cost. Explicit formulae of the potential truncated to the fourth order are given.

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing.

PubMed

Wan, Shixiang; Zou, Quan

2017-01-01

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types. Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction. The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource. THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

Efficient Data-Worth Analysis Using a Multilevel Monte Carlo Method Applied in Oil Reservoir Simulations

NASA Astrophysics Data System (ADS)

Lu, D.; Ricciuto, D. M.; Evans, K. J.

2017-12-01

Data-worth analysis plays an essential role in improving the understanding of the subsurface system, in developing and refining subsurface models, and in supporting rational water resources management. However, data-worth analysis is computationally expensive as it requires quantifying parameter uncertainty, prediction uncertainty, and both current and potential data uncertainties. Assessment of these uncertainties in large-scale stochastic subsurface simulations using standard Monte Carlo (MC) sampling or advanced surrogate modeling is extremely computationally intensive, sometimes even infeasible. In this work, we propose efficient Bayesian analysis of data-worth using a multilevel Monte Carlo (MLMC) method. Compared to the standard MC that requires a significantly large number of high-fidelity model executions to achieve a prescribed accuracy in estimating expectations, the MLMC can substantially reduce the computational cost with the use of multifidelity approximations. As the data-worth analysis involves a great deal of expectation estimations, the cost savings from MLMC in the assessment can be very outstanding. While the proposed MLMC-based data-worth analysis is broadly applicable, we use it to a highly heterogeneous oil reservoir simulation to select an optimal candidate data set that gives the largest uncertainty reduction in predicting mass flow rates at four production wells. The choices made by the MLMC estimation are validated by the actual measurements of the potential data, and consistent with the estimation obtained from the standard MC. But compared to the standard MC, the MLMC greatly reduces the computational costs in the uncertainty reduction estimation, with up to 600 days cost savings when one processor is used.

Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction.

PubMed

de Lima, Camila; Salomão Helou, Elias

2018-01-01

Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughly O(N 3 ) floating point operations (flops) for N × N pixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator to O(N 2 logN) flops. Also, incremental algorithms have been devised that reduce by an order of magnitude the number of iterations required to achieve acceptable images. The present paper introduces an incremental algorithm with a cost of O(N 2 logN) flops per iteration and applies it to the reconstruction of very large tomographic images obtained from synchrotron light illuminated data.

Two-step simulation of velocity and passive scalar mixing at high Schmidt number in turbulent jets

NASA Astrophysics Data System (ADS)

Rah, K. Jeff; Blanquart, Guillaume

2016-11-01

Simulation of passive scalar in the high Schmidt number turbulent mixing process requires higher computational cost than that of velocity fields, because the scalar is associated with smaller length scales than velocity. Thus, full simulation of both velocity and passive scalar with high Sc for a practical configuration is difficult to perform. In this work, a new approach to simulate velocity and passive scalar mixing at high Sc is suggested to reduce the computational cost. First, the velocity fields are resolved by Large Eddy Simulation (LES). Then, by extracting the velocity information from LES, the scalar inside a moving fluid blob is simulated by Direct Numerical Simulation (DNS). This two-step simulation method is applied to a turbulent jet and provides a new way to examine a scalar mixing process in a practical application with smaller computational cost. NSF, Samsung Scholarship.

Using Mosix for Wide-Area Compuational Resources

USGS Publications Warehouse

Maddox, Brian G.

2004-01-01

One of the problems with using traditional Beowulf-type distributed processing clusters is that they require an investment in dedicated computer resources. These resources are usually needed in addition to pre-existing ones such as desktop computers and file servers. Mosix is a series of modifications to the Linux kernel that creates a virtual computer, featuring automatic load balancing by migrating processes from heavily loaded nodes to less used ones. An extension of the Beowulf concept is to run a Mosixenabled Linux kernel on a large number of computer resources in an organization. This configuration would provide a very large amount of computational resources based on pre-existing equipment. The advantage of this method is that it provides much more processing power than a traditional Beowulf cluster without the added costs of dedicating resources.

Business aspects of cardiovascular computed tomography: tackling the challenges.

PubMed

Bateman, Timothy M

2008-01-01

The purpose of this article is to provide a comprehensive understanding of the business issues surrounding provision of dedicated cardiovascular computed tomographic imaging. Some of the challenges include high up-front costs, current low utilization relative to scanner capability, and inadequate payments. Cardiovascular computed tomographic imaging is a valuable clinical modality that should be offered by cardiovascular centers-of-excellence. With careful consideration of the business aspects, moderate-to-large size cardiology programs should be able to implement an economically viable cardiovascular computed tomographic service.

Parallel implementation of geometrical shock dynamics for two dimensional converging shock waves

NASA Astrophysics Data System (ADS)

Qiu, Shi; Liu, Kuang; Eliasson, Veronica

2016-10-01

Geometrical shock dynamics (GSD) theory is an appealing method to predict the shock motion in the sense that it is more computationally efficient than solving the traditional Euler equations, especially for converging shock waves. However, to solve and optimize large scale configurations, the main bottleneck is the computational cost. Among the existing numerical GSD schemes, there is only one that has been implemented on parallel computers, with the purpose to analyze detonation waves. To extend the computational advantage of the GSD theory to more general applications such as converging shock waves, a numerical implementation using a spatial decomposition method has been coupled with a front tracking approach on parallel computers. In addition, an efficient tridiagonal system solver for massively parallel computers has been applied to resolve the most expensive function in this implementation, resulting in an efficiency of 0.93 while using 32 HPCC cores. Moreover, symmetric boundary conditions have been developed to further reduce the computational cost, achieving a speedup of 19.26 for a 12-sided polygonal converging shock.

Use of cloud computing in biomedicine.

PubMed

Sobeslav, Vladimir; Maresova, Petra; Krejcar, Ondrej; Franca, Tanos C C; Kuca, Kamil

2016-12-01

Nowadays, biomedicine is characterised by a growing need for processing of large amounts of data in real time. This leads to new requirements for information and communication technologies (ICT). Cloud computing offers a solution to these requirements and provides many advantages, such as cost savings, elasticity and scalability of using ICT. The aim of this paper is to explore the concept of cloud computing and the related use of this concept in the area of biomedicine. Authors offer a comprehensive analysis of the implementation of the cloud computing approach in biomedical research, decomposed into infrastructure, platform and service layer, and a recommendation for processing large amounts of data in biomedicine. Firstly, the paper describes the appropriate forms and technological solutions of cloud computing. Secondly, the high-end computing paradigm of cloud computing aspects is analysed. Finally, the potential and current use of applications in scientific research of this technology in biomedicine is discussed.

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization

PubMed Central

Chen, Qingkui; Zhao, Deyu; Wang, Jingjuan

2017-01-01

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes’ diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services. PMID:28777325

RGCA: A Reliable GPU Cluster Architecture for Large-Scale Internet of Things Computing Based on Effective Performance-Energy Optimization.

PubMed

Fang, Yuling; Chen, Qingkui; Xiong, Neal N; Zhao, Deyu; Wang, Jingjuan

2017-08-04

This paper aims to develop a low-cost, high-performance and high-reliability computing system to process large-scale data using common data mining algorithms in the Internet of Things (IoT) computing environment. Considering the characteristics of IoT data processing, similar to mainstream high performance computing, we use a GPU (Graphics Processing Unit) cluster to achieve better IoT services. Firstly, we present an energy consumption calculation method (ECCM) based on WSNs. Then, using the CUDA (Compute Unified Device Architecture) Programming model, we propose a Two-level Parallel Optimization Model (TLPOM) which exploits reasonable resource planning and common compiler optimization techniques to obtain the best blocks and threads configuration considering the resource constraints of each node. The key to this part is dynamic coupling Thread-Level Parallelism (TLP) and Instruction-Level Parallelism (ILP) to improve the performance of the algorithms without additional energy consumption. Finally, combining the ECCM and the TLPOM, we use the Reliable GPU Cluster Architecture (RGCA) to obtain a high-reliability computing system considering the nodes' diversity, algorithm characteristics, etc. The results show that the performance of the algorithms significantly increased by 34.1%, 33.96% and 24.07% for Fermi, Kepler and Maxwell on average with TLPOM and the RGCA ensures that our IoT computing system provides low-cost and high-reliability services.

Two-stage atlas subset selection in multi-atlas based image segmentation.

PubMed

Zhao, Tingting; Ruan, Dan

2015-06-01

Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas subset selection scheme for multi-atlas based segmentation. It achieves good segmentation accuracy with significantly reduced computation cost, making it a suitable configuration in the presence of extensive heterogeneous atlases.

Cloud Computing Adoption and Usage in Community Colleges

ERIC Educational Resources Information Center

Behrend, Tara S.; Wiebe, Eric N.; London, Jennifer E.; Johnson, Emily C.

2011-01-01

Cloud computing is gaining popularity in higher education settings, but the costs and benefits of this tool have gone largely unexplored. The purpose of this study was to examine the factors that lead to technology adoption in a higher education setting. Specifically, we examined a range of predictors and outcomes relating to the acceptance of a…

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

Assessing the use of computers in industrial occupational health departments.

PubMed

Owen, J P

1995-04-01

Computers are widely used in business and industry and the benefits of computerizing occupational health (OH) departments have been advocated by several authors. The requirements for successful computerization of an OH department are reviewed. Having identified the theoretical benefits, the real picture in industry is assessed by surveying 52 firms with over 1000 employees in a large urban area. Only 15 (29%) of the companies reported having any OH service, of which six used computers in the OH department, reflecting the business priorities of most of the companies. The types of software systems used and their main use are examined, along with perceived benefits or disadvantages. With the decreasing costs of computers and increasingly 'user-friendly' software, there is a real cost benefit to be gained from using computers in OH departments, although the concept may have to be 'sold' to management.

An evaluation of multi-probe locality sensitive hashing for computing similarities over web-scale query logs

PubMed Central

2018-01-01

Many modern applications of AI such as web search, mobile browsing, image processing, and natural language processing rely on finding similar items from a large database of complex objects. Due to the very large scale of data involved (e.g., users’ queries from commercial search engines), computing such near or nearest neighbors is a non-trivial task, as the computational cost grows significantly with the number of items. To address this challenge, we adopt Locality Sensitive Hashing (a.k.a, LSH) methods and evaluate four variants in a distributed computing environment (specifically, Hadoop). We identify several optimizations which improve performance, suitable for deployment in very large scale settings. The experimental results demonstrate our variants of LSH achieve the robust performance with better recall compared with “vanilla” LSH, even when using the same amount of space. PMID:29346410

A Parallel Sliding Region Algorithm to Make Agent-Based Modeling Possible for a Large-Scale Simulation: Modeling Hepatitis C Epidemics in Canada.

PubMed

Wong, William W L; Feng, Zeny Z; Thein, Hla-Hla

2016-11-01

Agent-based models (ABMs) are computer simulation models that define interactions among agents and simulate emergent behaviors that arise from the ensemble of local decisions. ABMs have been increasingly used to examine trends in infectious disease epidemiology. However, the main limitation of ABMs is the high computational cost for a large-scale simulation. To improve the computational efficiency for large-scale ABM simulations, we built a parallelizable sliding region algorithm (SRA) for ABM and compared it to a nonparallelizable ABM. We developed a complex agent network and performed two simulations to model hepatitis C epidemics based on the real demographic data from Saskatchewan, Canada. The first simulation used the SRA that processed on each postal code subregion subsequently. The second simulation processed the entire population simultaneously. It was concluded that the parallelizable SRA showed computational time saving with comparable results in a province-wide simulation. Using the same method, SRA can be generalized for performing a country-wide simulation. Thus, this parallel algorithm enables the possibility of using ABM for large-scale simulation with limited computational resources.

Current state and future direction of computer systems at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Rogers, James L. (Editor); Tucker, Jerry H. (Editor)

1992-01-01

Computer systems have advanced at a rate unmatched by any other area of technology. As performance has dramatically increased there has been an equally dramatic reduction in cost. This constant cost performance improvement has precipitated the pervasiveness of computer systems into virtually all areas of technology. This improvement is due primarily to advances in microelectronics. Most people are now convinced that the new generation of supercomputers will be built using a large number (possibly thousands) of high performance microprocessors. Although the spectacular improvements in computer systems have come about because of these hardware advances, there has also been a steady improvement in software techniques. In an effort to understand how these hardware and software advances will effect research at NASA LaRC, the Computer Systems Technical Committee drafted this white paper to examine the current state and possible future directions of computer systems at the Center. This paper discusses selected important areas of computer systems including real-time systems, embedded systems, high performance computing, distributed computing networks, data acquisition systems, artificial intelligence, and visualization.

Effect of turbulence modelling to predict combustion and nanoparticle production in the flame assisted spray dryer based on computational fluid dynamics

NASA Astrophysics Data System (ADS)

Septiani, Eka Lutfi; Widiyastuti, W.; Winardi, Sugeng; Machmudah, Siti; Nurtono, Tantular; Kusdianto

2016-02-01

Flame assisted spray dryer are widely uses for large-scale production of nanoparticles because of it ability. Numerical approach is needed to predict combustion and particles production in scale up and optimization process due to difficulty in experimental observation and relatively high cost. Computational Fluid Dynamics (CFD) can provide the momentum, energy and mass transfer, so that CFD more efficient than experiment due to time and cost. Here, two turbulence models, k-ɛ and Large Eddy Simulation were compared and applied in flame assisted spray dryer system. The energy sources for particle drying was obtained from combustion between LPG as fuel and air as oxidizer and carrier gas that modelled by non-premixed combustion in simulation. Silica particles was used to particle modelling from sol silica solution precursor. From the several comparison result, i.e. flame contour, temperature distribution and particle size distribution, Large Eddy Simulation turbulence model can provide the closest data to the experimental result.

Numerical Propulsion System Simulation (NPSS) 1999 Industry Review

NASA Technical Reports Server (NTRS)

Lytle, John; Follen, Greg; Naiman, Cynthia; Evans, Austin

2000-01-01

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with industry, academia, and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This concept is called the Numerical Propulsion System Simulation (NPSS). NPSS consists of three main elements: (1) engineering models that enable multidisciplinary analysis of large subsystems and systems at various levels of detail, (2) a simulation environment that maximizes designer productivity, and (3) a cost-effective, high-performance computing platform. A fundamental requirement of the concept is that the simulations must be capable of overnight execution on easily accessible computing platforms. This will greatly facilitate the use of large-scale simulations in a design environment. This paper describes the current status of the NPSS with specific emphasis on the progress made over the past year on air breathing propulsion applications. In addition, the paper contains a summary of the feedback received from industry partners in the development effort and the actions taken over the past year to respond to that feedback. The NPSS development was supported in FY99 by the High Performance Computing and Communications Program.

Crowdtruth validation: a new paradigm for validating algorithms that rely on image correspondences.

PubMed

Maier-Hein, Lena; Kondermann, Daniel; Roß, Tobias; Mersmann, Sven; Heim, Eric; Bodenstedt, Sebastian; Kenngott, Hannes Götz; Sanchez, Alexandro; Wagner, Martin; Preukschas, Anas; Wekerle, Anna-Laura; Helfert, Stefanie; März, Keno; Mehrabi, Arianeb; Speidel, Stefanie; Stock, Christian

2015-08-01

Feature tracking and 3D surface reconstruction are key enabling techniques to computer-assisted minimally invasive surgery. One of the major bottlenecks related to training and validation of new algorithms is the lack of large amounts of annotated images that fully capture the wide range of anatomical/scene variance in clinical practice. To address this issue, we propose a novel approach to obtaining large numbers of high-quality reference image annotations at low cost in an extremely short period of time. The concept is based on outsourcing the correspondence search to a crowd of anonymous users from an online community (crowdsourcing) and comprises four stages: (1) feature detection, (2) correspondence search via crowdsourcing, (3) merging multiple annotations per feature by fitting Gaussian finite mixture models, (4) outlier removal using the result of the clustering as input for a second annotation task. On average, 10,000 annotations were obtained within 24 h at a cost of $100. The annotation of the crowd after clustering and before outlier removal was of expert quality with a median distance of about 1 pixel to a publically available reference annotation. The threshold for the outlier removal task directly determines the maximum annotation error, but also the number of points removed. Our concept is a novel and effective method for fast, low-cost and highly accurate correspondence generation that could be adapted to various other applications related to large-scale data annotation in medical image computing and computer-assisted interventions.

A hybrid computational strategy to address WGS variant analysis in >5000 samples.

PubMed

Huang, Zhuoyi; Rustagi, Navin; Veeraraghavan, Narayanan; Carroll, Andrew; Gibbs, Richard; Boerwinkle, Eric; Venkata, Manjunath Gorentla; Yu, Fuli

2016-09-10

The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies. We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms. Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scalable, cost effective and fast manner by using heterogeneous computing platforms without compromising on the quality of variants.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

Dash, Tirtharaj; Sahu, Prabhat K

2015-05-30

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.

Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics - a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)

NASA Astrophysics Data System (ADS)

Jamroz, Benjamin F.; Klöfkorn, Robert

2016-08-01

The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement non-blocking asynchronous communication in the High-Order Methods Modeling Environment for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. This allows the overlap of computation with communication, effectively hiding some of the costs of communication. A novel detail about our approach is that it provides some data movement to be performed during the asynchronous communication even in the absence of other computations. This method produces significant performance and scalability gains in large-scale simulations.

Simulation of turbulent separated flows using a novel, evolution-based, eddy-viscosity formulation

NASA Astrophysics Data System (ADS)

Castellucci, Paul

Currently, there exists a lack of confidence in the computational simulation of turbulent separated flows at large Reynolds numbers. The most accurate methods available are too computationally costly to use in engineering applications. Thus, inexpensive models, developed using the Reynolds-averaged Navier-Stokes (RANS) equations, are often extended beyond their applicability. Although these methods will often reproduce integrated quantities within engineering tolerances, such metrics are often insensitive to details within a separated wake, and therefore, poor indicators of simulation fidelity. Using concepts borrowed from large-eddy simulation (LES), a two-equation RANS model is modified to simulate the turbulent wake behind a circular cylinder. This modification involves the computation of one additional scalar field, adding very little to the overall computational cost. When properly inserted into the baseline RANS model, this modification mimics LES in the separated wake, yet reverts to the unmodified form at the cylinder surface. In this manner, superior predictive capability may be achieved without the additional cost of fine spatial resolution associated with LES near solid boundaries. Simulations using modified and baseline RANS models are benchmarked against both LES and experimental data for a circular cylinder wake at Reynolds number 3900. In addition, the computational tool used in this investigation is subject to verification via the Method of Manufactured Solutions. Post-processing of the resultant flow fields includes both mean value and triple-decomposition analysis. These results reveal substantial improvements using the modified system and appear to drive the baseline wake solution toward that of LES, as intended.

Long range Debye-Hückel correction for computation of grid-based electrostatic forces between biomacromolecules

PubMed Central

2014-01-01

Background Brownian dynamics (BD) simulations can be used to study very large molecular systems, such as models of the intracellular environment, using atomic-detail structures. Such simulations require strategies to contain the computational costs, especially for the computation of interaction forces and energies. A common approach is to compute interaction forces between macromolecules by precomputing their interaction potentials on three-dimensional discretized grids. For long-range interactions, such as electrostatics, grid-based methods are subject to finite size errors. We describe here the implementation of a Debye-Hückel correction to the grid-based electrostatic potential used in the SDA BD simulation software that was applied to simulate solutions of bovine serum albumin and of hen egg white lysozyme. Results We found that the inclusion of the long-range electrostatic correction increased the accuracy of both the protein-protein interaction profiles and the protein diffusion coefficients at low ionic strength. Conclusions An advantage of this method is the low additional computational cost required to treat long-range electrostatic interactions in large biomacromolecular systems. Moreover, the implementation described here for BD simulations of protein solutions can also be applied in implicit solvent molecular dynamics simulations that make use of gridded interaction potentials. PMID:25045516

Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques

USGS Publications Warehouse

Gaydos, Leonard

1978-01-01

The cost of classifying 5,607 square kilometers (2,165 sq. mi.) in the Portland area was less than 8 cents per square kilometer ($0.0788, or $0.2041 per square mile). Besides saving in costs, this and other signature extension techniques may be useful in completing land use and land cover mapping in other large areas where multispectral and multitemporal Landsat data are available in digital form but other source materials are generally lacking.

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.

Building A Community Focused Data and Modeling Collaborative platform with Hardware Virtualization Technology

NASA Astrophysics Data System (ADS)

Michaelis, A.; Wang, W.; Melton, F. S.; Votava, P.; Milesi, C.; Hashimoto, H.; Nemani, R. R.; Hiatt, S. H.

2009-12-01

As the length and diversity of the global earth observation data records grow, modeling and analyses of biospheric conditions increasingly requires multiple terabytes of data from a diversity of models and sensors. With network bandwidth beginning to flatten, transmission of these data from centralized data archives presents an increasing challenge, and costs associated with local storage and management of data and compute resources are often significant for individual research and application development efforts. Sharing community valued intermediary data sets, results and codes from individual efforts with others that are not in direct funded collaboration can also be a challenge with respect to time, cost and expertise. We purpose a modeling, data and knowledge center that houses NASA satellite data, climate data and ancillary data where a focused community may come together to share modeling and analysis codes, scientific results, knowledge and expertise on a centralized platform, named Ecosystem Modeling Center (EMC). With the recent development of new technologies for secure hardware virtualization, an opportunity exists to create specific modeling, analysis and compute environments that are customizable, “archiveable” and transferable. Allowing users to instantiate such environments on large compute infrastructures that are directly connected to large data archives may significantly reduce costs and time associated with scientific efforts by alleviating users from redundantly retrieving and integrating data sets and building modeling analysis codes. The EMC platform also provides the possibility for users receiving indirect assistance from expertise through prefabricated compute environments, potentially reducing study “ramp up” times.

A Logically Centralized Approach for Control and Management of Large Computer Networks

ERIC Educational Resources Information Center

Iqbal, Hammad A.

2012-01-01

Management of large enterprise and Internet service provider networks is a complex, error-prone, and costly challenge. It is widely accepted that the key contributors to this complexity are the bundling of control and data forwarding in traditional routers and the use of fully distributed protocols for network control. To address these…

A hierarchical storage management (HSM) scheme for cost-effective on-line archival using lossy compression.

PubMed

Avrin, D E; Andriole, K P; Yin, L; Gould, R G; Arenson, R L

2001-03-01

A hierarchical storage management (HSM) scheme for cost-effective on-line archival of image data using lossy compression is described. This HSM scheme also provides an off-site tape backup mechanism and disaster recovery. The full-resolution image data are viewed originally for primary diagnosis, then losslessly compressed and sent off site to a tape backup archive. In addition, the original data are wavelet lossy compressed (at approximately 25:1 for computed radiography, 10:1 for computed tomography, and 5:1 for magnetic resonance) and stored on a large RAID device for maximum cost-effective, on-line storage and immediate retrieval of images for review and comparison. This HSM scheme provides a solution to 4 problems in image archiving, namely cost-effective on-line storage, disaster recovery of data, off-site tape backup for the legal record, and maximum intermediate storage and retrieval through the use of on-site lossy compression.

Parametric Study of a YAV-8B Harrier in Ground Effect Using Time-Dependent Navier-Stokes Computations

NASA Technical Reports Server (NTRS)

Shishir, Pandya; Chaderjian, Neal; Ahmad, Jsaim; Kwak, Dochan (Technical Monitor)

2001-01-01

Flow simulations using the time-dependent Navier-Stokes equations remain a challenge for several reasons. Principal among them are the difficulty to accurately model complex flows, and the time needed to perform the computations. A parametric study of such complex problems is not considered practical due to the large cost associated with computing many time-dependent solutions. The computation time for each solution must be reduced in order to make a parametric study possible. With successful reduction of computation time, the issue of accuracy, and appropriateness of turbulence models will become more tractable.

Power monitoring and control for large scale projects: SKA, a case study

NASA Astrophysics Data System (ADS)

Barbosa, Domingos; Barraca, João. Paulo; Maia, Dalmiro; Carvalho, Bruno; Vieira, Jorge; Swart, Paul; Le Roux, Gerhard; Natarajan, Swaminathan; van Ardenne, Arnold; Seca, Luis

2016-07-01

Large sensor-based science infrastructures for radio astronomy like the SKA will be among the most intensive datadriven projects in the world, facing very high demanding computation, storage, management, and above all power demands. The geographically wide distribution of the SKA and its associated processing requirements in the form of tailored High Performance Computing (HPC) facilities, require a Greener approach towards the Information and Communications Technologies (ICT) adopted for the data processing to enable operational compliance to potentially strict power budgets. Addressing the reduction of electricity costs, improve system power monitoring and the generation and management of electricity at system level is paramount to avoid future inefficiencies and higher costs and enable fulfillments of Key Science Cases. Here we outline major characteristics and innovation approaches to address power efficiency and long-term power sustainability for radio astronomy projects, focusing on Green ICT for science and Smart power monitoring and control.

Finite-difference modeling with variable grid-size and adaptive time-step in porous media

NASA Astrophysics Data System (ADS)

Liu, Xinxin; Yin, Xingyao; Wu, Guochen

2014-04-01

Forward modeling of elastic wave propagation in porous media has great importance for understanding and interpreting the influences of rock properties on characteristics of seismic wavefield. However, the finite-difference forward-modeling method is usually implemented with global spatial grid-size and time-step; it consumes large amounts of computational cost when small-scaled oil/gas-bearing structures or large velocity-contrast exist underground. To overcome this handicap, combined with variable grid-size and time-step, this paper developed a staggered-grid finite-difference scheme for elastic wave modeling in porous media. Variable finite-difference coefficients and wavefield interpolation were used to realize the transition of wave propagation between regions of different grid-size. The accuracy and efficiency of the algorithm were shown by numerical examples. The proposed method is advanced with low computational cost in elastic wave simulation for heterogeneous oil/gas reservoirs.

A unified RANS–LES model: Computational development, accuracy and cost

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

Gopalan, Harish, E-mail: hgopalan@uwyo.edu; Heinz, Stefan, E-mail: heinz@uwyo.edu; Stöllinger, Michael K., E-mail: MStoell@uwyo.edu

2013-09-15

Large eddy simulation (LES) is computationally extremely expensive for the investigation of wall-bounded turbulent flows at high Reynolds numbers. A way to reduce the computational cost of LES by orders of magnitude is to combine LES equations with Reynolds-averaged Navier–Stokes (RANS) equations used in the near-wall region. A large variety of such hybrid RANS–LES methods are currently in use such that there is the question of which hybrid RANS-LES method represents the optimal approach. The properties of an optimal hybrid RANS–LES model are formulated here by taking reference to fundamental properties of fluid flow equations. It is shown that unifiedmore » RANS–LES models derived from an underlying stochastic turbulence model have the properties of optimal hybrid RANS–LES models. The rest of the paper is organized in two parts. First, a priori and a posteriori analyses of channel flow data are used to find the optimal computational formulation of the theoretically derived unified RANS–LES model and to show that this computational model, which is referred to as linear unified model (LUM), does also have all the properties of an optimal hybrid RANS–LES model. Second, a posteriori analyses of channel flow data are used to study the accuracy and cost features of the LUM. The following conclusions are obtained. (i) Compared to RANS, which require evidence for their predictions, the LUM has the significant advantage that the quality of predictions is relatively independent of the RANS model applied. (ii) Compared to LES, the significant advantage of the LUM is a cost reduction of high-Reynolds number simulations by a factor of 0.07Re{sup 0.46}. For coarse grids, the LUM has a significant accuracy advantage over corresponding LES. (iii) Compared to other usually applied hybrid RANS–LES models, it is shown that the LUM provides significantly improved predictions.« less

Accelerating epistasis analysis in human genetics with consumer graphics hardware.

PubMed

Sinnott-Armstrong, Nicholas A; Greene, Casey S; Cancare, Fabio; Moore, Jason H

2009-07-24

Human geneticists are now capable of measuring more than one million DNA sequence variations from across the human genome. The new challenge is to develop computationally feasible methods capable of analyzing these data for associations with common human disease, particularly in the context of epistasis. Epistasis describes the situation where multiple genes interact in a complex non-linear manner to determine an individual's disease risk and is thought to be ubiquitous for common diseases. Multifactor Dimensionality Reduction (MDR) is an algorithm capable of detecting epistasis. An exhaustive analysis with MDR is often computationally expensive, particularly for high order interactions. This challenge has previously been met with parallel computation and expensive hardware. The option we examine here exploits commodity hardware designed for computer graphics. In modern computers Graphics Processing Units (GPUs) have more memory bandwidth and computational capability than Central Processing Units (CPUs) and are well suited to this problem. Advances in the video game industry have led to an economy of scale creating a situation where these powerful components are readily available at very low cost. Here we implement and evaluate the performance of the MDR algorithm on GPUs. Of primary interest are the time required for an epistasis analysis and the price to performance ratio of available solutions. We found that using MDR on GPUs consistently increased performance per machine over both a feature rich Java software package and a C++ cluster implementation. The performance of a GPU workstation running a GPU implementation reduces computation time by a factor of 160 compared to an 8-core workstation running the Java implementation on CPUs. This GPU workstation performs similarly to 150 cores running an optimized C++ implementation on a Beowulf cluster. Furthermore this GPU system provides extremely cost effective performance while leaving the CPU available for other tasks. The GPU workstation containing three GPUs costs $2000 while obtaining similar performance on a Beowulf cluster requires 150 CPU cores which, including the added infrastructure and support cost of the cluster system, cost approximately $82,500. Graphics hardware based computing provides a cost effective means to perform genetic analysis of epistasis using MDR on large datasets without the infrastructure of a computing cluster.

A Probabilistic Collocation Based Iterative Kalman Filter for Landfill Data Assimilation

NASA Astrophysics Data System (ADS)

Qiang, Z.; Zeng, L.; Wu, L.

2016-12-01

Due to the strong spatial heterogeneity of landfill, uncertainty is ubiquitous in gas transport process in landfill. To accurately characterize the landfill properties, the ensemble Kalman filter (EnKF) has been employed to assimilate the measurements, e.g., the gas pressure. As a Monte Carlo (MC) based method, the EnKF usually requires a large ensemble size, which poses a high computational cost for large scale problems. In this work, we propose a probabilistic collocation based iterative Kalman filter (PCIKF) to estimate permeability in a liquid-gas coupling model. This method employs polynomial chaos expansion (PCE) to represent and propagate the uncertainties of model parameters and states, and an iterative form of Kalman filter to assimilate the current gas pressure data. To further reduce the computation cost, the functional ANOVA (analysis of variance) decomposition is conducted, and only the first order ANOVA components are remained for PCE. Illustrated with numerical case studies, this proposed method shows significant superiority in computation efficiency compared with the traditional MC based iterative EnKF. The developed method has promising potential in reliable prediction and management of landfill gas production.

Fast and accurate genotype imputation in genome-wide association studies through pre-phasing

PubMed Central

Howie, Bryan; Fuchsberger, Christian; Stephens, Matthew; Marchini, Jonathan; Abecasis, Gonçalo R.

2013-01-01

Sequencing efforts, including the 1000 Genomes Project and disease-specific efforts, are producing large collections of haplotypes that can be used for genotype imputation in genome-wide association studies (GWAS). Imputing from these reference panels can help identify new risk alleles, but the use of large panels with existing methods imposes a high computational burden. To keep imputation broadly accessible, we introduce a strategy called “pre-phasing” that maintains the accuracy of leading methods while cutting computational costs by orders of magnitude. In brief, we first statistically estimate the haplotypes for each GWAS individual (“pre-phasing”) and then impute missing genotypes into these estimated haplotypes. This reduces the computational cost because: (i) the GWAS samples must be phased only once, whereas standard methods would implicitly re-phase with each reference panel update; (ii) it is much faster to match a phased GWAS haplotype to one reference haplotype than to match unphased GWAS genotypes to a pair of reference haplotypes. This strategy will be particularly valuable for repeated imputation as reference panels evolve. PMID:22820512

Semi-exact concentric atomic density fitting: Reduced cost and increased accuracy compared to standard density fitting

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

Hollman, David S.; Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061; Schaefer, Henry F.

2014-02-14

A local density fitting scheme is considered in which atomic orbital (AO) products are approximated using only auxiliary AOs located on one of the nuclei in that product. The possibility of variational collapse to an unphysical “attractive electron” state that can affect such density fitting [P. Merlot, T. Kjærgaard, T. Helgaker, R. Lindh, F. Aquilante, S. Reine, and T. B. Pedersen, J. Comput. Chem. 34, 1486 (2013)] is alleviated by including atom-wise semidiagonal integrals exactly. Our approach leads to a significant decrease in the computational cost of density fitting for Hartree–Fock theory while still producing results with errors 2–5 timesmore » smaller than standard, nonlocal density fitting. Our method allows for large Hartree–Fock and density functional theory computations with exact exchange to be carried out efficiently on large molecules, which we demonstrate by benchmarking our method on 200 of the most widely used prescription drug molecules. Our new fitting scheme leads to smooth and artifact-free potential energy surfaces and the possibility of relatively simple analytic gradients.« less

Trends in computer hardware and software.

PubMed

Frankenfeld, F M

1993-04-01

Previously identified and current trends in the development of computer systems and in the use of computers for health care applications are reviewed. Trends identified in a 1982 article were increasing miniaturization and archival ability, increasing software costs, increasing software independence, user empowerment through new software technologies, shorter computer-system life cycles, and more rapid development and support of pharmaceutical services. Most of these trends continue today. Current trends in hardware and software include the increasing use of reduced instruction-set computing, migration to the UNIX operating system, the development of large software libraries, microprocessor-based smart terminals that allow remote validation of data, speech synthesis and recognition, application generators, fourth-generation languages, computer-aided software engineering, object-oriented technologies, and artificial intelligence. Current trends specific to pharmacy and hospitals are the withdrawal of vendors of hospital information systems from the pharmacy market, improved linkage of information systems within hospitals, and increased regulation by government. The computer industry and its products continue to undergo dynamic change. Software development continues to lag behind hardware, and its high cost is offsetting the savings provided by hardware.

Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)

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

Jamroz, Benjamin F.; Klofkorn, Robert

The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement non-blocking asynchronous communication in the High-Order Methods Modeling Environment for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. This allows the overlap of computation with communication, effectively hiding some of the costs of communication. A novel detail about our approach is that it provides some data movement to be performed during the asynchronous communication even in the absence of other computations. This method produces significant performance and scalability gains in large-scalemore » simulations.« less

Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)

DOE PAGES

Jamroz, Benjamin F.; Klofkorn, Robert

2016-08-26

The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement non-blocking asynchronous communication in the High-Order Methods Modeling Environment for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. This allows the overlap of computation with communication, effectively hiding some of the costs of communication. A novel detail about our approach is that it provides some data movement to be performed during the asynchronous communication even in the absence of other computations. This method produces significant performance and scalability gains in large-scalemore » simulations.« less

A new impedance accounting for short- and long-range effects in mixed substructured formulations of nonlinear problems

NASA Astrophysics Data System (ADS)

Negrello, Camille; Gosselet, Pierre; Rey, Christian

2018-05-01

An efficient method for solving large nonlinear problems combines Newton solvers and Domain Decomposition Methods (DDM). In the DDM framework, the boundary conditions can be chosen to be primal, dual or mixed. The mixed approach presents the advantage to be eligible for the research of an optimal interface parameter (often called impedance) which can increase the convergence rate. The optimal value for this parameter is often too expensive to be computed exactly in practice: an approximate version has to be sought for, along with a compromise between efficiency and computational cost. In the context of parallel algorithms for solving nonlinear structural mechanical problems, we propose a new heuristic for the impedance which combines short and long range effects at a low computational cost.

JPRS Report, Science & Technology, China.

DTIC Science & Technology

1992-12-08

impor- tance of the computer information industry to the develop- ment of the national economy and the people’s standard of living. Forecasts call...past several years, and the application of computers has permeated every trade and industry , providing powerful SCIENCE & TECHNOLOGY POLICY JPRS...system and ample human talent; market potential is large; and it has potential for low cost develop- ment. However, the scale of its industrial

A locally p-adaptive approach for Large Eddy Simulation of compressible flows in a DG framework

NASA Astrophysics Data System (ADS)

Tugnoli, Matteo; Abbà, Antonella; Bonaventura, Luca; Restelli, Marco

2017-11-01

We investigate the possibility of reducing the computational burden of LES models by employing local polynomial degree adaptivity in the framework of a high-order DG method. A novel degree adaptation technique especially featured to be effective for LES applications is proposed and its effectiveness is compared to that of other criteria already employed in the literature. The resulting locally adaptive approach allows to achieve significant reductions in computational cost of representative LES computations.

Atlas2 Cloud: a framework for personal genome analysis in the cloud

PubMed Central

2012-01-01

Background Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. Results We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. Conclusions We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms. PMID:23134663

Atlas2 Cloud: a framework for personal genome analysis in the cloud.

PubMed

Evani, Uday S; Challis, Danny; Yu, Jin; Jackson, Andrew R; Paithankar, Sameer; Bainbridge, Matthew N; Jakkamsetti, Adinarayana; Pham, Peter; Coarfa, Cristian; Milosavljevic, Aleksandar; Yu, Fuli

2012-01-01

Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.

Accelerating Dust Storm Simulation by Balancing Task Allocation in Parallel Computing Environment

NASA Astrophysics Data System (ADS)

Gui, Z.; Yang, C.; XIA, J.; Huang, Q.; YU, M.

2013-12-01

Dust storm has serious negative impacts on environment, human health, and assets. The continuing global climate change has increased the frequency and intensity of dust storm in the past decades. To better understand and predict the distribution, intensity and structure of dust storm, a series of dust storm models have been developed, such as Dust Regional Atmospheric Model (DREAM), the NMM meteorological module (NMM-dust) and Chinese Unified Atmospheric Chemistry Environment for Dust (CUACE/Dust). The developments and applications of these models have contributed significantly to both scientific research and our daily life. However, dust storm simulation is a data and computing intensive process. Normally, a simulation for a single dust storm event may take several days or hours to run. It seriously impacts the timeliness of prediction and potential applications. To speed up the process, high performance computing is widely adopted. By partitioning a large study area into small subdomains according to their geographic location and executing them on different computing nodes in a parallel fashion, the computing performance can be significantly improved. Since spatiotemporal correlations exist in the geophysical process of dust storm simulation, each subdomain allocated to a node need to communicate with other geographically adjacent subdomains to exchange data. Inappropriate allocations may introduce imbalance task loads and unnecessary communications among computing nodes. Therefore, task allocation method is the key factor, which may impact the feasibility of the paralleling. The allocation algorithm needs to carefully leverage the computing cost and communication cost for each computing node to minimize total execution time and reduce overall communication cost for the entire system. This presentation introduces two algorithms for such allocation and compares them with evenly distributed allocation method. Specifically, 1) In order to get optimized solutions, a quadratic programming based modeling method is proposed. This algorithm performs well with small amount of computing tasks. However, its efficiency decreases significantly as the subdomain number and computing node number increase. 2) To compensate performance decreasing for large scale tasks, a K-Means clustering based algorithm is introduced. Instead of dedicating to get optimized solutions, this method can get relatively good feasible solutions within acceptable time. However, it may introduce imbalance communication for nodes or node-isolated subdomains. This research shows both two algorithms have their own strength and weakness for task allocation. A combination of the two algorithms is under study to obtain a better performance. Keywords: Scheduling; Parallel Computing; Load Balance; Optimization; Cost Model

Accelerating Large Scale Image Analyses on Parallel, CPU-GPU Equipped Systems

PubMed Central

Teodoro, George; Kurc, Tahsin M.; Pan, Tony; Cooper, Lee A.D.; Kong, Jun; Widener, Patrick; Saltz, Joel H.

2014-01-01

The past decade has witnessed a major paradigm shift in high performance computing with the introduction of accelerators as general purpose processors. These computing devices make available very high parallel computing power at low cost and power consumption, transforming current high performance platforms into heterogeneous CPU-GPU equipped systems. Although the theoretical performance achieved by these hybrid systems is impressive, taking practical advantage of this computing power remains a very challenging problem. Most applications are still deployed to either GPU or CPU, leaving the other resource under- or un-utilized. In this paper, we propose, implement, and evaluate a performance aware scheduling technique along with optimizations to make efficient collaborative use of CPUs and GPUs on a parallel system. In the context of feature computations in large scale image analysis applications, our evaluations show that intelligently co-scheduling CPUs and GPUs can significantly improve performance over GPU-only or multi-core CPU-only approaches. PMID:25419545

SparseMaps—A systematic infrastructure for reduced-scaling electronic structure methods. III. Linear-scaling multireference domain-based pair natural orbital N-electron valence perturbation theory

NASA Astrophysics Data System (ADS)

Guo, Yang; Sivalingam, Kantharuban; Valeev, Edward F.; Neese, Frank

2016-03-01

Multi-reference (MR) electronic structure methods, such as MR configuration interaction or MR perturbation theory, can provide reliable energies and properties for many molecular phenomena like bond breaking, excited states, transition states or magnetic properties of transition metal complexes and clusters. However, owing to their inherent complexity, most MR methods are still too computationally expensive for large systems. Therefore the development of more computationally attractive MR approaches is necessary to enable routine application for large-scale chemical systems. Among the state-of-the-art MR methods, second-order N-electron valence state perturbation theory (NEVPT2) is an efficient, size-consistent, and intruder-state-free method. However, there are still two important bottlenecks in practical applications of NEVPT2 to large systems: (a) the high computational cost of NEVPT2 for large molecules, even with moderate active spaces and (b) the prohibitive cost for treating large active spaces. In this work, we address problem (a) by developing a linear scaling "partially contracted" NEVPT2 method. This development uses the idea of domain-based local pair natural orbitals (DLPNOs) to form a highly efficient algorithm. As shown previously in the framework of single-reference methods, the DLPNO concept leads to an enormous reduction in computational effort while at the same time providing high accuracy (approaching 99.9% of the correlation energy), robustness, and black-box character. In the DLPNO approach, the virtual space is spanned by pair natural orbitals that are expanded in terms of projected atomic orbitals in large orbital domains, while the inactive space is spanned by localized orbitals. The active orbitals are left untouched. Our implementation features a highly efficient "electron pair prescreening" that skips the negligible inactive pairs. The surviving pairs are treated using the partially contracted NEVPT2 formalism. A detailed comparison between the partial and strong contraction schemes is made, with conclusions that discourage the strong contraction scheme as a basis for local correlation methods due to its non-invariance with respect to rotations in the inactive and external subspaces. A minimal set of conservatively chosen truncation thresholds controls the accuracy of the method. With the default thresholds, about 99.9% of the canonical partially contracted NEVPT2 correlation energy is recovered while the crossover of the computational cost with the already very efficient canonical method occurs reasonably early; in linear chain type compounds at a chain length of around 80 atoms. Calculations are reported for systems with more than 300 atoms and 5400 basis functions.

GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service.

PubMed

Wang, Yiqi; Li, Gen; Ma, Mark; He, Fazhong; Song, Zhuo; Zhang, Wei; Wu, Chengkun

2018-01-19

Whole-genome sequencing (WGS) plays an increasingly important role in clinical practice and public health. Due to the big data size, WGS data analysis is usually compute-intensive and IO-intensive. Currently it usually takes 30 to 40 h to finish a 50× WGS analysis task, which is far from the ideal speed required by the industry. Furthermore, the high-end infrastructure required by WGS computing is costly in terms of time and money. In this paper, we aim to improve the time efficiency of WGS analysis and minimize the cost by elastic cloud computing. We developed a distributed system, GT-WGS, for large-scale WGS analyses utilizing the Amazon Web Services (AWS). Our system won the first prize on the Wind and Cloud challenge held by Genomics and Cloud Technology Alliance conference (GCTA) committee. The system makes full use of the dynamic pricing mechanism of AWS. We evaluate the performance of GT-WGS with a 55× WGS dataset (400GB fastq) provided by the GCTA 2017 competition. In the best case, it only took 18.4 min to finish the analysis and the AWS cost of the whole process is only 16.5 US dollars. The accuracy of GT-WGS is 99.9% consistent with that of the Genome Analysis Toolkit (GATK) best practice. We also evaluated the performance of GT-WGS performance on a real-world dataset provided by the XiangYa hospital, which consists of 5× whole-genome dataset with 500 samples, and on average GT-WGS managed to finish one 5× WGS analysis task in 2.4 min at a cost of $3.6. WGS is already playing an important role in guiding therapeutic intervention. However, its application is limited by the time cost and computing cost. GT-WGS excelled as an efficient and affordable WGS analyses tool to address this problem. The demo video and supplementary materials of GT-WGS can be accessed at https://github.com/Genetalks/wgs_analysis_demo .

Multigrid preconditioned conjugate-gradient method for large-scale wave-front reconstruction.

PubMed

Gilles, Luc; Vogel, Curtis R; Ellerbroek, Brent L

2002-09-01

We introduce a multigrid preconditioned conjugate-gradient (MGCG) iterative scheme for computing open-loop wave-front reconstructors for extreme adaptive optics systems. We present numerical simulations for a 17-m class telescope with n = 48756 sensor measurement grid points within the aperture, which indicate that our MGCG method has a rapid convergence rate for a wide range of subaperture average slope measurement signal-to-noise ratios. The total computational cost is of order n log n. Hence our scheme provides for fast wave-front simulation and control in large-scale adaptive optics systems.

Rapid insights from remote sensing in the geosciences

NASA Astrophysics Data System (ADS)

Plaza, Antonio

2015-03-01

The growing availability of capacity computing for atomistic materials modeling has encouraged the use of high-accuracy computationally intensive interatomic potentials, such as SNAP. These potentials also happen to scale well on petascale computing platforms. SNAP has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The computational cost per atom is much greater than that of simpler potentials such as Lennard-Jones or EAM, while the communication cost remains modest. We discuss a variety of strategies for implementing SNAP in the LAMMPS molecular dynamics package. We present scaling results obtained running SNAP on three different classes of machine: a conventional Intel Xeon CPU cluster; the Titan GPU-based system; and the combined Sequoia and Vulcan BlueGene/Q. The growing availability of capacity computing for atomistic materials modeling has encouraged the use of high-accuracy computationally intensive interatomic potentials, such as SNAP. These potentials also happen to scale well on petascale computing platforms. SNAP has a very general form and uses machine-learning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected on to a basis of hyperspherical harmonics in four dimensions. The computational cost per atom is much greater than that of simpler potentials such as Lennard-Jones or EAM, while the communication cost remains modest. We discuss a variety of strategies for implementing SNAP in the LAMMPS molecular dynamics package. We present scaling results obtained running SNAP on three different classes of machine: a conventional Intel Xeon CPU cluster; the Titan GPU-based system; and the combined Sequoia and Vulcan BlueGene/Q. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corp., for the U.S. Dept. of Energy's National Nuclear Security Admin. under Contract DE-AC04-94AL85000.

VHSIC Electronics and the Cost of Air Force Avionics in the 1990s

DTIC Science & Technology

1990-11-01

circuit. LRM Line replaceable module. LRU Line replaceable unit. LSI Large-scale integration. LSTTL Tow-power Schottky Transitor -to-Transistor Logic...displays, communications/navigation/identification, electronic combat equipment, dispensers, and computers. These CERs, which statistically relate the...some of the reliability numbers, and adding the F-15 and F-16 to obtain the data sample shown in Table 6. Both suite costs and reliability statistics

Inexpensive and Highly Reproducible Cloud-Based Variant Calling of 2,535 Human Genomes

PubMed Central

Shringarpure, Suyash S.; Carroll, Andrew; De La Vega, Francisco M.; Bustamante, Carlos D.

2015-01-01

Population scale sequencing of whole human genomes is becoming economically feasible; however, data management and analysis remains a formidable challenge for many research groups. Large sequencing studies, like the 1000 Genomes Project, have improved our understanding of human demography and the effect of rare genetic variation in disease. Variant calling on datasets of hundreds or thousands of genomes is time-consuming, expensive, and not easily reproducible given the myriad components of a variant calling pipeline. Here, we describe a cloud-based pipeline for joint variant calling in large samples using the Real Time Genomics population caller. We deployed the population caller on the Amazon cloud with the DNAnexus platform in order to achieve low-cost variant calling. Using our pipeline, we were able to identify 68.3 million variants in 2,535 samples from Phase 3 of the 1000 Genomes Project. By performing the variant calling in a parallel manner, the data was processed within 5 days at a compute cost of $7.33 per sample (a total cost of $18,590 for completed jobs and $21,805 for all jobs). Analysis of cost dependence and running time on the data size suggests that, given near linear scalability, cloud computing can be a cheap and efficient platform for analyzing even larger sequencing studies in the future. PMID:26110529

Two-stage atlas subset selection in multi-atlas based image segmentation

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

Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu

2015-06-15

Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stagemore » atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors have developed a novel two-stage atlas subset selection scheme for multi-atlas based segmentation. It achieves good segmentation accuracy with significantly reduced computation cost, making it a suitable configuration in the presence of extensive heterogeneous atlases.« less

Extreme-Scale De Novo Genome Assembly

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

Georganas, Evangelos; Hofmeyr, Steven; Egan, Rob

De novo whole genome assembly reconstructs genomic sequence from short, overlapping, and potentially erroneous DNA segments and is one of the most important computations in modern genomics. This work presents HipMER, a high-quality end-to-end de novo assembler designed for extreme scale analysis, via efficient parallelization of the Meraculous code. Genome assembly software has many components, each of which stresses different components of a computer system. This chapter explains the computational challenges involved in each step of the HipMer pipeline, the key distributed data structures, and communication costs in detail. We present performance results of assembling the human genome and themore » large hexaploid wheat genome on large supercomputers up to tens of thousands of cores.« less

Cost-effective use of minicomputers to solve structural problems

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Foster, E. P.

1978-01-01

Minicomputers are receiving increased use throughout the aerospace industry. Until recently, their use focused primarily on process control and numerically controlled tooling applications, while their exposure to and the opportunity for structural calculations has been limited. With the increased availability of this computer hardware, the question arises as to the feasibility and practicality of carrying out comprehensive structural analysis on a minicomputer. This paper presents results on the potential for using minicomputers for structural analysis by (1) selecting a comprehensive, finite-element structural analysis system in use on large mainframe computers; (2) implementing the system on a minicomputer; and (3) comparing the performance of the minicomputers with that of a large mainframe computer for the solution to a wide range of finite element structural analysis problems.

Computational solutions to large-scale data management and analysis

PubMed Central

Schadt, Eric E.; Linderman, Michael D.; Sorenson, Jon; Lee, Lawrence; Nolan, Garry P.

2011-01-01

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist — such as cloud and heterogeneous computing — to successfully tackle our big data problems. PMID:20717155

Efficient tiled calculation of over-10-gigapixel holograms using ray-wavefront conversion.

PubMed

Igarashi, Shunsuke; Nakamura, Tomoya; Matsushima, Kyoji; Yamaguchi, Masahiro

2018-04-16

In the calculation of large-scale computer-generated holograms, an approach called "tiling," which divides the hologram plane into small rectangles, is often employed due to limitations on computational memory. However, the total amount of computational complexity severely increases with the number of divisions. In this paper, we propose an efficient method for calculating tiled large-scale holograms using ray-wavefront conversion. In experiments, the effectiveness of the proposed method was verified by comparing its calculation cost with that using the previous method. Additionally, a hologram of 128K × 128K pixels was calculated and fabricated by a laser-lithography system, and a high-quality 105 mm × 105 mm 3D image including complicated reflection and translucency was optically reconstructed.

Signal and image processing algorithm performance in a virtual and elastic computing environment

NASA Astrophysics Data System (ADS)

Bennett, Kelly W.; Robertson, James

2013-05-01

The U.S. Army Research Laboratory (ARL) supports the development of classification, detection, tracking, and localization algorithms using multiple sensing modalities including acoustic, seismic, E-field, magnetic field, PIR, and visual and IR imaging. Multimodal sensors collect large amounts of data in support of algorithm development. The resulting large amount of data, and their associated high-performance computing needs, increases and challenges existing computing infrastructures. Purchasing computer power as a commodity using a Cloud service offers low-cost, pay-as-you-go pricing models, scalability, and elasticity that may provide solutions to develop and optimize algorithms without having to procure additional hardware and resources. This paper provides a detailed look at using a commercial cloud service provider, such as Amazon Web Services (AWS), to develop and deploy simple signal and image processing algorithms in a cloud and run the algorithms on a large set of data archived in the ARL Multimodal Signatures Database (MMSDB). Analytical results will provide performance comparisons with existing infrastructure. A discussion on using cloud computing with government data will discuss best security practices that exist within cloud services, such as AWS.

Reducing computational costs in large scale 3D EIT by using a sparse Jacobian matrix with block-wise CGLS reconstruction.

PubMed

Yang, C L; Wei, H Y; Adler, A; Soleimani, M

2013-06-01

Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.

Does Cloud Computing in the Atmospheric Sciences Make Sense? A case study of hybrid cloud computing at NASA Langley Research Center

NASA Astrophysics Data System (ADS)

Nguyen, L.; Chee, T.; Minnis, P.; Spangenberg, D.; Ayers, J. K.; Palikonda, R.; Vakhnin, A.; Dubois, R.; Murphy, P. R.

2014-12-01

The processing, storage and dissemination of satellite cloud and radiation products produced at NASA Langley Research Center are key activities for the Climate Science Branch. A constellation of systems operates in sync to accomplish these goals. Because of the complexity involved with operating such intricate systems, there are both high failure rates and high costs for hardware and system maintenance. Cloud computing has the potential to ameliorate cost and complexity issues. Over time, the cloud computing model has evolved and hybrid systems comprising off-site as well as on-site resources are now common. Towards our mission of providing the highest quality research products to the widest audience, we have explored the use of the Amazon Web Services (AWS) Cloud and Storage and present a case study of our results and efforts. This project builds upon NASA Langley Cloud and Radiation Group's experience with operating large and complex computing infrastructures in a reliable and cost effective manner to explore novel ways to leverage cloud computing resources in the atmospheric science environment. Our case study presents the project requirements and then examines the fit of AWS with the LaRC computing model. We also discuss the evaluation metrics, feasibility, and outcomes and close the case study with the lessons we learned that would apply to others interested in exploring the implementation of the AWS system in their own atmospheric science computing environments.

The grammar of anger: Mapping the computational architecture of a recalibrational emotion.

PubMed

Sell, Aaron; Sznycer, Daniel; Al-Shawaf, Laith; Lim, Julian; Krauss, Andre; Feldman, Aneta; Rascanu, Ruxandra; Sugiyama, Lawrence; Cosmides, Leda; Tooby, John

2017-11-01

According to the recalibrational theory of anger, anger is a computationally complex cognitive system that evolved to bargain for better treatment. Anger coordinates facial expressions, vocal changes, verbal arguments, the withholding of benefits, the deployment of aggression, and a suite of other cognitive and physiological variables in the service of leveraging bargaining position into better outcomes. The prototypical trigger of anger is an indication that the offender places too little weight on the angry individual's welfare when making decisions, i.e. the offender has too low a welfare tradeoff ratio (WTR) toward the angry individual. Twenty-three experiments in six cultures, including a group of foragers in the Ecuadorian Amazon, tested six predictions about the computational structure of anger derived from the recalibrational theory. Subjects judged that anger would intensify when: (i) the cost was large, (ii) the benefit the offender received from imposing the cost was small, or (iii) the offender imposed the cost despite knowing that the angered individual was the person to be harmed. Additionally, anger-based arguments conformed to a conceptual grammar of anger, such that offenders were inclined to argue that they held a high WTR toward the victim, e.g., "the cost I imposed on you was small", "the benefit I gained was large", or "I didn't know it was you I was harming." These results replicated across all six tested cultures: the US, Australia, Turkey, Romania, India, and Shuar hunter-horticulturalists in Ecuador. Results contradict key predictions about anger based on equity theory and social constructivism. Copyright © 2017 Elsevier B.V. All rights reserved.

Scalable Domain Decomposed Monte Carlo Particle Transport

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

O'Brien, Matthew Joseph

2013-12-05

In this dissertation, we present the parallel algorithms necessary to run domain decomposed Monte Carlo particle transport on large numbers of processors (millions of processors). Previous algorithms were not scalable, and the parallel overhead became more computationally costly than the numerical simulation.

Data Center Consolidation: A Step towards Infrastructure Clouds

NASA Astrophysics Data System (ADS)

Winter, Markus

Application service providers face enormous challenges and rising costs in managing and operating a growing number of heterogeneous system and computing landscapes. Limitations of traditional computing environments force IT decision-makers to reorganize computing resources within the data center, as continuous growth leads to an inefficient utilization of the underlying hardware infrastructure. This paper discusses a way for infrastructure providers to improve data center operations based on the findings of a case study on resource utilization of very large business applications and presents an outlook beyond server consolidation endeavors, transforming corporate data centers into compute clouds.

Analysis and evaluation in the production process and equipment area of the low-cost solar array project

NASA Technical Reports Server (NTRS)

Wolf, M.

1981-01-01

The effect of solar cell metallization pattern design on solar cell performance and the costs and performance effects of different metallization processes are discussed. Definitive design rules for the front metallization pattern for large area solar cells are presented. Chemical and physical deposition processes for metallization are described and compared. An economic evaluation of the 6 principal metallization options is presented. Instructions for preparing Format A cost data for solar cell manufacturing processes from UPPC forms for input into the SAMIC computer program are presented.

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.

An Optimization Code for Nonlinear Transient Problems of a Large Scale Multidisciplinary Mathematical Model

NASA Astrophysics Data System (ADS)

Takasaki, Koichi

This paper presents a program for the multidisciplinary optimization and identification problem of the nonlinear model of large aerospace vehicle structures. The program constructs the global matrix of the dynamic system in the time direction by the p-version finite element method (pFEM), and the basic matrix for each pFEM node in the time direction is described by a sparse matrix similarly to the static finite element problem. The algorithm used by the program does not require the Hessian matrix of the objective function and so has low memory requirements. It also has a relatively low computational cost, and is suited to parallel computation. The program was integrated as a solver module of the multidisciplinary analysis system CUMuLOUS (Computational Utility for Multidisciplinary Large scale Optimization of Undense System) which is under development by the Aerospace Research and Development Directorate (ARD) of the Japan Aerospace Exploration Agency (JAXA).

A Grid Infrastructure for Supporting Space-based Science Operations

NASA Technical Reports Server (NTRS)

Bradford, Robert N.; Redman, Sandra H.; McNair, Ann R. (Technical Monitor)

2002-01-01

Emerging technologies for computational grid infrastructures have the potential for revolutionizing the way computers are used in all aspects of our lives. Computational grids are currently being implemented to provide a large-scale, dynamic, and secure research and engineering environments based on standards and next-generation reusable software, enabling greater science and engineering productivity through shared resources and distributed computing for less cost than traditional architectures. Combined with the emerging technologies of high-performance networks, grids provide researchers, scientists and engineers the first real opportunity for an effective distributed collaborative environment with access to resources such as computational and storage systems, instruments, and software tools and services for the most computationally challenging applications.

Large-scale high-throughput computer-aided discovery of advanced materials using cloud computing

NASA Astrophysics Data System (ADS)

Bazhirov, Timur; Mohammadi, Mohammad; Ding, Kevin; Barabash, Sergey

Recent advances in cloud computing made it possible to access large-scale computational resources completely on-demand in a rapid and efficient manner. When combined with high fidelity simulations, they serve as an alternative pathway to enable computational discovery and design of new materials through large-scale high-throughput screening. Here, we present a case study for a cloud platform implemented at Exabyte Inc. We perform calculations to screen lightweight ternary alloys for thermodynamic stability. Due to the lack of experimental data for most such systems, we rely on theoretical approaches based on first-principle pseudopotential density functional theory. We calculate the formation energies for a set of ternary compounds approximated by special quasirandom structures. During an example run we were able to scale to 10,656 CPUs within 7 minutes from the start, and obtain results for 296 compounds within 38 hours. The results indicate that the ultimate formation enthalpy of ternary systems can be negative for some of lightweight alloys, including Li and Mg compounds. We conclude that compared to traditional capital-intensive approach that requires in on-premises hardware resources, cloud computing is agile and cost-effective, yet scalable and delivers similar performance.

Resources and costs for microbial sequence analysis evaluated using virtual machines and cloud computing.

PubMed

Angiuoli, Samuel V; White, James R; Matalka, Malcolm; White, Owen; Fricke, W Florian

2011-01-01

The widespread popularity of genomic applications is threatened by the "bioinformatics bottleneck" resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers.

Resources and Costs for Microbial Sequence Analysis Evaluated Using Virtual Machines and Cloud Computing

PubMed Central

Angiuoli, Samuel V.; White, James R.; Matalka, Malcolm; White, Owen; Fricke, W. Florian

2011-01-01

Background The widespread popularity of genomic applications is threatened by the “bioinformatics bottleneck” resulting from uncertainty about the cost and infrastructure needed to meet increasing demands for next-generation sequence analysis. Cloud computing services have been discussed as potential new bioinformatics support systems but have not been evaluated thoroughly. Results We present benchmark costs and runtimes for common microbial genomics applications, including 16S rRNA analysis, microbial whole-genome shotgun (WGS) sequence assembly and annotation, WGS metagenomics and large-scale BLAST. Sequence dataset types and sizes were selected to correspond to outputs typically generated by small- to midsize facilities equipped with 454 and Illumina platforms, except for WGS metagenomics where sampling of Illumina data was used. Automated analysis pipelines, as implemented in the CloVR virtual machine, were used in order to guarantee transparency, reproducibility and portability across different operating systems, including the commercial Amazon Elastic Compute Cloud (EC2), which was used to attach real dollar costs to each analysis type. We found considerable differences in computational requirements, runtimes and costs associated with different microbial genomics applications. While all 16S analyses completed on a single-CPU desktop in under three hours, microbial genome and metagenome analyses utilized multi-CPU support of up to 120 CPUs on Amazon EC2, where each analysis completed in under 24 hours for less than $60. Representative datasets were used to estimate maximum data throughput on different cluster sizes and to compare costs between EC2 and comparable local grid servers. Conclusions Although bioinformatics requirements for microbial genomics depend on dataset characteristics and the analysis protocols applied, our results suggests that smaller sequencing facilities (up to three Roche/454 or one Illumina GAIIx sequencer) invested in 16S rRNA amplicon sequencing, microbial single-genome and metagenomics WGS projects can achieve cost-efficient bioinformatics support using CloVR in combination with Amazon EC2 as an alternative to local computing centers. PMID:22028928

Pre-Hardware Optimization of Spacecraft Image Processing Algorithms and Hardware Implementation

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Petrick, David J.; Flatley, Thomas P.; Hestnes, Phyllis; Jentoft-Nilsen, Marit; Day, John H. (Technical Monitor)

2002-01-01

Spacecraft telemetry rates and telemetry product complexity have steadily increased over the last decade presenting a problem for real-time processing by ground facilities. This paper proposes a solution to a related problem for the Geostationary Operational Environmental Spacecraft (GOES-8) image data processing and color picture generation application. Although large super-computer facilities are the obvious heritage solution, they are very costly, making it imperative to seek a feasible alternative engineering solution at a fraction of the cost. The proposed solution is based on a Personal Computer (PC) platform and synergy of optimized software algorithms, and reconfigurable computing hardware (RC) technologies, such as Field Programmable Gate Arrays (FPGA) and Digital Signal Processors (DSP). It has been shown that this approach can provide superior inexpensive performance for a chosen application on the ground station or on-board a spacecraft.

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.

Multi-strategy based quantum cost reduction of linear nearest-neighbor quantum circuit

NASA Astrophysics Data System (ADS)

Tan, Ying-ying; Cheng, Xue-yun; Guan, Zhi-jin; Liu, Yang; Ma, Haiying

2018-03-01

With the development of reversible and quantum computing, study of reversible and quantum circuits has also developed rapidly. Due to physical constraints, most quantum circuits require quantum gates to interact on adjacent quantum bits. However, many existing quantum circuits nearest-neighbor have large quantum cost. Therefore, how to effectively reduce quantum cost is becoming a popular research topic. In this paper, we proposed multiple optimization strategies to reduce the quantum cost of the circuit, that is, we reduce quantum cost from MCT gates decomposition, nearest neighbor and circuit simplification, respectively. The experimental results show that the proposed strategies can effectively reduce the quantum cost, and the maximum optimization rate is 30.61% compared to the corresponding results.

A Portable Computer System for Auditing Quality of Ambulatory Care

PubMed Central

McCoy, J. Michael; Dunn, Earl V.; Borgiel, Alexander E.

1987-01-01

Prior efforts to effectively and efficiently audit quality of ambulatory care based on comprehensive process criteria have been limited largely by the complexity and cost of data abstraction and management. Over the years, several demonstration projects have generated large sets of process criteria and mapping systems for evaluating quality of care, but these paper-based approaches have been impractical to implement on a routine basis. Recognizing that portable microcomputers could solve many of the technical problems in abstracting data from medical records, we built upon previously described criteria and developed a microcomputer-based abstracting system that facilitates reliable and cost-effective data abstraction.

Thin Client Architecture: The Promise and the Problems.

ERIC Educational Resources Information Center

Machovec, George S.

1997-01-01

Describes thin clients, a networking technology that allows organizations to provide software applications over networked workstations connected to a central server. Topics include corporate settings; major advantages, including cost effectiveness and increased computer security; problems; and possible applications for large public and academic…

Microcomputers in the Anesthesia Library.

ERIC Educational Resources Information Center

Wright, A. J.

The combination of computer technology and library operation is helping to alleviate such library problems as escalating costs, increasing collection size, deteriorating materials, unwieldy arrangement schemes, poor subject control, and the acquisition and processing of large numbers of rarely used documents. Small special libraries such as…

A Modeling Framework for Optimal Computational Resource Allocation Estimation: Considering the Trade-offs between Physical Resolutions, Uncertainty and Computational Costs

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.; Rajagopal, R.

2014-12-01

Hydrogeological models that represent flow and transport in subsurface domains are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting flow and transport in heterogeneous formations often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field representing hydrogeological characteristics of the field. The physical resolution (e.g. grid resolution associated with the physical space) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We propose an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model predictions and physical errors corresponding to numerical grid resolution. In this research, we optimally allocate computational resources by developing a modeling framework for the overall error based on a joint statistical and numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The accuracy of the proposed framework is verified in this study by applying it to several computationally extensive examples. Having this framework at hand aims hydrogeologists to achieve the optimum physical and statistical resolutions to minimize the error with a given computational budget. Moreover, the influence of the available computational resources and the geometric properties of the contaminant source zone on the optimum resolutions are investigated. We conclude that the computational cost associated with optimal allocation can be substantially reduced compared with prevalent recommendations in the literature.

Recruitment strategies for a clinical trial of community-based water therapy for osteoarthritis.

PubMed

Davey, Rachel; Edwards, Sarah Matthes; Cochrane, Tom

2003-04-01

This study compares the efficiency of two methods of recruitment into a randomised controlled trial examining the cost-effectiveness of water therapy for elderly people with lower limb osteoarthritis. The direct cost of recruiting patients via general practice was 27.66 Pounds per patient (1.1 personnel hours/patient). The cost per recruited patient from a local newspaper article was 2.72 Pounds (0.2 personnel hours/patient). The cost differential between the two recruitment methods was largely owing to poor administration practices, difficulties in accessing patient information, and difficulties in contacting patients from the general practice computer database.

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

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

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less

Cost effectiveness of a computer-delivered intervention to improve HIV medication adherence

PubMed Central

2013-01-01

Background High levels of adherence to medications for HIV infection are essential for optimal clinical outcomes and to reduce viral transmission, but many patients do not achieve required levels. Clinician-delivered interventions can improve patients’ adherence, but usually require substantial effort by trained individuals and may not be widely available. Computer-delivered interventions can address this problem by reducing required staff time for delivery and by making the interventions widely available via the Internet. We previously developed a computer-delivered intervention designed to improve patients’ level of health literacy as a strategy to improve their HIV medication adherence. The intervention was shown to increase patients’ adherence, but it was not clear that the benefits resulting from the increase in adherence could justify the costs of developing and deploying the intervention. The purpose of this study was to evaluate the relation of development and deployment costs to the effectiveness of the intervention. Methods Costs of intervention development were drawn from accounting reports for the grant under which its development was supported, adjusted for costs primarily resulting from the project’s research purpose. Effectiveness of the intervention was drawn from results of the parent study. The relation of the intervention’s effects to changes in health status, expressed as utilities, was also evaluated in order to assess the net cost of the intervention in terms of quality adjusted life years (QALYs). Sensitivity analyses evaluated ranges of possible intervention effectiveness and durations of its effects, and costs were evaluated over several deployment scenarios. Results The intervention’s cost effectiveness depends largely on the number of persons using it and the duration of its effectiveness. Even with modest effects for a small number of patients the intervention was associated with net cost savings in some scenarios and for durations greater than three months and longer it was usually associated with a favorable cost per QALY. For intermediate and larger assumed effects and longer durations of intervention effectiveness, the intervention was associated with net cost savings. Conclusions Computer-delivered adherence interventions may be a cost-effective strategy to improve adherence in persons treated for HIV. Trial registration Clinicaltrials.gov identifier NCT01304186. PMID:23446180

Cost effectiveness of a computer-delivered intervention to improve HIV medication adherence.

PubMed

Ownby, Raymond L; Waldrop-Valverde, Drenna; Jacobs, Robin J; Acevedo, Amarilis; Caballero, Joshua

2013-02-28

High levels of adherence to medications for HIV infection are essential for optimal clinical outcomes and to reduce viral transmission, but many patients do not achieve required levels. Clinician-delivered interventions can improve patients' adherence, but usually require substantial effort by trained individuals and may not be widely available. Computer-delivered interventions can address this problem by reducing required staff time for delivery and by making the interventions widely available via the Internet. We previously developed a computer-delivered intervention designed to improve patients' level of health literacy as a strategy to improve their HIV medication adherence. The intervention was shown to increase patients' adherence, but it was not clear that the benefits resulting from the increase in adherence could justify the costs of developing and deploying the intervention. The purpose of this study was to evaluate the relation of development and deployment costs to the effectiveness of the intervention. Costs of intervention development were drawn from accounting reports for the grant under which its development was supported, adjusted for costs primarily resulting from the project's research purpose. Effectiveness of the intervention was drawn from results of the parent study. The relation of the intervention's effects to changes in health status, expressed as utilities, was also evaluated in order to assess the net cost of the intervention in terms of quality adjusted life years (QALYs). Sensitivity analyses evaluated ranges of possible intervention effectiveness and durations of its effects, and costs were evaluated over several deployment scenarios. The intervention's cost effectiveness depends largely on the number of persons using it and the duration of its effectiveness. Even with modest effects for a small number of patients the intervention was associated with net cost savings in some scenarios and for durations greater than three months and longer it was usually associated with a favorable cost per QALY. For intermediate and larger assumed effects and longer durations of intervention effectiveness, the intervention was associated with net cost savings. Computer-delivered adherence interventions may be a cost-effective strategy to improve adherence in persons treated for HIV. Clinicaltrials.gov identifier NCT01304186.

Enhanced nonlinearity interval mapping scheme for high-performance simulation-optimization of watershed-scale BMP placement

NASA Astrophysics Data System (ADS)

Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn

2015-03-01

Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.

The cost of large numbers of hypothesis tests on power, effect size and sample size.

PubMed

Lazzeroni, L C; Ray, A

2012-01-01

Advances in high-throughput biology and computer science are driving an exponential increase in the number of hypothesis tests in genomics and other scientific disciplines. Studies using current genotyping platforms frequently include a million or more tests. In addition to the monetary cost, this increase imposes a statistical cost owing to the multiple testing corrections needed to avoid large numbers of false-positive results. To safeguard against the resulting loss of power, some have suggested sample sizes on the order of tens of thousands that can be impractical for many diseases or may lower the quality of phenotypic measurements. This study examines the relationship between the number of tests on the one hand and power, detectable effect size or required sample size on the other. We show that once the number of tests is large, power can be maintained at a constant level, with comparatively small increases in the effect size or sample size. For example at the 0.05 significance level, a 13% increase in sample size is needed to maintain 80% power for ten million tests compared with one million tests, whereas a 70% increase in sample size is needed for 10 tests compared with a single test. Relative costs are less when measured by increases in the detectable effect size. We provide an interactive Excel calculator to compute power, effect size or sample size when comparing study designs or genome platforms involving different numbers of hypothesis tests. The results are reassuring in an era of extreme multiple testing.

Optimizing Teleportation Cost in Distributed Quantum Circuits

NASA Astrophysics Data System (ADS)

Zomorodi-Moghadam, Mariam; Houshmand, Mahboobeh; Houshmand, Monireh

2018-03-01

The presented work provides a procedure for optimizing the communication cost of a distributed quantum circuit (DQC) in terms of the number of qubit teleportations. Because of technology limitations which do not allow large quantum computers to work as a single processing element, distributed quantum computation is an appropriate solution to overcome this difficulty. Previous studies have applied ad-hoc solutions to distribute a quantum system for special cases and applications. In this study, a general approach is proposed to optimize the number of teleportations for a DQC consisting of two spatially separated and long-distance quantum subsystems. To this end, different configurations of locations for executing gates whose qubits are in distinct subsystems are considered and for each of these configurations, the proposed algorithm is run to find the minimum number of required teleportations. Finally, the configuration which leads to the minimum number of teleportations is reported. The proposed method can be used as an automated procedure to find the configuration with the optimal communication cost for the DQC. This cost can be used as a basic measure of the communication cost for future works in the distributed quantum circuits.

Computational Issues in Damping Identification for Large Scale Problems

NASA Technical Reports Server (NTRS)

Pilkey, Deborah L.; Roe, Kevin P.; Inman, Daniel J.

1997-01-01

Two damping identification methods are tested for efficiency in large-scale applications. One is an iterative routine, and the other a least squares method. Numerical simulations have been performed on multiple degree-of-freedom models to test the effectiveness of the algorithm and the usefulness of parallel computation for the problems. High Performance Fortran is used to parallelize the algorithm. Tests were performed using the IBM-SP2 at NASA Ames Research Center. The least squares method tested incurs high communication costs, which reduces the benefit of high performance computing. This method's memory requirement grows at a very rapid rate meaning that larger problems can quickly exceed available computer memory. The iterative method's memory requirement grows at a much slower pace and is able to handle problems with 500+ degrees of freedom on a single processor. This method benefits from parallelization, and significant speedup can he seen for problems of 100+ degrees-of-freedom.

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

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.

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

DOE PAGES

McDaniel, Tyler; D’Azevedo, Ed F.; Li, Ying Wai; ...

2017-11-07

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is therefore formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with applicationmore » of accepted moves to the matrices delayed until after a predetermined number of moves, K. The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. Here this procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi- core CPUs and GPUs.« less

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

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

McDaniel, Tyler; D’Azevedo, Ed F.; Li, Ying Wai

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is therefore formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with applicationmore » of accepted moves to the matrices delayed until after a predetermined number of moves, K. The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. Here this procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi- core CPUs and GPUs.« less

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo.

PubMed

McDaniel, T; D'Azevedo, E F; Li, Y W; Wong, K; Kent, P R C

2017-11-07

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is, therefore, formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with an application of accepted moves to the matrices delayed until after a predetermined number of moves, K. The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. This procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo, where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi-core central processing units and graphical processing units.

Delayed Slater determinant update algorithms for high efficiency quantum Monte Carlo

NASA Astrophysics Data System (ADS)

McDaniel, T.; D'Azevedo, E. F.; Li, Y. W.; Wong, K.; Kent, P. R. C.

2017-11-01

Within ab initio Quantum Monte Carlo simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunction. Each Monte Carlo step requires finding the determinant of a dense matrix. This is most commonly iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. The overall computational cost is, therefore, formally cubic in the number of electrons or matrix size. To improve the numerical efficiency of this procedure, we propose a novel multiple rank delayed update scheme. This strategy enables probability evaluation with an application of accepted moves to the matrices delayed until after a predetermined number of moves, K. The accepted events are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency via matrix-matrix operations instead of matrix-vector operations. This procedure does not change the underlying Monte Carlo sampling or its statistical efficiency. For calculations on large systems and algorithms such as diffusion Monte Carlo, where the acceptance ratio is high, order of magnitude improvements in the update time can be obtained on both multi-core central processing units and graphical processing units.

An adaptive Gaussian process-based iterative ensemble smoother for data assimilation

NASA Astrophysics Data System (ADS)

Ju, Lei; Zhang, Jiangjiang; Meng, Long; Wu, Laosheng; Zeng, Lingzao

2018-05-01

Accurate characterization of subsurface hydraulic conductivity is vital for modeling of subsurface flow and transport. The iterative ensemble smoother (IES) has been proposed to estimate the heterogeneous parameter field. As a Monte Carlo-based method, IES requires a relatively large ensemble size to guarantee its performance. To improve the computational efficiency, we propose an adaptive Gaussian process (GP)-based iterative ensemble smoother (GPIES) in this study. At each iteration, the GP surrogate is adaptively refined by adding a few new base points chosen from the updated parameter realizations. Then the sensitivity information between model parameters and measurements is calculated from a large number of realizations generated by the GP surrogate with virtually no computational cost. Since the original model evaluations are only required for base points, whose number is much smaller than the ensemble size, the computational cost is significantly reduced. The applicability of GPIES in estimating heterogeneous conductivity is evaluated by the saturated and unsaturated flow problems, respectively. Without sacrificing estimation accuracy, GPIES achieves about an order of magnitude of speed-up compared with the standard IES. Although subsurface flow problems are considered in this study, the proposed method can be equally applied to other hydrological models.

The QSE-Reduced Nuclear Reaction Network for Silicon Burning

NASA Astrophysics Data System (ADS)

Hix, W. Raphael; Parete-Koon, Suzanne T.; Freiburghaus, Christian; Thielemann, Friedrich-Karl

2007-09-01

Iron and neighboring nuclei are formed in massive stars shortly before core collapse and during their supernova outbursts, as well as during thermonuclear supernovae. Complete and incomplete silicon burning are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present a new hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. This allows accurate prediction of the nuclear abundance evolution, deleptonization, and energy generation at a greatly reduced computational cost when compared to a conventional nuclear reaction network. During silicon burning, the resultant QSE-reduced network is approximately an order of magnitude faster than the full network it replaces and requires the tracking of less than a third as many abundance variables, without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multidimensional applications.

CELES: CUDA-accelerated simulation of electromagnetic scattering by large ensembles of spheres

NASA Astrophysics Data System (ADS)

Egel, Amos; Pattelli, Lorenzo; Mazzamuto, Giacomo; Wiersma, Diederik S.; Lemmer, Uli

2017-09-01

CELES is a freely available MATLAB toolbox to simulate light scattering by many spherical particles. Aiming at high computational performance, CELES leverages block-diagonal preconditioning, a lookup-table approach to evaluate costly functions and massively parallel execution on NVIDIA graphics processing units using the CUDA computing platform. The combination of these techniques allows to efficiently address large electrodynamic problems (>104 scatterers) on inexpensive consumer hardware. In this paper, we validate near- and far-field distributions against the well-established multi-sphere T-matrix (MSTM) code and discuss the convergence behavior for ensembles of different sizes, including an exemplary system comprising 105 particles.

Large-scale structural optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.

1983-01-01

Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.

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.

Gaussian polarizable-ion tight binding.

PubMed

Boleininger, Max; Guilbert, Anne Ay; Horsfield, Andrew P

2016-10-14

To interpret ultrafast dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous poly(3-hexylthiophene-2,5-diyl).

Gaussian polarizable-ion tight binding

NASA Astrophysics Data System (ADS)

Boleininger, Max; Guilbert, Anne AY; Horsfield, Andrew P.

2016-10-01

To interpret ultrafast dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous poly(3-hexylthiophene-2,5-diyl).

Using 3D infrared imaging to calibrate and refine computational fluid dynamic modeling for large computer and data centers

NASA Astrophysics Data System (ADS)

Stockton, Gregory R.

2011-05-01

Over the last 10 years, very large government, military, and commercial computer and data center operators have spent millions of dollars trying to optimally cool data centers as each rack has begun to consume as much as 10 times more power than just a few years ago. In fact, the maximum amount of data computation in a computer center is becoming limited by the amount of available power, space and cooling capacity at some data centers. Tens of millions of dollars and megawatts of power are being annually spent to keep data centers cool. The cooling and air flows dynamically change away from any predicted 3-D computational fluid dynamic modeling during construction and as time goes by, and the efficiency and effectiveness of the actual cooling rapidly departs even farther from predicted models. By using 3-D infrared (IR) thermal mapping and other techniques to calibrate and refine the computational fluid dynamic modeling and make appropriate corrections and repairs, the required power for data centers can be dramatically reduced which reduces costs and also improves reliability.

Security and Cloud Outsourcing Framework for Economic Dispatch

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

Sarker, Mushfiqur R.; Wang, Jianhui; Li, Zuyi

The computational complexity and problem sizes of power grid applications have increased significantly with the advent of renewable resources and smart grid technologies. The current paradigm of solving these issues consist of inhouse high performance computing infrastructures, which have drawbacks of high capital expenditures, maintenance, and limited scalability. Cloud computing is an ideal alternative due to its powerful computational capacity, rapid scalability, and high cost-effectiveness. A major challenge, however, remains in that the highly confidential grid data is susceptible for potential cyberattacks when outsourced to the cloud. In this work, a security and cloud outsourcing framework is developed for themore » Economic Dispatch (ED) linear programming application. As a result, the security framework transforms the ED linear program into a confidentiality-preserving linear program, that masks both the data and problem structure, thus enabling secure outsourcing to the cloud. Results show that for large grid test cases the performance gain and costs outperforms the in-house infrastructure.« less

Security and Cloud Outsourcing Framework for Economic Dispatch

DOE PAGES

Sarker, Mushfiqur R.; Wang, Jianhui; Li, Zuyi; ...

2017-04-24

The computational complexity and problem sizes of power grid applications have increased significantly with the advent of renewable resources and smart grid technologies. The current paradigm of solving these issues consist of inhouse high performance computing infrastructures, which have drawbacks of high capital expenditures, maintenance, and limited scalability. Cloud computing is an ideal alternative due to its powerful computational capacity, rapid scalability, and high cost-effectiveness. A major challenge, however, remains in that the highly confidential grid data is susceptible for potential cyberattacks when outsourced to the cloud. In this work, a security and cloud outsourcing framework is developed for themore » Economic Dispatch (ED) linear programming application. As a result, the security framework transforms the ED linear program into a confidentiality-preserving linear program, that masks both the data and problem structure, thus enabling secure outsourcing to the cloud. Results show that for large grid test cases the performance gain and costs outperforms the in-house infrastructure.« less

Streaming support for data intensive cloud-based sequence analysis.

PubMed

Issa, Shadi A; Kienzler, Romeo; El-Kalioby, Mohamed; Tonellato, Peter J; Wall, Dennis; Bruggmann, Rémy; Abouelhoda, Mohamed

2013-01-01

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of "resources-on-demand" and "pay-as-you-go", scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation.

Precision Parameter Estimation and Machine Learning

NASA Astrophysics Data System (ADS)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

Educational Technology--The White Elephant.

ERIC Educational Resources Information Center

Molnar, Andrew R.

A ten year experiment in educational technology sponsored under Title VII of the National Defense Education Act (NDEA) demonstrated the feasibility of large-scale educational systems which can extend education to all while permitting the individualization of instruction without significant increase in cost (through television, computer systems,…

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

On Convergence of Development Costs and Cost Models for Complex Spaceflight Instrument Electronics

NASA Technical Reports Server (NTRS)

Kizhner, Semion; Patel, Umeshkumar D.; Kasa, Robert L.; Hestnes, Phyllis; Brown, Tammy; Vootukuru, Madhavi

2008-01-01

Development costs of a few recent spaceflight instrument electrical and electronics subsystems have diverged from respective heritage cost model predictions. The cost models used are Grass Roots, Price-H and Parametric Model. These cost models originated in the military and industry around 1970 and were successfully adopted and patched by NASA on a mission-by-mission basis for years. However, the complexity of new instruments recently changed rapidly by orders of magnitude. This is most obvious in the complexity of representative spaceflight instrument electronics' data system. It is now required to perform intermediate processing of digitized data apart from conventional processing of science phenomenon signals from multiple detectors. This involves on-board instrument formatting of computational operands from row data for example, images), multi-million operations per second on large volumes of data in reconfigurable hardware (in addition to processing on a general purpose imbedded or standalone instrument flight computer), as well as making decisions for on-board system adaptation and resource reconfiguration. The instrument data system is now tasked to perform more functions, such as forming packets and instrument-level data compression of more than one data stream, which are traditionally performed by the spacecraft command and data handling system. It is furthermore required that the electronics box for new complex instruments is developed for one-digit watt power consumption, small size and that it is light-weight, and delivers super-computing capabilities. The conflict between the actual development cost of newer complex instruments and its electronics components' heritage cost model predictions seems to be irreconcilable. This conflict and an approach to its resolution are addressed in this paper by determining the complexity parameters, complexity index, and their use in enhanced cost model.

Exploiting Locality in Quantum Computation for Quantum Chemistry.

PubMed

McClean, Jarrod R; Babbush, Ryan; Love, Peter J; Aspuru-Guzik, Alán

2014-12-18

Accurate prediction of chemical and material properties from first-principles quantum chemistry is a challenging task on traditional computers. Recent developments in quantum computation offer a route toward highly accurate solutions with polynomial cost; however, this solution still carries a large overhead. In this Perspective, we aim to bring together known results about the locality of physical interactions from quantum chemistry with ideas from quantum computation. We show that the utilization of spatial locality combined with the Bravyi-Kitaev transformation offers an improvement in the scaling of known quantum algorithms for quantum chemistry and provides numerical examples to help illustrate this point. We combine these developments to improve the outlook for the future of quantum chemistry on quantum computers.

Efficient Control Law Simulation for Multiple Mobile Robots

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

Driessen, B.J.; Feddema, J.T.; Kotulski, J.D.

1998-10-06

In this paper we consider the problem of simulating simple control laws involving large numbers of mobile robots. Such simulation can be computationally prohibitive if the number of robots is large enough, say 1 million, due to the 0(N2 ) cost of each time step. This work therefore uses hierarchical tree-based methods for calculating the control law. These tree-based approaches have O(NlogN) cost per time step, thus allowing for efficient simulation involving a large number of robots. For concreteness, a decentralized control law which involves only the distance and bearing to the closest neighbor robot will be considered. The timemore » to calculate the control law for each robot at each time step is demonstrated to be O(logN).« less

Numerical propulsion system simulation

NASA Technical Reports Server (NTRS)

Lytle, John K.; Remaklus, David A.; Nichols, Lester D.

1990-01-01

The cost of implementing new technology in aerospace propulsion systems is becoming prohibitively expensive. One of the major contributors to the high cost is the need to perform many large scale system tests. Extensive testing is used to capture the complex interactions among the multiple disciplines and the multiple components inherent in complex systems. The objective of the Numerical Propulsion System Simulation (NPSS) is to provide insight into these complex interactions through computational simulations. This will allow for comprehensive evaluation of new concepts early in the design phase before a commitment to hardware is made. It will also allow for rapid assessment of field-related problems, particularly in cases where operational problems were encountered during conditions that would be difficult to simulate experimentally. The tremendous progress taking place in computational engineering and the rapid increase in computing power expected through parallel processing make this concept feasible within the near future. However it is critical that the framework for such simulations be put in place now to serve as a focal point for the continued developments in computational engineering and computing hardware and software. The NPSS concept which is described will provide that framework.

An atomic orbital based real-time time-dependent density functional theory for computing electronic circular dichroism band spectra

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

Goings, Joshua J.; Li, Xiaosong, E-mail: xsli@uw.edu

2016-06-21

One of the challenges of interpreting electronic circular dichroism (ECD) band spectra is that different states may have different rotatory strength signs, determined by their absolute configuration. If the states are closely spaced and opposite in sign, observed transitions may be washed out by nearby states, unlike absorption spectra where transitions are always positive additive. To accurately compute ECD bands, it is necessary to compute a large number of excited states, which may be prohibitively costly if one uses the linear-response time-dependent density functional theory (TDDFT) framework. Here we implement a real-time, atomic-orbital based TDDFT method for computing the entiremore » ECD spectrum simultaneously. The method is advantageous for large systems with a high density of states. In contrast to previous implementations based on real-space grids, the method is variational, independent of nuclear orientation, and does not rely on pseudopotential approximations, making it suitable for computation of chiroptical properties well into the X-ray regime.« less

SparseMaps—A systematic infrastructure for reduced-scaling electronic structure methods. III. Linear-scaling multireference domain-based pair natural orbital N-electron valence perturbation theory

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

Guo, Yang; Sivalingam, Kantharuban; Neese, Frank, E-mail: Frank.Neese@cec.mpg.de

2016-03-07

Multi-reference (MR) electronic structure methods, such as MR configuration interaction or MR perturbation theory, can provide reliable energies and properties for many molecular phenomena like bond breaking, excited states, transition states or magnetic properties of transition metal complexes and clusters. However, owing to their inherent complexity, most MR methods are still too computationally expensive for large systems. Therefore the development of more computationally attractive MR approaches is necessary to enable routine application for large-scale chemical systems. Among the state-of-the-art MR methods, second-order N-electron valence state perturbation theory (NEVPT2) is an efficient, size-consistent, and intruder-state-free method. However, there are still twomore » important bottlenecks in practical applications of NEVPT2 to large systems: (a) the high computational cost of NEVPT2 for large molecules, even with moderate active spaces and (b) the prohibitive cost for treating large active spaces. In this work, we address problem (a) by developing a linear scaling “partially contracted” NEVPT2 method. This development uses the idea of domain-based local pair natural orbitals (DLPNOs) to form a highly efficient algorithm. As shown previously in the framework of single-reference methods, the DLPNO concept leads to an enormous reduction in computational effort while at the same time providing high accuracy (approaching 99.9% of the correlation energy), robustness, and black-box character. In the DLPNO approach, the virtual space is spanned by pair natural orbitals that are expanded in terms of projected atomic orbitals in large orbital domains, while the inactive space is spanned by localized orbitals. The active orbitals are left untouched. Our implementation features a highly efficient “electron pair prescreening” that skips the negligible inactive pairs. The surviving pairs are treated using the partially contracted NEVPT2 formalism. A detailed comparison between the partial and strong contraction schemes is made, with conclusions that discourage the strong contraction scheme as a basis for local correlation methods due to its non-invariance with respect to rotations in the inactive and external subspaces. A minimal set of conservatively chosen truncation thresholds controls the accuracy of the method. With the default thresholds, about 99.9% of the canonical partially contracted NEVPT2 correlation energy is recovered while the crossover of the computational cost with the already very efficient canonical method occurs reasonably early; in linear chain type compounds at a chain length of around 80 atoms. Calculations are reported for systems with more than 300 atoms and 5400 basis functions.« less

SOMAR-LES: A framework for multi-scale modeling of turbulent stratified oceanic flows

NASA Astrophysics Data System (ADS)

Chalamalla, Vamsi K.; Santilli, Edward; Scotti, Alberto; Jalali, Masoud; Sarkar, Sutanu

2017-12-01

A new multi-scale modeling technique, SOMAR-LES, is presented in this paper. Localized grid refinement gives SOMAR (the Stratified Ocean Model with Adaptive Resolution) access to small scales of the flow which are normally inaccessible to general circulation models (GCMs). SOMAR-LES drives a LES (Large Eddy Simulation) on SOMAR's finest grids, forced with large scale forcing from the coarser grids. Three-dimensional simulations of internal tide generation, propagation and scattering are performed to demonstrate this multi-scale modeling technique. In the case of internal tide generation at a two-dimensional bathymetry, SOMAR-LES is able to balance the baroclinic energy budget and accurately model turbulence losses at only 10% of the computational cost required by a non-adaptive solver running at SOMAR-LES's fine grid resolution. This relative cost is significantly reduced in situations with intermittent turbulence or where the location of the turbulence is not known a priori because SOMAR-LES does not require persistent, global, high resolution. To illustrate this point, we consider a three-dimensional bathymetry with grids adaptively refined along the tidally generated internal waves to capture remote mixing in regions of wave focusing. The computational cost in this case is found to be nearly 25 times smaller than that of a non-adaptive solver at comparable resolution. In the final test case, we consider the scattering of a mode-1 internal wave at an isolated two-dimensional and three-dimensional topography, and we compare the results with Legg (2014) numerical experiments. We find good agreement with theoretical estimates. SOMAR-LES is less dissipative than the closure scheme employed by Legg (2014) near the bathymetry. Depending on the flow configuration and resolution employed, a reduction of more than an order of magnitude in computational costs is expected, relative to traditional existing solvers.

A generic, cost-effective, and scalable cell lineage analysis platform

PubMed Central

Biezuner, Tamir; Spiro, Adam; Raz, Ofir; Amir, Shiran; Milo, Lilach; Adar, Rivka; Chapal-Ilani, Noa; Berman, Veronika; Fried, Yael; Ainbinder, Elena; Cohen, Galit; Barr, Haim M.; Halaban, Ruth; Shapiro, Ehud

2016-01-01

Advances in single-cell genomics enable commensurate improvements in methods for uncovering lineage relations among individual cells. Current sequencing-based methods for cell lineage analysis depend on low-resolution bulk analysis or rely on extensive single-cell sequencing, which is not scalable and could be biased by functional dependencies. Here we show an integrated biochemical-computational platform for generic single-cell lineage analysis that is retrospective, cost-effective, and scalable. It consists of a biochemical-computational pipeline that inputs individual cells, produces targeted single-cell sequencing data, and uses it to generate a lineage tree of the input cells. We validated the platform by applying it to cells sampled from an ex vivo grown tree and analyzed its feasibility landscape by computer simulations. We conclude that the platform may serve as a generic tool for lineage analysis and thus pave the way toward large-scale human cell lineage discovery. PMID:27558250

Design of automata theory of cubical complexes with applications to diagnosis and algorithmic description

NASA Technical Reports Server (NTRS)

Roth, J. P.

1972-01-01

The following problems are considered: (1) methods for development of logic design together with algorithms, so that it is possible to compute a test for any failure in the logic design, if such a test exists, and developing algorithms and heuristics for the purpose of minimizing the computation for tests; and (2) a method of design of logic for ultra LSI (large scale integration). It was discovered that the so-called quantum calculus can be extended to render it possible: (1) to describe the functional behavior of a mechanism component by component, and (2) to compute tests for failures, in the mechanism, using the diagnosis algorithm. The development of an algorithm for the multioutput two-level minimization problem is presented and the program MIN 360 was written for this algorithm. The program has options of mode (exact minimum or various approximations), cost function, cost bound, etc., providing flexibility.

Benefits of cloud computing for PACS and archiving.

PubMed

Koch, Patrick

2012-01-01

The goal of cloud-based services is to provide easy, scalable access to computing resources and IT services. The healthcare industry requires a private cloud that adheres to government mandates designed to ensure privacy and security of patient data while enabling access by authorized users. Cloud-based computing in the imaging market has evolved from a service that provided cost effective disaster recovery for archived data to fully featured PACS and vendor neutral archiving services that can address the needs of healthcare providers of all sizes. Healthcare providers worldwide are now using the cloud to distribute images to remote radiologists while supporting advanced reading tools, deliver radiology reports and imaging studies to referring physicians, and provide redundant data storage. Vendor managed cloud services eliminate large capital investments in equipment and maintenance, as well as staffing for the data center--creating a reduction in total cost of ownership for the healthcare provider.

Evolutionary Telemetry and Command Processor (TCP) architecture

NASA Technical Reports Server (NTRS)

Schneider, John R.

1992-01-01

A low cost, modular, high performance, and compact Telemetry and Command Processor (TCP) is being built as the foundation of command and data handling subsystems for the next generation of satellites. The TCP product line will support command and telemetry requirements for small to large spacecraft and from low to high rate data transmission. It is compatible with the latest TDRSS, STDN and SGLS transponders and provides CCSDS protocol communications in addition to standard TDM formats. Its high performance computer provides computing resources for hosted flight software. Layered and modular software provides common services using standardized interfaces to applications thereby enhancing software re-use, transportability, and interoperability. The TCP architecture is based on existing standards, distributed networking, distributed and open system computing, and packet technology. The first TCP application is planned for the 94 SDIO SPAS 3 mission. The architecture enhances rapid tailoring of functions thereby reducing costs and schedules developed for individual spacecraft missions.

Permittivity and conductivity parameter estimations using full waveform inversion

NASA Astrophysics Data System (ADS)

Serrano, Jheyston O.; Ramirez, Ana B.; Abreo, Sergio A.; Sadler, Brian M.

2018-04-01

Full waveform inversion of Ground Penetrating Radar (GPR) data is a promising strategy to estimate quantitative characteristics of the subsurface such as permittivity and conductivity. In this paper, we propose a methodology that uses Full Waveform Inversion (FWI) in time domain of 2D GPR data to obtain highly resolved images of the permittivity and conductivity parameters of the subsurface. FWI is an iterative method that requires a cost function to measure the misfit between observed and modeled data, a wave propagator to compute the modeled data and an initial velocity model that is updated at each iteration until an acceptable decrease of the cost function is reached. The use of FWI with GPR are expensive computationally because it is based on the computation of the electromagnetic full wave propagation. Also, the commercially available acquisition systems use only one transmitter and one receiver antenna at zero offset, requiring a large number of shots to scan a single line.

Development and implementation of a low cost micro computer system for LANDSAT analysis and geographic data base applications

NASA Technical Reports Server (NTRS)

Faust, N.; Jordon, L.

1981-01-01

Since the implementation of the GRID and IMGRID computer programs for multivariate spatial analysis in the early 1970's, geographic data analysis subsequently moved from large computers to minicomputers and now to microcomputers with radical reduction in the costs associated with planning analyses. Programs designed to process LANDSAT data to be used as one element in a geographic data base were used once NIMGRID (new IMGRID), a raster oriented geographic information system, was implemented on the microcomputer. Programs for training field selection, supervised and unsupervised classification, and image enhancement were added. Enhancements to the color graphics capabilities of the microsystem allow display of three channels of LANDSAT data in color infrared format. The basic microcomputer hardware needed to perform NIMGRID and most LANDSAT analyses is listed as well as the software available for LANDSAT processing.

Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Lee, Jonghyun; Yoon, Hongkyu; Kitanidis, Peter K.; Werth, Charles J.; Valocchi, Albert J.

2016-07-01

Characterizing subsurface properties is crucial for reliable and cost-effective groundwater supply management and contaminant remediation. With recent advances in sensor technology, large volumes of hydrogeophysical and geochemical data can be obtained to achieve high-resolution images of subsurface properties. However, characterization with such a large amount of information requires prohibitive computational costs associated with "big data" processing and numerous large-scale numerical simulations. To tackle such difficulties, the principal component geostatistical approach (PCGA) has been proposed as a "Jacobian-free" inversion method that requires much smaller forward simulation runs for each iteration than the number of unknown parameters and measurements needed in the traditional inversion methods. PCGA can be conveniently linked to any multiphysics simulation software with independent parallel executions. In this paper, we extend PCGA to handle a large number of measurements (e.g., 106 or more) by constructing a fast preconditioner whose computational cost scales linearly with the data size. For illustration, we characterize the heterogeneous hydraulic conductivity (K) distribution in a laboratory-scale 3-D sand box using about 6 million transient tracer concentration measurements obtained using magnetic resonance imaging. Since each individual observation has little information on the K distribution, the data were compressed by the zeroth temporal moment of breakthrough curves, which is equivalent to the mean travel time under the experimental setting. Only about 2000 forward simulations in total were required to obtain the best estimate with corresponding estimation uncertainty, and the estimated K field captured key patterns of the original packing design, showing the efficiency and effectiveness of the proposed method.

Cost aware cache replacement policy in shared last-level cache for hybrid memory based fog computing

NASA Astrophysics Data System (ADS)

Jia, Gangyong; Han, Guangjie; Wang, Hao; Wang, Feng

2018-04-01

Fog computing requires a large main memory capacity to decrease latency and increase the Quality of Service (QoS). However, dynamic random access memory (DRAM), the commonly used random access memory, cannot be included into a fog computing system due to its high consumption of power. In recent years, non-volatile memories (NVM) such as Phase-Change Memory (PCM) and Spin-transfer torque RAM (STT-RAM) with their low power consumption have emerged to replace DRAM. Moreover, the currently proposed hybrid main memory, consisting of both DRAM and NVM, have shown promising advantages in terms of scalability and power consumption. However, the drawbacks of NVM, such as long read/write latency give rise to potential problems leading to asymmetric cache misses in the hybrid main memory. Current last level cache (LLC) policies are based on the unified miss cost, and result in poor performance in LLC and add to the cost of using NVM. In order to minimize the cache miss cost in the hybrid main memory, we propose a cost aware cache replacement policy (CACRP) that reduces the number of cache misses from NVM and improves the cache performance for a hybrid memory system. Experimental results show that our CACRP behaves better in LLC performance, improving performance up to 43.6% (15.5% on average) compared to LRU.

Portable parallel stochastic optimization for the design of aeropropulsion components

NASA Technical Reports Server (NTRS)

Sues, Robert H.; Rhodes, G. S.

1994-01-01

This report presents the results of Phase 1 research to develop a methodology for performing large-scale Multi-disciplinary Stochastic Optimization (MSO) for the design of aerospace systems ranging from aeropropulsion components to complete aircraft configurations. The current research recognizes that such design optimization problems are computationally expensive, and require the use of either massively parallel or multiple-processor computers. The methodology also recognizes that many operational and performance parameters are uncertain, and that uncertainty must be considered explicitly to achieve optimum performance and cost. The objective of this Phase 1 research was to initialize the development of an MSO methodology that is portable to a wide variety of hardware platforms, while achieving efficient, large-scale parallelism when multiple processors are available. The first effort in the project was a literature review of available computer hardware, as well as review of portable, parallel programming environments. The first effort was to implement the MSO methodology for a problem using the portable parallel programming language, Parallel Virtual Machine (PVM). The third and final effort was to demonstrate the example on a variety of computers, including a distributed-memory multiprocessor, a distributed-memory network of workstations, and a single-processor workstation. Results indicate the MSO methodology can be well-applied towards large-scale aerospace design problems. Nearly perfect linear speedup was demonstrated for computation of optimization sensitivity coefficients on both a 128-node distributed-memory multiprocessor (the Intel iPSC/860) and a network of workstations (speedups of almost 19 times achieved for 20 workstations). Very high parallel efficiencies (75 percent for 31 processors and 60 percent for 50 processors) were also achieved for computation of aerodynamic influence coefficients on the Intel. Finally, the multi-level parallelization strategy that will be needed for large-scale MSO problems was demonstrated to be highly efficient. The same parallel code instructions were used on both platforms, demonstrating portability. There are many applications for which MSO can be applied, including NASA's High-Speed-Civil Transport, and advanced propulsion systems. The use of MSO will reduce design and development time and testing costs dramatically.

Large-scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU).

PubMed

Shi, Yulin; Veidenbaum, Alexander V; Nicolau, Alex; Xu, Xiangmin

2015-01-15

Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post hoc processing and analysis. Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22× speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Large scale neural circuit mapping data analysis accelerated with the graphical processing unit (GPU)

PubMed Central

Shi, Yulin; Veidenbaum, Alexander V.; Nicolau, Alex; Xu, Xiangmin

2014-01-01

Background Modern neuroscience research demands computing power. Neural circuit mapping studies such as those using laser scanning photostimulation (LSPS) produce large amounts of data and require intensive computation for post-hoc processing and analysis. New Method Here we report on the design and implementation of a cost-effective desktop computer system for accelerated experimental data processing with recent GPU computing technology. A new version of Matlab software with GPU enabled functions is used to develop programs that run on Nvidia GPUs to harness their parallel computing power. Results We evaluated both the central processing unit (CPU) and GPU-enabled computational performance of our system in benchmark testing and practical applications. The experimental results show that the GPU-CPU co-processing of simulated data and actual LSPS experimental data clearly outperformed the multi-core CPU with up to a 22x speedup, depending on computational tasks. Further, we present a comparison of numerical accuracy between GPU and CPU computation to verify the precision of GPU computation. In addition, we show how GPUs can be effectively adapted to improve the performance of commercial image processing software such as Adobe Photoshop. Comparison with Existing Method(s) To our best knowledge, this is the first demonstration of GPU application in neural circuit mapping and electrophysiology-based data processing. Conclusions Together, GPU enabled computation enhances our ability to process large-scale data sets derived from neural circuit mapping studies, allowing for increased processing speeds while retaining data precision. PMID:25277633

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...

Volunteered Cloud Computing for Disaster Management

NASA Astrophysics Data System (ADS)

Evans, J. D.; Hao, W.; Chettri, S. R.

2014-12-01

Disaster management relies increasingly on interpreting earth observations and running numerical models; which require significant computing capacity - usually on short notice and at irregular intervals. Peak computing demand during event detection, hazard assessment, or incident response may exceed agency budgets; however some of it can be met through volunteered computing, which distributes subtasks to participating computers via the Internet. This approach has enabled large projects in mathematics, basic science, and climate research to harness the slack computing capacity of thousands of desktop computers. This capacity is likely to diminish as desktops give way to battery-powered mobile devices (laptops, smartphones, tablets) in the consumer market; but as cloud computing becomes commonplace, it may offer significant slack capacity -- if its users are given an easy, trustworthy mechanism for participating. Such a "volunteered cloud computing" mechanism would also offer several advantages over traditional volunteered computing: tasks distributed within a cloud have fewer bandwidth limitations; granular billing mechanisms allow small slices of "interstitial" computing at no marginal cost; and virtual storage volumes allow in-depth, reversible machine reconfiguration. Volunteered cloud computing is especially suitable for "embarrassingly parallel" tasks, including ones requiring large data volumes: examples in disaster management include near-real-time image interpretation, pattern / trend detection, or large model ensembles. In the context of a major disaster, we estimate that cloud users (if suitably informed) might volunteer hundreds to thousands of CPU cores across a large provider such as Amazon Web Services. To explore this potential, we are building a volunteered cloud computing platform and targeting it to a disaster management context. Using a lightweight, fault-tolerant network protocol, this platform helps cloud users join parallel computing projects; automates reconfiguration of their virtual machines; ensures accountability for donated computing; and optimizes the use of "interstitial" computing. Initial applications include fire detection from multispectral satellite imagery and flood risk mapping through hydrological simulations.

Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies

PubMed Central

Zhao, Shanrong; Prenger, Kurt; Smith, Lance

2013-01-01

RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets. PMID:25937948

Stormbow: A Cloud-Based Tool for Reads Mapping and Expression Quantification in Large-Scale RNA-Seq Studies.

PubMed

Zhao, Shanrong; Prenger, Kurt; Smith, Lance

2013-01-01

RNA-Seq is becoming a promising replacement to microarrays in transcriptome profiling and differential gene expression study. Technical improvements have decreased sequencing costs and, as a result, the size and number of RNA-Seq datasets have increased rapidly. However, the increasing volume of data from large-scale RNA-Seq studies poses a practical challenge for data analysis in a local environment. To meet this challenge, we developed Stormbow, a cloud-based software package, to process large volumes of RNA-Seq data in parallel. The performance of Stormbow has been tested by practically applying it to analyse 178 RNA-Seq samples in the cloud. In our test, it took 6 to 8 hours to process an RNA-Seq sample with 100 million reads, and the average cost was $3.50 per sample. Utilizing Amazon Web Services as the infrastructure for Stormbow allows us to easily scale up to handle large datasets with on-demand computational resources. Stormbow is a scalable, cost effective, and open-source based tool for large-scale RNA-Seq data analysis. Stormbow can be freely downloaded and can be used out of box to process Illumina RNA-Seq datasets.

Computed tomographic colonography to screen for colorectal cancer, extracolonic cancer, and aortic aneurysm: model simulation with cost-effectiveness analysis.

PubMed

Hassan, Cesare; Pickhardt, Perry J; Pickhardt, Perry; Laghi, Andrea; Kim, Daniel H; Kim, Daniel; Zullo, Angelo; Iafrate, Franco; Di Giulio, Lorenzo; Morini, Sergio

2008-04-14

In addition to detecting colorectal neoplasia, abdominal computed tomography (CT) with colonography technique (CTC) can also detect unsuspected extracolonic cancers and abdominal aortic aneurysms (AAA).The efficacy and cost-effectiveness of this combined abdominal CT screening strategy are unknown. A computerized Markov model was constructed to simulate the occurrence of colorectal neoplasia, extracolonic malignant neoplasm, and AAA in a hypothetical cohort of 100,000 subjects from the United States who were 50 years of age. Simulated screening with CTC, using a 6-mm polyp size threshold for reporting, was compared with a competing model of optical colonoscopy (OC), both without and with abdominal ultrasonography for AAA detection (OC-US strategy). In the simulated population, CTC was the dominant screening strategy, gaining an additional 1458 and 462 life-years compared with the OC and OC-US strategies and being less costly, with a savings of $266 and $449 per person, respectively. The additional gains for CTC were largely due to a decrease in AAA-related deaths, whereas the modeled benefit from extracolonic cancer downstaging was a relatively minor factor. At sensitivity analysis, OC-US became more cost-effective only when the CTC sensitivity for large polyps dropped to 61% or when broad variations of costs were simulated, such as an increase in CTC cost from $814 to $1300 or a decrease in OC cost from $1100 to $500. With the OC-US approach, suboptimal compliance had a strong negative influence on efficacy and cost-effectiveness. The estimated mortality from CT-induced cancer was less than estimated colonoscopy-related mortality (8 vs 22 deaths), both of which were minor compared with the positive benefit from screening. When detection of extracolonic findings such as AAA and extracolonic cancer are considered in addition to colorectal neoplasia in our model simulation, CT colonography is a dominant screening strategy (ie, more clinically effective and more cost-effective) over both colonoscopy and colonoscopy with 1-time ultrasonography.

Solving large-scale dynamic systems using band Lanczos method in Rockwell NASTRAN on CRAY X-MP

NASA Technical Reports Server (NTRS)

Gupta, V. K.; Zillmer, S. D.; Allison, R. E.

1986-01-01

The improved cost effectiveness using better models, more accurate and faster algorithms and large scale computing offers more representative dynamic analyses. The band Lanczos eigen-solution method was implemented in Rockwell's version of 1984 COSMIC-released NASTRAN finite element structural analysis computer program to effectively solve for structural vibration modes including those of large complex systems exceeding 10,000 degrees of freedom. The Lanczos vectors were re-orthogonalized locally using the Lanczos Method and globally using the modified Gram-Schmidt method for sweeping rigid-body modes and previously generated modes and Lanczos vectors. The truncated band matrix was solved for vibration frequencies and mode shapes using Givens rotations. Numerical examples are included to demonstrate the cost effectiveness and accuracy of the method as implemented in ROCKWELL NASTRAN. The CRAY version is based on RPK's COSMIC/NASTRAN. The band Lanczos method was more reliable and accurate and converged faster than the single vector Lanczos Method. The band Lanczos method was comparable to the subspace iteration method which was a block version of the inverse power method. However, the subspace matrix tended to be fully populated in the case of subspace iteration and not as sparse as a band matrix.

A Brief Description of the Kokkos implementation of the SNAP potential in ExaMiniMD.

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

Thompson, Aidan P.; Trott, Christian Robert

2017-11-01

Within the EXAALT project, the SNAP [1] approach is being used to develop high accuracy potentials for use in large-scale long-time molecular dynamics simulations of materials behavior. In particular, we have developed a new SNAP potential that is suitable for describing the interplay between helium atoms and vacancies in high-temperature tungsten[2]. This model is now being used to study plasma-surface interactions in nuclear fusion reactors for energy production. The high-accuracy of SNAP potentials comes at the price of increased computational cost per atom and increased computational complexity. The increased cost is mitigated by improvements in strong scaling that can bemore » achieved using advanced algorithms [3].« less

Advanced On-Board Processor (AOP). [for future spacecraft applications

NASA Technical Reports Server (NTRS)

1973-01-01

Advanced On-board Processor the (AOP) uses large scale integration throughout and is the most advanced space qualified computer of its class in existence today. It was designed to satisfy most spacecraft requirements which are anticipated over the next several years. The AOP design utilizes custom metallized multigate arrays (CMMA) which have been designed specifically for this computer. This approach provides the most efficient use of circuits, reduces volume, weight, assembly costs and provides for a significant increase in reliability by the significant reduction in conventional circuit interconnections. The required 69 CMMA packages are assembled on a single multilayer printed circuit board which together with associated connectors constitutes the complete AOP. This approach also reduces conventional interconnections thus further reducing weight, volume and assembly costs.

A strategy for improved computational efficiency of the method of anchored distributions

NASA Astrophysics Data System (ADS)

Over, Matthew William; Yang, Yarong; Chen, Xingyuan; Rubin, Yoram

2013-06-01

This paper proposes a strategy for improving the computational efficiency of model inversion using the method of anchored distributions (MAD) by "bundling" similar model parametrizations in the likelihood function. Inferring the likelihood function typically requires a large number of forward model (FM) simulations for each possible model parametrization; as a result, the process is quite expensive. To ease this prohibitive cost, we present an approximation for the likelihood function called bundling that relaxes the requirement for high quantities of FM simulations. This approximation redefines the conditional statement of the likelihood function as the probability of a set of similar model parametrizations "bundle" replicating field measurements, which we show is neither a model reduction nor a sampling approach to improving the computational efficiency of model inversion. To evaluate the effectiveness of these modifications, we compare the quality of predictions and computational cost of bundling relative to a baseline MAD inversion of 3-D flow and transport model parameters. Additionally, to aid understanding of the implementation we provide a tutorial for bundling in the form of a sample data set and script for the R statistical computing language. For our synthetic experiment, bundling achieved a 35% reduction in overall computational cost and had a limited negative impact on predicted probability distributions of the model parameters. Strategies for minimizing error in the bundling approximation, for enforcing similarity among the sets of model parametrizations, and for identifying convergence of the likelihood function are also presented.

Ride comfort control in large flexible aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Warren, M. E.

1971-01-01

The problem of ameliorating the discomfort of passengers on a large air transport subject to flight disturbances is examined. The longitudinal dynamics of the aircraft, including effects of body flexing, are developed in terms of linear, constant coefficient differential equations in state variables. A cost functional, penalizing the rigid body displacements and flexure accelerations over the surface of the aircraft is formulated as a quadratic form. The resulting control problem, to minimize the cost subject to the state equation constraints, is of a class whose solutions are well known. The feedback gains for the optimal controller are calculated digitally, and the resulting autopilot is simulated on an analog computer and its performance evaluated.

Optimization and large scale computation of an entropy-based moment closure

NASA Astrophysics Data System (ADS)

Kristopher Garrett, C.; Hauck, Cory; Hill, Judith

2015-12-01

We present computational advances and results in the implementation of an entropy-based moment closure, MN, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as PN, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which are used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. These results show, in particular, load balancing issues in scaling the MN algorithm that do not appear for the PN algorithm. We also observe that in weak scaling tests, the ratio in time to solution of MN to PN decreases.

Optimization and large scale computation of an entropy-based moment closure

DOE PAGES

Hauck, Cory D.; Hill, Judith C.; Garrett, C. Kristopher

2015-09-10

We present computational advances and results in the implementation of an entropy-based moment closure, M N, in the context of linear kinetic equations, with an emphasis on heterogeneous and large-scale computing platforms. Entropy-based closures are known in several cases to yield more accurate results than closures based on standard spectral approximations, such as P N, but the computational cost is generally much higher and often prohibitive. Several optimizations are introduced to improve the performance of entropy-based algorithms over previous implementations. These optimizations include the use of GPU acceleration and the exploitation of the mathematical properties of spherical harmonics, which aremore » used as test functions in the moment formulation. To test the emerging high-performance computing paradigm of communication bound simulations, we present timing results at the largest computational scales currently available. Lastly, these results show, in particular, load balancing issues in scaling the M N algorithm that do not appear for the P N algorithm. We also observe that in weak scaling tests, the ratio in time to solution of M N to P N decreases.« less

Transmitted wavefront testing with large dynamic range based on computer-aided deflectometry

NASA Astrophysics Data System (ADS)

Wang, Daodang; Xu, Ping; Gong, Zhidong; Xie, Zhongmin; Liang, Rongguang; Xu, Xinke; Kong, Ming; Zhao, Jun

2018-06-01

The transmitted wavefront testing technique is demanded for the performance evaluation of transmission optics and transparent glass, in which the achievable dynamic range is a key issue. A computer-aided deflectometric testing method with fringe projection is proposed for the accurate testing of transmitted wavefronts with a large dynamic range. Ray tracing of the modeled testing system is carried out to achieve the virtual ‘null’ testing of transmitted wavefront aberrations. The ray aberration is obtained from the ray tracing result and measured slope, with which the test wavefront aberration can be reconstructed. To eliminate testing system modeling errors, a system geometry calibration based on computer-aided reverse optimization is applied to realize accurate testing. Both numerical simulation and experiments have been carried out to demonstrate the feasibility and high accuracy of the proposed testing method. The proposed testing method can achieve a large dynamic range compared with the interferometric method, providing a simple, low-cost and accurate way for the testing of transmitted wavefronts from various kinds of optics and a large amount of industrial transmission elements.

Herd-Level Mastitis-Associated Costs on Canadian Dairy Farms

PubMed Central

Aghamohammadi, Mahjoob; Haine, Denis; Kelton, David F.; Barkema, Herman W.; Hogeveen, Henk; Keefe, Gregory P.; Dufour, Simon

2018-01-01

Mastitis imposes considerable and recurring economic losses on the dairy industry worldwide. The main objective of this study was to estimate herd-level costs incurred by expenditures and production losses associated with mastitis on Canadian dairy farms in 2015, based on producer reports. Previously, published mastitis economic frameworks were used to develop an economic model with the most important cost components. Components investigated were divided between clinical mastitis (CM), subclinical mastitis (SCM), and other costs components (i.e., preventive measures and product quality). A questionnaire was mailed to 374 dairy producers randomly selected from the (Canadian National Dairy Study 2015) to collect data on these costs components, and 145 dairy producers returned a completed questionnaire. For each herd, costs due to the different mastitis-related components were computed by applying the values reported by the dairy producer to the developed economic model. Then, for each herd, a proportion of the costs attributable to a specific component was computed by dividing absolute costs for this component by total herd mastitis-related costs. Median self-reported CM incidence was 19 cases/100 cow-year and mean self-reported bulk milk somatic cell count was 184,000 cells/mL. Most producers reported using post-milking teat disinfection (97%) and dry cow therapy (93%), and a substantial proportion of producers reported using pre-milking teat disinfection (79%) and wearing gloves during milking (77%). Mastitis costs were substantial (662 CAD per milking cow per year for a typical Canadian dairy farm), with a large portion of the costs (48%) being attributed to SCM, and 34 and 15% due to CM and implementation of preventive measures, respectively. For SCM, the two most important cost components were the subsequent milk yield reduction and culling (72 and 25% of SCM costs, respectively). For CM, first, second, and third most important cost components were culling (48% of CM costs), milk yield reduction following the CM events (34%), and discarded milk (11%), respectively. This study is the first since 1990 to investigate costs of mastitis in Canada. The model developed in the current study can be used to compute mastitis costs at the herd and national level in Canada. PMID:29868620

Cross-indexing of binary SIFT codes for large-scale image search.

PubMed

Liu, Zhen; Li, Houqiang; Zhang, Liyan; Zhou, Wengang; Tian, Qi

2014-05-01

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm.

Large-eddy simulation of a boundary layer with concave streamwise curvature

NASA Technical Reports Server (NTRS)

Lund, Thomas S.

1994-01-01

Turbulence modeling continues to be one of the most difficult problems in fluid mechanics. Existing prediction methods are well developed for certain classes of simple equilibrium flows, but are still not entirely satisfactory for a large category of complex non-equilibrium flows found in engineering practice. Direct and large-eddy simulation (LES) approaches have long been believed to have great potential for the accurate prediction of difficult turbulent flows, but the associated computational cost has been prohibitive for practical problems. This remains true for direct simulation but is no longer clear for large-eddy simulation. Advances in computer hardware, numerical methods, and subgrid-scale modeling have made it possible to conduct LES for flows or practical interest at Reynolds numbers in the range of laboratory experiments. The objective of this work is to apply ES and the dynamic subgrid-scale model to the flow of a boundary layer over a concave surface.

Computer-aided engineering of semiconductor integrated circuits

NASA Astrophysics Data System (ADS)

Meindl, J. D.; Dutton, R. W.; Gibbons, J. F.; Helms, C. R.; Plummer, J. D.; Tiller, W. A.; Ho, C. P.; Saraswat, K. C.; Deal, B. E.; Kamins, T. I.

1980-07-01

Economical procurement of small quantities of high performance custom integrated circuits for military systems is impeded by inadequate process, device and circuit models that handicap low cost computer aided design. The principal objective of this program is to formulate physical models of fabrication processes, devices and circuits to allow total computer-aided design of custom large-scale integrated circuits. The basic areas under investigation are (1) thermal oxidation, (2) ion implantation and diffusion, (3) chemical vapor deposition of silicon and refractory metal silicides, (4) device simulation and analytic measurements. This report discusses the fourth year of the program.

Developing and Implementing a Voluntary Personal Computer Program in the Business School: One College's Experience.

ERIC Educational Resources Information Center

Darter, Marvin E.; Wise, Donald E.

1989-01-01

Describes the experiences of Rider College School of Business Administration in implementing the use of microcomputers for courses in the business curriculum. Topics discussed include student purchase of microcomputers; cost effectiveness; software considerations; security for student equipment; printers; large screen projection facilities; and…

GATECloud.net: a platform for large-scale, open-source text processing on the cloud.

PubMed

Tablan, Valentin; Roberts, Ian; Cunningham, Hamish; Bontcheva, Kalina

2013-01-28

Cloud computing is increasingly being regarded as a key enabler of the 'democratization of science', because on-demand, highly scalable cloud computing facilities enable researchers anywhere to carry out data-intensive experiments. In the context of natural language processing (NLP), algorithms tend to be complex, which makes their parallelization and deployment on cloud platforms a non-trivial task. This study presents a new, unique, cloud-based platform for large-scale NLP research--GATECloud. net. It enables researchers to carry out data-intensive NLP experiments by harnessing the vast, on-demand compute power of the Amazon cloud. Important infrastructural issues are dealt with by the platform, completely transparently for the researcher: load balancing, efficient data upload and storage, deployment on the virtual machines, security and fault tolerance. We also include a cost-benefit analysis and usage evaluation.

Least Squares Shadowing Sensitivity Analysis of Chaotic Flow Around a Two-Dimensional Airfoil

NASA Technical Reports Server (NTRS)

Blonigan, Patrick J.; Wang, Qiqi; Nielsen, Eric J.; Diskin, Boris

2016-01-01

Gradient-based sensitivity analysis has proven to be an enabling technology for many applications, including design of aerospace vehicles. However, conventional sensitivity analysis methods break down when applied to long-time averages of chaotic systems. This breakdown is a serious limitation because many aerospace applications involve physical phenomena that exhibit chaotic dynamics, most notably high-resolution large-eddy and direct numerical simulations of turbulent aerodynamic flows. A recently proposed methodology, Least Squares Shadowing (LSS), avoids this breakdown and advances the state of the art in sensitivity analysis for chaotic flows. The first application of LSS to a chaotic flow simulated with a large-scale computational fluid dynamics solver is presented. The LSS sensitivity computed for this chaotic flow is verified and shown to be accurate, but the computational cost of the current LSS implementation is high.

Deep Neural Network Emulation of a High-Order, WENO-Limited, Space-Time Reconstruction

NASA Astrophysics Data System (ADS)

Norman, M. R.; Hall, D. M.

2017-12-01

Deep Neural Networks (DNNs) have been used to emulate a number of processes in atmospheric models, including radiation and even so-called super-parameterization of moist convection. In each scenario, the DNN provides a good representation of the process even for inputs that have not been encountered before. More notably, they provide an emulation at a fraction of the cost of the original routine, giving speed-ups of 30× and even up to 200× compared to the runtime costs of the original routines. However, to our knowledge there has not been an investigation into using DNNs to emulate the dynamics. The most likely reason for this is that dynamics operators are typically both linear and low cost, meaning they cannot be sped up by a non-linear DNN emulation. However, there exist high-cost non-linear space-time dynamics operators that significantly reduce the number of parallel data transfers necessary to complete an atmospheric simulation. The WENO-limited Finite-Volume method with ADER-DT time integration is a prime example of this - needing only two parallel communications per large, fully limited time step. However, it comes at a high cost in terms of computation, which is why many would hesitate to use it. This talk investigates DNN emulation of the WENO-limited space-time finite-volume reconstruction procedure - the most expensive portion of this method, which densely clusters a large amount of non-linear computation. Different training techniques and network architectures are tested, and the accuracy and speed-up of each is given.

Large-scale inverse model analyses employing fast randomized data reduction

NASA Astrophysics Data System (ADS)

Lin, Youzuo; Le, Ellen B.; O'Malley, Daniel; Vesselinov, Velimir V.; Bui-Thanh, Tan

2017-08-01

When the number of observations is large, it is computationally challenging to apply classical inverse modeling techniques. We have developed a new computationally efficient technique for solving inverse problems with a large number of observations (e.g., on the order of 107 or greater). Our method, which we call the randomized geostatistical approach (RGA), is built upon the principal component geostatistical approach (PCGA). We employ a data reduction technique combined with the PCGA to improve the computational efficiency and reduce the memory usage. Specifically, we employ a randomized numerical linear algebra technique based on a so-called "sketching" matrix to effectively reduce the dimension of the observations without losing the information content needed for the inverse analysis. In this way, the computational and memory costs for RGA scale with the information content rather than the size of the calibration data. Our algorithm is coded in Julia and implemented in the MADS open-source high-performance computational framework (http://mads.lanl.gov). We apply our new inverse modeling method to invert for a synthetic transmissivity field. Compared to a standard geostatistical approach (GA), our method is more efficient when the number of observations is large. Most importantly, our method is capable of solving larger inverse problems than the standard GA and PCGA approaches. Therefore, our new model inversion method is a powerful tool for solving large-scale inverse problems. The method can be applied in any field and is not limited to hydrogeological applications such as the characterization of aquifer heterogeneity.

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

The Department of Defense and the Power of Cloud Computing: Weighing Acceptable Cost Versus Acceptable Risk

DTIC Science & Technology

2016-04-01

the DOD will put DOD systems and data at a risk level comparable to that of their neighbors in the cloud. Just as a user browses a Web page on the...proxy servers for controlling user access to Web pages, and large-scale storage for data management. Each of these devices allows access to the...user to develop applications. Acunetics.com describes Web applications as “computer programs allowing Website visitors to submit and retrieve data

Wetland mapping from digitized aerial photography. [Sheboygen Marsh, Sheboygen County, Wisconsin

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Quirk, B. K.; Kiefer, R. W.; Wynn, S. L.

1981-01-01

Computer assisted interpretation of small scale aerial imagery was found to be a cost effective and accurate method of mapping complex vegetation patterns if high resolution information is desired. This type of technique is suited for problems such as monitoring changes in species composition due to environmental factors and is a feasible method of monitoring and mapping large areas of wetlands. The technique has the added advantage of being in a computer compatible form which can be transformed into any georeference system of interest.

Parallel fuzzy connected image segmentation on GPU

PubMed Central

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K.; Miller, Robert W.

2011-01-01

Purpose: Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA’s compute unified device Architecture (cuda) platform for segmenting medical image data sets. Methods: In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as cuda kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Results: Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. Conclusions: The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set. PMID:21859037

Parallel fuzzy connected image segmentation on GPU.

PubMed

Zhuge, Ying; Cao, Yong; Udupa, Jayaram K; Miller, Robert W

2011-07-01

Image segmentation techniques using fuzzy connectedness (FC) principles have shown their effectiveness in segmenting a variety of objects in several large applications. However, one challenge in these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays, commodity graphics hardware provides a highly parallel computing environment. In this paper, the authors present a parallel fuzzy connected image segmentation algorithm implementation on NVIDIA's compute unified device Architecture (CUDA) platform for segmenting medical image data sets. In the FC algorithm, there are two major computational tasks: (i) computing the fuzzy affinity relations and (ii) computing the fuzzy connectedness relations. These two tasks are implemented as CUDA kernels and executed on GPU. A dramatic improvement in speed for both tasks is achieved as a result. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 24.4x, 18.1x, and 10.3x, correspondingly, for the three data sets on the NVIDIA Tesla C1060 over the implementation of the algorithm on CPU, and takes 0.25, 0.72, and 15.04 s, correspondingly, for the three data sets. The authors developed a parallel algorithm of the widely used fuzzy connected image segmentation method on the NVIDIA GPUs, which are far more cost- and speed-effective than both cluster of workstations and multiprocessing systems. A near-interactive speed of segmentation has been achieved, even for the large data set.

Dealing with electronic waste: modeling the costs and environmental benefits of computer monitor disposal.

PubMed

Macauley, Molly; Palmer, Karen; Shih, Jhih-Shyang

2003-05-01

The importance of information technology to the world economy has brought about a surge in demand for electronic equipment. With rapid technological change, a growing fraction of the increasing stock of many types of electronics becomes obsolete each year. We model the costs and benefits of policies to manage 'e-waste' by focusing on a large component of the electronic waste stream-computer monitors-and the environmental concerns associated with disposal of the lead embodied in cathode ray tubes (CRTs) used in most monitors. We find that the benefits of avoiding health effects associated with CRT disposal appear far outweighed by the costs for a wide range of policies. For the stock of monitors disposed of in the United States in 1998, we find that policies restricting or banning some popular disposal options would increase disposal costs from about US dollar 1 per monitor to between US dollars 3 and US dollars 20 per monitor. Policies to promote a modest amount of recycling of monitor parts, including lead, can be less expensive. In all cases, however, the costs of the policies exceed the value of the avoided health effects of CRT disposal.

A workload model and measures for computer performance evaluation

NASA Technical Reports Server (NTRS)

Kerner, H.; Kuemmerle, K.

1972-01-01

A generalized workload definition is presented which constructs measurable workloads of unit size from workload elements, called elementary processes. An elementary process makes almost exclusive use of one of the processors, CPU, I/O processor, etc., and is measured by the cost of its execution. Various kinds of user programs can be simulated by quantitative composition of elementary processes into a type. The character of the type is defined by the weights of its elementary processes and its structure by the amount and sequence of transitions between its elementary processes. A set of types is batched to a mix. Mixes of identical cost are considered as equivalent amounts of workload. These formalized descriptions of workloads allow investigators to compare the results of different studies quantitatively. Since workloads of different composition are assigned a unit of cost, these descriptions enable determination of cost effectiveness of different workloads on a machine. Subsequently performance parameters such as throughput rate, gain factor, internal and external delay factors are defined and used to demonstrate the effects of various workload attributes on the performance of a selected large scale computer system.

Scalable and cost-effective NGS genotyping in the cloud.

PubMed

Souilmi, Yassine; Lancaster, Alex K; Jung, Jae-Yoon; Rizzo, Ettore; Hawkins, Jared B; Powles, Ryan; Amzazi, Saaïd; Ghazal, Hassan; Tonellato, Peter J; Wall, Dennis P

2015-10-15

While next-generation sequencing (NGS) costs have plummeted in recent years, cost and complexity of computation remain substantial barriers to the use of NGS in routine clinical care. The clinical potential of NGS will not be realized until robust and routine whole genome sequencing data can be accurately rendered to medically actionable reports within a time window of hours and at scales of economy in the 10's of dollars. We take a step towards addressing this challenge, by using COSMOS, a cloud-enabled workflow management system, to develop GenomeKey, an NGS whole genome analysis workflow. COSMOS implements complex workflows making optimal use of high-performance compute clusters. Here we show that the Amazon Web Service (AWS) implementation of GenomeKey via COSMOS provides a fast, scalable, and cost-effective analysis of both public benchmarking and large-scale heterogeneous clinical NGS datasets. Our systematic benchmarking reveals important new insights and considerations to produce clinical turn-around of whole genome analysis optimization and workflow management including strategic batching of individual genomes and efficient cluster resource configuration.

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.

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.

Using Adaptive Mesh Refinment to Simulate Storm Surge

NASA Astrophysics Data System (ADS)

Mandli, K. T.; Dawson, C.

2012-12-01

Coastal hazards related to strong storms such as hurricanes and typhoons are one of the most frequently recurring and wide spread hazards to coastal communities. Storm surges are among the most devastating effects of these storms, and their prediction and mitigation through numerical simulations is of great interest to coastal communities that need to plan for the subsequent rise in sea level during these storms. Unfortunately these simulations require a large amount of resolution in regions of interest to capture relevant effects resulting in a computational cost that may be intractable. This problem is exacerbated in situations where a large number of similar runs is needed such as in design of infrastructure or forecasting with ensembles of probable storms. One solution to address the problem of computational cost is to employ adaptive mesh refinement (AMR) algorithms. AMR functions by decomposing the computational domain into regions which may vary in resolution as time proceeds. Decomposing the domain as the flow evolves makes this class of methods effective at ensuring that computational effort is spent only where it is needed. AMR also allows for placement of computational resolution independent of user interaction and expectation of the dynamics of the flow as well as particular regions of interest such as harbors. The simulation of many different applications have only been made possible by using AMR-type algorithms, which have allowed otherwise impractical simulations to be performed for much less computational expense. Our work involves studying how storm surge simulations can be improved with AMR algorithms. We have implemented relevant storm surge physics in the GeoClaw package and tested how Hurricane Ike's surge into Galveston Bay and up the Houston Ship Channel compares to available tide gauge data. We will also discuss issues dealing with refinement criteria, optimal resolution and refinement ratios, and inundation.

Yahoo! Compute Coop (YCC). A Next-Generation Passive Cooling Design for Data Centers

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

Robison, AD; Page, Christina; Lytle, Bob

The purpose of the Yahoo! Compute Coop (YCC) project is to research, design, build and implement a greenfield "efficient data factory" and to specifically demonstrate that the YCC concept is feasible for large facilities housing tens of thousands of heat-producing computing servers. The project scope for the Yahoo! Compute Coop technology includes: - Analyzing and implementing ways in which to drastically decrease energy consumption and waste output. - Analyzing the laws of thermodynamics and implementing naturally occurring environmental effects in order to maximize the "free-cooling" for large data center facilities. "Free cooling" is the direct usage of outside air tomore » cool the servers vs. traditional "mechanical cooling" which is supplied by chillers or other Dx units. - Redesigning and simplifying building materials and methods. - Shortening and simplifying build-to-operate schedules while at the same time reducing initial build and operating costs. Selected for its favorable climate, the greenfield project site is located in Lockport, NY. Construction on the 9.0 MW critical load data center facility began in May 2009, with the fully operational facility deployed in September 2010. The relatively low initial build cost, compatibility with current server and network models, and the efficient use of power and water are all key features that make it a highly compatible and globally implementable design innovation for the data center industry. Yahoo! Compute Coop technology is designed to achieve 99.98% uptime availability. This integrated building design allows for free cooling 99% of the year via the building's unique shape and orientation, as well as server physical configuration.« less

Efficient Ab initio Modeling of Random Multicomponent Alloys

DOE PAGES

Jiang, Chao; Uberuaga, Blas P.

2016-03-08

Here, we present in this Letter a novel small set of ordered structures (SSOS) method that allows extremely efficient ab initio modeling of random multi-component alloys. Using inverse II-III spinel oxides and equiatomic quinary bcc (so-called high entropy) alloys as examples, we also demonstrate that a SSOS can achieve the same accuracy as a large supercell or a well-converged cluster expansion, but with significantly reduced computational cost. In particular, because of this efficiency, a large number of quinary alloy compositions can be quickly screened, leading to the identification of several new possible high entropy alloy chemistries. Furthermore, the SSOS methodmore » developed here can be broadly useful for the rapid computational design of multi-component materials, especially those with a large number of alloying elements, a challenging problem for other approaches.« less

Operating Dedicated Data Centers - Is It Cost-Effective?

NASA Astrophysics Data System (ADS)

Ernst, M.; Hogue, R.; Hollowell, C.; Strecker-Kellog, W.; Wong, A.; Zaytsev, A.

2014-06-01

The advent of cloud computing centres such as Amazon's EC2 and Google's Computing Engine has elicited comparisons with dedicated computing clusters. Discussions on appropriate usage of cloud resources (both academic and commercial) and costs have ensued. This presentation discusses a detailed analysis of the costs of operating and maintaining the RACF (RHIC and ATLAS Computing Facility) compute cluster at Brookhaven National Lab and compares them with the cost of cloud computing resources under various usage scenarios. An extrapolation of likely future cost effectiveness of dedicated computing resources is also presented.

Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging

DOE PAGES

Lee, Jonghyun; Yoon, Hongkyu; Kitanidis, Peter K.; ...

2016-06-09

When characterizing subsurface properties is crucial for reliable and cost-effective groundwater supply management and contaminant remediation. With recent advances in sensor technology, large volumes of hydro-geophysical and geochemical data can be obtained to achieve high-resolution images of subsurface properties. However, characterization with such a large amount of information requires prohibitive computational costs associated with “big data” processing and numerous large-scale numerical simulations. To tackle such difficulties, the Principal Component Geostatistical Approach (PCGA) has been proposed as a “Jacobian-free” inversion method that requires much smaller forward simulation runs for each iteration than the number of unknown parameters and measurements needed inmore » the traditional inversion methods. PCGA can be conveniently linked to any multi-physics simulation software with independent parallel executions. In our paper, we extend PCGA to handle a large number of measurements (e.g. 106 or more) by constructing a fast preconditioner whose computational cost scales linearly with the data size. For illustration, we characterize the heterogeneous hydraulic conductivity (K) distribution in a laboratory-scale 3-D sand box using about 6 million transient tracer concentration measurements obtained using magnetic resonance imaging. Since each individual observation has little information on the K distribution, the data was compressed by the zero-th temporal moment of breakthrough curves, which is equivalent to the mean travel time under the experimental setting. Moreover, only about 2,000 forward simulations in total were required to obtain the best estimate with corresponding estimation uncertainty, and the estimated K field captured key patterns of the original packing design, showing the efficiency and effectiveness of the proposed method. This article is protected by copyright. All rights reserved.« less

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

Lee, Jonghyun; Yoon, Hongkyu; Kitanidis, Peter K.

When characterizing subsurface properties is crucial for reliable and cost-effective groundwater supply management and contaminant remediation. With recent advances in sensor technology, large volumes of hydro-geophysical and geochemical data can be obtained to achieve high-resolution images of subsurface properties. However, characterization with such a large amount of information requires prohibitive computational costs associated with “big data” processing and numerous large-scale numerical simulations. To tackle such difficulties, the Principal Component Geostatistical Approach (PCGA) has been proposed as a “Jacobian-free” inversion method that requires much smaller forward simulation runs for each iteration than the number of unknown parameters and measurements needed inmore » the traditional inversion methods. PCGA can be conveniently linked to any multi-physics simulation software with independent parallel executions. In our paper, we extend PCGA to handle a large number of measurements (e.g. 106 or more) by constructing a fast preconditioner whose computational cost scales linearly with the data size. For illustration, we characterize the heterogeneous hydraulic conductivity (K) distribution in a laboratory-scale 3-D sand box using about 6 million transient tracer concentration measurements obtained using magnetic resonance imaging. Since each individual observation has little information on the K distribution, the data was compressed by the zero-th temporal moment of breakthrough curves, which is equivalent to the mean travel time under the experimental setting. Moreover, only about 2,000 forward simulations in total were required to obtain the best estimate with corresponding estimation uncertainty, and the estimated K field captured key patterns of the original packing design, showing the efficiency and effectiveness of the proposed method. This article is protected by copyright. All rights reserved.« less

Wall Modeled Large Eddy Simulation of Airfoil Trailing Edge Noise

NASA Astrophysics Data System (ADS)

Kocheemoolayil, Joseph; Lele, Sanjiva

2014-11-01

Large eddy simulation (LES) of airfoil trailing edge noise has largely been restricted to low Reynolds numbers due to prohibitive computational cost. Wall modeled LES (WMLES) is a computationally cheaper alternative that makes full-scale Reynolds numbers relevant to large wind turbines accessible. A systematic investigation of trailing edge noise prediction using WMLES is conducted. Detailed comparisons are made with experimental data. The stress boundary condition from a wall model does not constrain the fluctuating velocity to vanish at the wall. This limitation has profound implications for trailing edge noise prediction. The simulation over-predicts the intensity of fluctuating wall pressure and far-field noise. An improved wall model formulation that minimizes the over-prediction of fluctuating wall pressure is proposed and carefully validated. The flow configurations chosen for the study are from the workshop on benchmark problems for airframe noise computations. The large eddy simulation database is used to examine the adequacy of scaling laws that quantify the dependence of trailing edge noise on Mach number, Reynolds number and angle of attack. Simplifying assumptions invoked in engineering approaches towards predicting trailing edge noise are critically evaluated. We gratefully acknowledge financial support from GE Global Research and thank Cascade Technologies Inc. for providing access to their massively-parallel large eddy simulation framework.

Approximate kernel competitive learning.

PubMed

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

PubMed

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment

PubMed Central

2014-01-01

Background To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. Results This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Conclusions Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high quality solutions can be obtained within relatively short time. This integrated approach is a promising way for inferring large networks. PMID:24428926

PAD_AUDIT -- PAD Auditing Package

NASA Astrophysics Data System (ADS)

Clayton, C. A.

The PAD (Packet Assembler Disassembler) utility is the part of the VAX/VMS Coloured Book Software (CBS) which allows a user to log onto remote computers from a local VAX. Unfortunately, logging into a computer via either the Packet SwitchStream (PSS) or the International Packet SwitchStream (IPSS) costs real money. Some users either do not appreciate this or do not care and have been known to clock up rather large quarterly bills. This software package allows a system manager to determine who has used PAD to call where and (most importantly) how much it has cost. The system manager can then take appropriate action - either charging the individuals, warning them to use the facility with more care or even denying access to a greedy user to one or more sites.

Efficient searching in meshfree methods

NASA Astrophysics Data System (ADS)

Olliff, James; Alford, Brad; Simkins, Daniel C.

2018-04-01

Meshfree methods such as the Reproducing Kernel Particle Method and the Element Free Galerkin method have proven to be excellent choices for problems involving complex geometry, evolving topology, and large deformation, owing to their ability to model the problem domain without the constraints imposed on the Finite Element Method (FEM) meshes. However, meshfree methods have an added computational cost over FEM that come from at least two sources: increased cost of shape function evaluation and the determination of adjacency or connectivity. The focus of this paper is to formally address the types of adjacency information that arises in various uses of meshfree methods; a discussion of available techniques for computing the various adjacency graphs; propose a new search algorithm and data structure; and finally compare the memory and run time performance of the methods.

Numerical computation of spherical harmonics of arbitrary degree and order by extending exponent of floating point numbers

NASA Astrophysics Data System (ADS)

Fukushima, Toshio

2012-04-01

By extending the exponent of floating point numbers with an additional integer as the power index of a large radix, we compute fully normalized associated Legendre functions (ALF) by recursion without underflow problem. The new method enables us to evaluate ALFs of extremely high degree as 232 = 4,294,967,296, which corresponds to around 1 cm resolution on the Earth's surface. By limiting the application of exponent extension to a few working variables in the recursion, choosing a suitable large power of 2 as the radix, and embedding the contents of the basic arithmetic procedure of floating point numbers with the exponent extension directly in the program computing the recurrence formulas, we achieve the evaluation of ALFs in the double-precision environment at the cost of around 10% increase in computational time per single ALF. This formulation realizes meaningful execution of the spherical harmonic synthesis and/or analysis of arbitrary degree and order.

Adjoint Sensitivity Analysis for Scale-Resolving Turbulent Flow Solvers

NASA Astrophysics Data System (ADS)

Blonigan, Patrick; Garai, Anirban; Diosady, Laslo; Murman, Scott

2017-11-01

Adjoint-based sensitivity analysis methods are powerful design tools for engineers who use computational fluid dynamics. In recent years, these engineers have started to use scale-resolving simulations like large-eddy simulations (LES) and direct numerical simulations (DNS), which resolve more scales in complex flows with unsteady separation and jets than the widely-used Reynolds-averaged Navier-Stokes (RANS) methods. However, the conventional adjoint method computes large, unusable sensitivities for scale-resolving simulations, which unlike RANS simulations exhibit the chaotic dynamics inherent in turbulent flows. Sensitivity analysis based on least-squares shadowing (LSS) avoids the issues encountered by conventional adjoint methods, but has a high computational cost even for relatively small simulations. The following talk discusses a more computationally efficient formulation of LSS, ``non-intrusive'' LSS, and its application to turbulent flows simulated with a discontinuous-Galkerin spectral-element-method LES/DNS solver. Results are presented for the minimal flow unit, a turbulent channel flow with a limited streamwise and spanwise domain.

Multi-Scale Modeling to Improve Single-Molecule, Single-Cell Experiments

NASA Astrophysics Data System (ADS)

Munsky, Brian; Shepherd, Douglas

2014-03-01

Single-cell, single-molecule experiments are producing an unprecedented amount of data to capture the dynamics of biological systems. When integrated with computational models, observations of spatial, temporal and stochastic fluctuations can yield powerful quantitative insight. We concentrate on experiments that localize and count individual molecules of mRNA. These high precision experiments have large imaging and computational processing costs, and we explore how improved computational analyses can dramatically reduce overall data requirements. In particular, we show how analyses of spatial, temporal and stochastic fluctuations can significantly enhance parameter estimation results for small, noisy data sets. We also show how full probability distribution analyses can constrain parameters with far less data than bulk analyses or statistical moment closures. Finally, we discuss how a systematic modeling progression from simple to more complex analyses can reduce total computational costs by orders of magnitude. We illustrate our approach using single-molecule, spatial mRNA measurements of Interleukin 1-alpha mRNA induction in human THP1 cells following stimulation. Our approach could improve the effectiveness of single-molecule gene regulation analyses for many other process.

Streaming Support for Data Intensive Cloud-Based Sequence Analysis

PubMed Central

Issa, Shadi A.; Kienzler, Romeo; El-Kalioby, Mohamed; Tonellato, Peter J.; Wall, Dennis; Bruggmann, Rémy; Abouelhoda, Mohamed

2013-01-01

Cloud computing provides a promising solution to the genomics data deluge problem resulting from the advent of next-generation sequencing (NGS) technology. Based on the concepts of “resources-on-demand” and “pay-as-you-go”, scientists with no or limited infrastructure can have access to scalable and cost-effective computational resources. However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services. In this paper, we provide a streaming-based scheme to overcome this problem, where the NGS data is processed while being transferred to the cloud. Our scheme targets the wide class of NGS data analysis tasks, where the NGS sequences can be processed independently from one another. We also provide the elastream package that supports the use of this scheme with individual analysis programs or with workflow systems. Experiments presented in this paper show that our solution mitigates the effect of data transfer latency and saves both time and cost of computation. PMID:23710461

On the precision of aero-thermal simulations for TMT

NASA Astrophysics Data System (ADS)

Vogiatzis, Konstantinos; Thompson, Hugh

2016-08-01

Environmental effects on the Image Quality (IQ) of the Thirty Meter Telescope (TMT) are estimated by aero-thermal numerical simulations. These simulations utilize Computational Fluid Dynamics (CFD) to estimate, among others, thermal (dome and mirror) seeing as well as wind jitter and blur. As the design matures, guidance obtained from these numerical experiments can influence significant cost-performance trade-offs and even component survivability. The stochastic nature of environmental conditions results in the generation of a large computational solution matrix in order to statistically predict Observatory Performance. Moreover, the relative contribution of selected key subcomponents to IQ increases the parameter space and thus computational cost, while dictating a reduced prediction error bar. The current study presents the strategy followed to minimize prediction time and computational resources, the subsequent physical and numerical limitations and finally the approach to mitigate the issues experienced. In particular, the paper describes a mesh-independence study, the effect of interpolation of CFD results on the TMT IQ metric, and an analysis of the sensitivity of IQ to certain important heat sources and geometric features.

The emerging role of cloud computing in molecular modelling.

PubMed

Ebejer, Jean-Paul; Fulle, Simone; Morris, Garrett M; Finn, Paul W

2013-07-01

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways. Copyright © 2013 Elsevier Inc. All rights reserved.

FOCUS: a fire management planning system -- final report

Treesearch

Frederick W. Bratten; James B. Davis; George T. Flatman; Jerold W. Keith; Stanley R. Rapp; Theodore G. Storey

1981-01-01

FOCUS (Fire Operational Characteristics Using Simulation) is a computer simulation model for evaluating alternative fire management plans. This final report provides a broad overview of the FOCUS system, describes two major modules-fire suppression and cost, explains the role in the system of gaming large fires, and outlines the support programs and ways of...

Surveillance in the Information Age: Text Quantification, Anomaly Detection, and Empirical Evaluation

ERIC Educational Resources Information Center

Lu, Hsin-Min

2010-01-01

Deep penetration of personal computers, data communication networks, and the Internet has created a massive platform for data collection, dissemination, storage, and retrieval. Large amounts of textual data are now available at a very low cost. Valuable information, such as consumer preferences, new product developments, trends, and opportunities,…

Sparse matrix methods based on orthogonality and conjugacy

NASA Technical Reports Server (NTRS)

Lawson, C. L.

1973-01-01

A matrix having a high percentage of zero elements is called spares. In the solution of systems of linear equations or linear least squares problems involving large sparse matrices, significant saving of computer cost can be achieved by taking advantage of the sparsity. The conjugate gradient algorithm and a set of related algorithms are described.

The Nimrod computational workbench: a case study in desktop metacomputing

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

Abramson, D.; Sosic, R.; Foster, I.

The coordinated use of geographically distributed computers, or metacomputing, can in principle provide more accessible and cost- effective supercomputing than conventional high-performance systems. However, we lack evidence that metacomputing systems can be made easily usable, or that there exist large numbers of applications able to exploit metacomputing resources. In this paper, we present work that addresses both these concerns. The basis for this work is a system called Nimrod that provides a desktop problem-solving environment for parametric experiments. We describe how Nimrod has been extended to support the scheduling of computational resources located in a wide-area environment, and report onmore » an experiment in which Nimrod was used to schedule a large parametric study across the Australian Internet. The experiment provided both new scientific results and insights into Nimrod capabilities. We relate the results of this experiment to lessons learned from the I-WAY distributed computing experiment, and draw conclusions as to how Nimrod and I-WAY- like computing environments should be developed to support desktop metacomputing.« less

HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing

PubMed Central

Karimi, Ramin; Hajdu, Andras

2016-01-01

Comprehensive effort for low-cost sequencing in the past few years has led to the growth of complete genome databases. In parallel with this effort, a strong need, fast and cost-effective methods and applications have been developed to accelerate sequence analysis. Identification is the very first step of this task. Due to the difficulties, high costs, and computational challenges of alignment-based approaches, an alternative universal identification method is highly required. Like an alignment-free approach, DNA signatures have provided new opportunities for the rapid identification of species. In this paper, we present an effective pipeline HTSFinder (high-throughput signature finder) with a corresponding k-mer generator GkmerG (genome k-mers generator). Using this pipeline, we determine the frequency of k-mers from the available complete genome databases for the detection of extensive DNA signatures in a reasonably short time. Our application can detect both unique and common signatures in the arbitrarily selected target and nontarget databases. Hadoop and MapReduce as parallel and distributed computing tools with commodity hardware are used in this pipeline. This approach brings the power of high-performance computing into the ordinary desktop personal computers for discovering DNA signatures in large databases such as bacterial genome. A considerable number of detected unique and common DNA signatures of the target database bring the opportunities to improve the identification process not only for polymerase chain reaction and microarray assays but also for more complex scenarios such as metagenomics and next-generation sequencing analysis. PMID:26884678

HTSFinder: Powerful Pipeline of DNA Signature Discovery by Parallel and Distributed Computing.

PubMed

Karimi, Ramin; Hajdu, Andras

2016-01-01

Comprehensive effort for low-cost sequencing in the past few years has led to the growth of complete genome databases. In parallel with this effort, a strong need, fast and cost-effective methods and applications have been developed to accelerate sequence analysis. Identification is the very first step of this task. Due to the difficulties, high costs, and computational challenges of alignment-based approaches, an alternative universal identification method is highly required. Like an alignment-free approach, DNA signatures have provided new opportunities for the rapid identification of species. In this paper, we present an effective pipeline HTSFinder (high-throughput signature finder) with a corresponding k-mer generator GkmerG (genome k-mers generator). Using this pipeline, we determine the frequency of k-mers from the available complete genome databases for the detection of extensive DNA signatures in a reasonably short time. Our application can detect both unique and common signatures in the arbitrarily selected target and nontarget databases. Hadoop and MapReduce as parallel and distributed computing tools with commodity hardware are used in this pipeline. This approach brings the power of high-performance computing into the ordinary desktop personal computers for discovering DNA signatures in large databases such as bacterial genome. A considerable number of detected unique and common DNA signatures of the target database bring the opportunities to improve the identification process not only for polymerase chain reaction and microarray assays but also for more complex scenarios such as metagenomics and next-generation sequencing analysis.

Predictive wind turbine simulation with an adaptive lattice Boltzmann method for moving boundaries

NASA Astrophysics Data System (ADS)

Deiterding, Ralf; Wood, Stephen L.

2016-09-01

Operating horizontal axis wind turbines create large-scale turbulent wake structures that affect the power output of downwind turbines considerably. The computational prediction of this phenomenon is challenging as efficient low dissipation schemes are necessary that represent the vorticity production by the moving structures accurately and that are able to transport wakes without significant artificial decay over distances of several rotor diameters. We have developed a parallel adaptive lattice Boltzmann method for large eddy simulation of turbulent weakly compressible flows with embedded moving structures that considers these requirements rather naturally and enables first principle simulations of wake-turbine interaction phenomena at reasonable computational costs. The paper describes the employed computational techniques and presents validation simulations for the Mexnext benchmark experiments as well as simulations of the wake propagation in the Scaled Wind Farm Technology (SWIFT) array consisting of three Vestas V27 turbines in triangular arrangement.

Accelerating the two-point and three-point galaxy correlation functions using Fourier transforms

NASA Astrophysics Data System (ADS)

Slepian, Zachary; Eisenstein, Daniel J.

2016-01-01

Though Fourier transforms (FTs) are a common technique for finding correlation functions, they are not typically used in computations of the anisotropy of the two-point correlation function (2PCF) about the line of sight in wide-angle surveys because the line-of-sight direction is not constant on the Cartesian grid. Here we show how FTs can be used to compute the multipole moments of the anisotropic 2PCF. We also show how FTs can be used to accelerate the 3PCF algorithm of Slepian & Eisenstein. In both cases, these FT methods allow one to avoid the computational cost of pair counting, which scales as the square of the number density of objects in the survey. With the upcoming large data sets of Dark Energy Spectroscopic Instrument, Euclid, and Large Synoptic Survey Telescope, FT techniques will therefore offer an important complement to simple pair or triplet counts.

Conceptual spacecraft systems design and synthesis

NASA Technical Reports Server (NTRS)

Wright, R. L.; Deryder, D. D.; Ferebee, M. J., Jr.

1984-01-01

An interactive systems design and synthesis is performed on future spacecraft concepts using the Interactive Design and Evaluation of Advanced Systems (IDEAS) computer-aided design and analysis system. The capabilities and advantages of the systems-oriented interactive computer-aided design and analysis system are described. The synthesis of both large antenna and space station concepts, and space station evolutionary growth designs is demonstrated. The IDEAS program provides the user with both an interactive graphics and an interactive computing capability which consists of over 40 multidisciplinary synthesis and analysis modules. Thus, the user can create, analyze, and conduct parametric studies and modify earth-orbiting spacecraft designs (space stations, large antennas or platforms, and technologically advanced spacecraft) at an interactive terminal with relative ease. The IDEAS approach is useful during the conceptual design phase of advanced space missions when a multiplicity of parameters and concepts must be analyzed and evaluated in a cost-effective and timely manner.

Interactive systems design and synthesis of future spacecraft concepts

NASA Technical Reports Server (NTRS)

Wright, R. L.; Deryder, D. D.; Ferebee, M. J., Jr.

1984-01-01

An interactive systems design and synthesis is performed on future spacecraft concepts using the Interactive Design and Evaluation of Advanced spacecraft (IDEAS) computer-aided design and analysis system. The capabilities and advantages of the systems-oriented interactive computer-aided design and analysis system are described. The synthesis of both large antenna and space station concepts, and space station evolutionary growth is demonstrated. The IDEAS program provides the user with both an interactive graphics and an interactive computing capability which consists of over 40 multidisciplinary synthesis and analysis modules. Thus, the user can create, analyze and conduct parametric studies and modify Earth-orbiting spacecraft designs (space stations, large antennas or platforms, and technologically advanced spacecraft) at an interactive terminal with relative ease. The IDEAS approach is useful during the conceptual design phase of advanced space missions when a multiplicity of parameters and concepts must be analyzed and evaluated in a cost-effective and timely manner.

GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering.

PubMed

Suzuki, Shuji; Kakuta, Masanori; Ishida, Takashi; Akiyama, Yutaka

2016-01-01

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads.

A Technical Survey on Optimization of Processing Geo Distributed Data

NASA Astrophysics Data System (ADS)

Naga Malleswari, T. Y. J.; Ushasukhanya, S.; Nithyakalyani, A.; Girija, S.

2018-04-01

With growing cloud services and technology, there is growth in some geographically distributed data centers to store large amounts of data. Analysis of geo-distributed data is required in various services for data processing, storage of essential information, etc., processing this geo-distributed data and performing analytics on this data is a challenging task. The distributed data processing is accompanied by issues in storage, computation and communication. The key issues to be dealt with are time efficiency, cost minimization, utility maximization. This paper describes various optimization methods like end-to-end multiphase, G-MR, etc., using the techniques like Map-Reduce, CDS (Community Detection based Scheduling), ROUT, Workload-Aware Scheduling, SAGE, AMP (Ant Colony Optimization) to handle these issues. In this paper various optimization methods and techniques used are analyzed. It has been observed that end-to end multiphase achieves time efficiency; Cost minimization concentrates to achieve Quality of Service, Computation and reduction of Communication cost. SAGE achieves performance improvisation in processing geo-distributed data sets.

Wide field imaging problems in radio astronomy

NASA Astrophysics Data System (ADS)

Cornwell, T. J.; Golap, K.; Bhatnagar, S.

2005-03-01

The new generation of synthesis radio telescopes now being proposed, designed, and constructed face substantial problems in making images over wide fields of view. Such observations are required either to achieve the full sensitivity limit in crowded fields or for surveys. The Square Kilometre Array (SKA Consortium, Tech. Rep., 2004), now being developed by an international consortium of 15 countries, will require advances well beyond the current state of the art. We review the theory of synthesis radio telescopes for large fields of view. We describe a new algorithm, W projection, for correcting the non-coplanar baselines aberration. This algorithm has improved performance over those previously used (typically an order of magnitude in speed). Despite the advent of W projection, the computing hardware required for SKA wide field imaging is estimated to cost up to $500M (2015 dollars). This is about half the target cost of the SKA. Reconfigurable computing is one way in which the costs can be decreased dramatically.

Assessment of regional management strategies for controlling seawater intrusion

USGS Publications Warehouse

Reichard, E.G.; Johnson, T.A.

2005-01-01

Simulation-optimization methods, applied with adequate sensitivity tests, can provide useful quantitative guidance for controlling seawater intrusion. This is demonstrated in an application to the West Coast Basin of coastal Los Angeles that considers two management options for improving hydraulic control of seawater intrusion: increased injection into barrier wells and in lieu delivery of surface water to replace current pumpage. For the base-case optimization analysis, assuming constant groundwater demand, in lieu delivery was determined to be most cost effective. Reduced-cost information from the optimization provided guidance for prioritizing locations for in lieu delivery. Model sensitivity to a suite of hydrologic, economic, and policy factors was tested. Raising the imposed average water-level constraint at the hydraulic-control locations resulted in nonlinear increases in cost. Systematic varying of the relative costs of injection and in lieu water yielded a trade-off curve between relative costs and injection/in lieu amounts. Changing the assumed future scenario to one of increasing pumpage in the adjacent Central Basin caused a small increase in the computed costs of seawater intrusion control. Changing the assumed boundary condition representing interaction with an adjacent basin did not affect the optimization results. Reducing the assumed hydraulic conductivity of the main productive aquifer resulted in a large increase in the model-computed cost. Journal of Water Resources Planning and Management ?? ASCE.

Cost-effectiveness analysis of computerized ECG interpretation system in an ambulatory health care organization.

PubMed

Carel, R S

1982-04-01

The cost-effectiveness of a computerized ECG interpretation system in an ambulatory health care organization has been evaluated in comparison with a conventional (manual) system. The automated system was shown to be more cost-effective at a minimum load of 2,500 patients/month. At larger monthly loads an even greater cost-effectiveness was found, the average cost/ECG being about $2. In the manual system the cost/unit is practically independent of patient load. This is primarily due to the fact that 87% of the cost/ECG is attributable to wages and fees of highly trained personnel. In the automated system, on the other hand, the cost/ECG is heavily dependent on examinee load. This is due to the relatively large impact of equipment depreciation on fixed (and total) cost. Utilization of a computer-assisted system leads to marked reduction in cardiologists' interpretation time, substantially shorter turnaround time (of unconfirmed reports), and potential provision of simultaneous service at several remotely located "heart stations."

Accelerating root system phenotyping of seedlings through a computer-assisted processing pipeline.

PubMed

Dupuy, Lionel X; Wright, Gladys; Thompson, Jacqueline A; Taylor, Anna; Dekeyser, Sebastien; White, Christopher P; Thomas, William T B; Nightingale, Mark; Hammond, John P; Graham, Neil S; Thomas, Catherine L; Broadley, Martin R; White, Philip J

2017-01-01

There are numerous systems and techniques to measure the growth of plant roots. However, phenotyping large numbers of plant roots for breeding and genetic analyses remains challenging. One major difficulty is to achieve high throughput and resolution at a reasonable cost per plant sample. Here we describe a cost-effective root phenotyping pipeline, on which we perform time and accuracy benchmarking to identify bottlenecks in such pipelines and strategies for their acceleration. Our root phenotyping pipeline was assembled with custom software and low cost material and equipment. Results show that sample preparation and handling of samples during screening are the most time consuming task in root phenotyping. Algorithms can be used to speed up the extraction of root traits from image data, but when applied to large numbers of images, there is a trade-off between time of processing the data and errors contained in the database. Scaling-up root phenotyping to large numbers of genotypes will require not only automation of sample preparation and sample handling, but also efficient algorithms for error detection for more reliable replacement of manual interventions.

Surpassing Humans and Computers with JellyBean: Crowd-Vision-Hybrid Counting Algorithms.

PubMed

Sarma, Akash Das; Jain, Ayush; Nandi, Arnab; Parameswaran, Aditya; Widom, Jennifer

2015-11-01

Counting objects is a fundamental image processisng primitive, and has many scientific, health, surveillance, security, and military applications. Existing supervised computer vision techniques typically require large quantities of labeled training data, and even with that, fail to return accurate results in all but the most stylized settings. Using vanilla crowd-sourcing, on the other hand, can lead to significant errors, especially on images with many objects. In this paper, we present our JellyBean suite of algorithms, that combines the best of crowds and computer vision to count objects in images, and uses judicious decomposition of images to greatly improve accuracy at low cost. Our algorithms have several desirable properties: (i) they are theoretically optimal or near-optimal , in that they ask as few questions as possible to humans (under certain intuitively reasonable assumptions that we justify in our paper experimentally); (ii) they operate under stand-alone or hybrid modes, in that they can either work independent of computer vision algorithms, or work in concert with them, depending on whether the computer vision techniques are available or useful for the given setting; (iii) they perform very well in practice, returning accurate counts on images that no individual worker or computer vision algorithm can count correctly, while not incurring a high cost.

Basic principles of cone beam computed tomography.

PubMed

Abramovitch, Kenneth; Rice, Dwight D

2014-07-01

At the end of the millennium, cone-beam computed tomography (CBCT) heralded a new dental technology for the next century. Owing to the dramatic and positive impact of CBCT on implant dentistry and orthognathic/orthodontic patient care, additional applications for this technology soon evolved. New software programs were developed to improve the applicability of, and access to, CBCT for dental patients. Improved, rapid, and cost-effective computer technology, combined with the ability of software engineers to develop multiple dental imaging applications for CBCT with broad diagnostic capability, have played a large part in the rapid incorporation of CBCT technology into dentistry. Copyright © 2014 Elsevier Inc. All rights reserved.

The Fast Multipole Method and Fourier Convolution for the Solution of Acoustic Scattering on Regular Volumetric Grids

PubMed Central

Hesford, Andrew J.; Waag, Robert C.

2010-01-01

The fast multipole method (FMM) is applied to the solution of large-scale, three-dimensional acoustic scattering problems involving inhomogeneous objects defined on a regular grid. The grid arrangement is especially well suited to applications in which the scattering geometry is not known a priori and is reconstructed on a regular grid using iterative inverse scattering algorithms or other imaging techniques. The regular structure of unknown scattering elements facilitates a dramatic reduction in the amount of storage and computation required for the FMM, both of which scale linearly with the number of scattering elements. In particular, the use of fast Fourier transforms to compute Green's function convolutions required for neighboring interactions lowers the often-significant cost of finest-level FMM computations and helps mitigate the dependence of FMM cost on finest-level box size. Numerical results demonstrate the efficiency of the composite method as the number of scattering elements in each finest-level box is increased. PMID:20835366

The fast multipole method and Fourier convolution for the solution of acoustic scattering on regular volumetric grids

NASA Astrophysics Data System (ADS)

Hesford, Andrew J.; Waag, Robert C.

2010-10-01

The fast multipole method (FMM) is applied to the solution of large-scale, three-dimensional acoustic scattering problems involving inhomogeneous objects defined on a regular grid. The grid arrangement is especially well suited to applications in which the scattering geometry is not known a priori and is reconstructed on a regular grid using iterative inverse scattering algorithms or other imaging techniques. The regular structure of unknown scattering elements facilitates a dramatic reduction in the amount of storage and computation required for the FMM, both of which scale linearly with the number of scattering elements. In particular, the use of fast Fourier transforms to compute Green's function convolutions required for neighboring interactions lowers the often-significant cost of finest-level FMM computations and helps mitigate the dependence of FMM cost on finest-level box size. Numerical results demonstrate the efficiency of the composite method as the number of scattering elements in each finest-level box is increased.

The Fast Multipole Method and Fourier Convolution for the Solution of Acoustic Scattering on Regular Volumetric Grids.

PubMed

Hesford, Andrew J; Waag, Robert C

2010-10-20

The fast multipole method (FMM) is applied to the solution of large-scale, three-dimensional acoustic scattering problems involving inhomogeneous objects defined on a regular grid. The grid arrangement is especially well suited to applications in which the scattering geometry is not known a priori and is reconstructed on a regular grid using iterative inverse scattering algorithms or other imaging techniques. The regular structure of unknown scattering elements facilitates a dramatic reduction in the amount of storage and computation required for the FMM, both of which scale linearly with the number of scattering elements. In particular, the use of fast Fourier transforms to compute Green's function convolutions required for neighboring interactions lowers the often-significant cost of finest-level FMM computations and helps mitigate the dependence of FMM cost on finest-level box size. Numerical results demonstrate the efficiency of the composite method as the number of scattering elements in each finest-level box is increased.

Ad Hoc modeling, expert problem solving, and R&T program evaluation

NASA Technical Reports Server (NTRS)

Silverman, B. G.; Liebowitz, J.; Moustakis, V. S.

1983-01-01

A simplified cost and time (SCAT) analysis program utilizing personal-computer technology is presented and demonstrated in the case of the NASA-Goddard end-to-end data system. The difficulties encountered in implementing complex program-selection and evaluation models in the research and technology field are outlined. The prototype SCAT system described here is designed to allow user-friendly ad hoc modeling in real time and at low cost. A worksheet constructed on the computer screen displays the critical parameters and shows how each is affected when one is altered experimentally. In the NASA case, satellite data-output and control requirements, ground-facility data-handling capabilities, and project priorities are intricately interrelated. Scenario studies of the effects of spacecraft phaseout or new spacecraft on throughput and delay parameters are shown. The use of a network of personal computers for higher-level coordination of decision-making processes is suggested, as a complement or alternative to complex large-scale modeling.

A parallel simulated annealing algorithm for standard cell placement on a hypercube computer

NASA Technical Reports Server (NTRS)

Jones, Mark Howard

1987-01-01

A parallel version of a simulated annealing algorithm is presented which is targeted to run on a hypercube computer. A strategy for mapping the cells in a two dimensional area of a chip onto processors in an n-dimensional hypercube is proposed such that both small and large distance moves can be applied. Two types of moves are allowed: cell exchanges and cell displacements. The computation of the cost function in parallel among all the processors in the hypercube is described along with a distributed data structure that needs to be stored in the hypercube to support parallel cost evaluation. A novel tree broadcasting strategy is used extensively in the algorithm for updating cell locations in the parallel environment. Studies on the performance of the algorithm on example industrial circuits show that it is faster and gives better final placement results than the uniprocessor simulated annealing algorithms. An improved uniprocessor algorithm is proposed which is based on the improved results obtained from parallelization of the simulated annealing algorithm.

Towards quantum chemistry on a quantum computer.

PubMed

Lanyon, B P; Whitfield, J D; Gillett, G G; Goggin, M E; Almeida, M P; Kassal, I; Biamonte, J D; Mohseni, M; Powell, B J; Barbieri, M; Aspuru-Guzik, A; White, A G

2010-02-01

Exact first-principles calculations of molecular properties are currently intractable because their computational cost grows exponentially with both the number of atoms and basis set size. A solution is to move to a radically different model of computing by building a quantum computer, which is a device that uses quantum systems themselves to store and process data. Here we report the application of the latest photonic quantum computer technology to calculate properties of the smallest molecular system: the hydrogen molecule in a minimal basis. We calculate the complete energy spectrum to 20 bits of precision and discuss how the technique can be expanded to solve large-scale chemical problems that lie beyond the reach of modern supercomputers. These results represent an early practical step toward a powerful tool with a broad range of quantum-chemical applications.

Accurate and efficient calculation of excitation energies with the active-space particle-particle random phase approximation

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

Zhang, Du; Yang, Weitao

An efficient method for calculating excitation energies based on the particle-particle random phase approximation (ppRPA) is presented. Neglecting the contributions from the high-lying virtual states and the low-lying core states leads to the significantly smaller active-space ppRPA matrix while keeping the error to within 0.05 eV from the corresponding full ppRPA excitation energies. The resulting computational cost is significantly reduced and becomes less than the construction of the non-local Fock exchange potential matrix in the self-consistent-field (SCF) procedure. With only a modest number of active orbitals, the original ppRPA singlet-triplet (ST) gaps as well as the low-lying single and doublemore » excitation energies can be accurately reproduced at much reduced computational costs, up to 100 times faster than the iterative Davidson diagonalization of the original full ppRPA matrix. For high-lying Rydberg excitations where the Davidson algorithm fails, the computational savings of active-space ppRPA with respect to the direct diagonalization is even more dramatic. The virtues of the underlying full ppRPA combined with the significantly lower computational cost of the active-space approach will significantly expand the applicability of the ppRPA method to calculate excitation energies at a cost of O(K^{4}), with a prefactor much smaller than a single SCF Hartree-Fock (HF)/hybrid functional calculation, thus opening up new possibilities for the quantum mechanical study of excited state electronic structure of large systems.« less

Accurate and efficient calculation of excitation energies with the active-space particle-particle random phase approximation

DOE PAGES

Zhang, Du; Yang, Weitao

2016-10-13

An efficient method for calculating excitation energies based on the particle-particle random phase approximation (ppRPA) is presented. Neglecting the contributions from the high-lying virtual states and the low-lying core states leads to the significantly smaller active-space ppRPA matrix while keeping the error to within 0.05 eV from the corresponding full ppRPA excitation energies. The resulting computational cost is significantly reduced and becomes less than the construction of the non-local Fock exchange potential matrix in the self-consistent-field (SCF) procedure. With only a modest number of active orbitals, the original ppRPA singlet-triplet (ST) gaps as well as the low-lying single and doublemore » excitation energies can be accurately reproduced at much reduced computational costs, up to 100 times faster than the iterative Davidson diagonalization of the original full ppRPA matrix. For high-lying Rydberg excitations where the Davidson algorithm fails, the computational savings of active-space ppRPA with respect to the direct diagonalization is even more dramatic. The virtues of the underlying full ppRPA combined with the significantly lower computational cost of the active-space approach will significantly expand the applicability of the ppRPA method to calculate excitation energies at a cost of O(K^{4}), with a prefactor much smaller than a single SCF Hartree-Fock (HF)/hybrid functional calculation, thus opening up new possibilities for the quantum mechanical study of excited state electronic structure of large systems.« less

Constructing a Foundational Platform Driven by Japan's K Supercomputer for Next-Generation Drug Design.

PubMed

Brown, J B; Nakatsui, Masahiko; Okuno, Yasushi

2014-12-01

The cost of pharmaceutical R&D has risen enormously, both worldwide and in Japan. However, Japan faces a particularly difficult situation in that its population is aging rapidly, and the cost of pharmaceutical R&D affects not only the industry but the entire medical system as well. To attempt to reduce costs, the newly launched K supercomputer is available for big data drug discovery and structural simulation-based drug discovery. We have implemented both primary (direct) and secondary (infrastructure, data processing) methods for the two types of drug discovery, custom tailored to maximally use the 88 128 compute nodes/CPUs of K, and evaluated the implementations. We present two types of results. In the first, we executed the virtual screening of nearly 19 billion compound-protein interactions, and calculated the accuracy of predictions against publicly available experimental data. In the second investigation, we implemented a very computationally intensive binding free energy algorithm, and found that comparison of our binding free energies was considerably accurate when validated against another type of publicly available experimental data. The common feature of both result types is the scale at which computations were executed. The frameworks presented in this article provide prospectives and applications that, while tuned to the computing resources available in Japan, are equally applicable to any equivalent large-scale infrastructure provided elsewhere. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Performance simulation for the design of solar heating and cooling systems

NASA Technical Reports Server (NTRS)

Mccormick, P. O.

1975-01-01

Suitable approaches for evaluating the performance and the cost of a solar heating and cooling system are considered, taking into account the value of a computer simulation concerning the entire system in connection with the large number of parameters involved. Operational relations concerning the collector efficiency in the case of a new improved collector and a reference collector are presented in a graph. Total costs for solar and conventional heating, ventilation, and air conditioning systems as a function of time are shown in another graph.

A large-grain mapping approach for multiprocessor systems through data flow model. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Kim, Hwa-Soo

1991-01-01

A large-grain level mapping method is presented of numerical oriented applications onto multiprocessor systems. The method is based on the large-grain data flow representation of the input application and it assumes a general interconnection topology of the multiprocessor system. The large-grain data flow model was used because such representation best exhibits inherited parallelism in many important applications, e.g., CFD models based on partial differential equations can be presented in large-grain data flow format, very effectively. A generalized interconnection topology of the multiprocessor architecture is considered, including such architectural issues as interprocessor communication cost, with the aim to identify the 'best matching' between the application and the multiprocessor structure. The objective is to minimize the total execution time of the input algorithm running on the target system. The mapping strategy consists of the following: (1) large-grain data flow graph generation from the input application using compilation techniques; (2) data flow graph partitioning into basic computation blocks; and (3) physical mapping onto the target multiprocessor using a priority allocation scheme for the computation blocks.

The application of cloud computing to scientific workflows: a study of cost and performance.

PubMed

Berriman, G Bruce; Deelman, Ewa; Juve, Gideon; Rynge, Mats; Vöckler, Jens-S

2013-01-28

The current model of transferring data from data centres to desktops for analysis will soon be rendered impractical by the accelerating growth in the volume of science datasets. Processing will instead often take place on high-performance servers co-located with data. Evaluations of how new technologies such as cloud computing would support such a new distributed computing model are urgently needed. Cloud computing is a new way of purchasing computing and storage resources on demand through virtualization technologies. We report here the results of investigations of the applicability of commercial cloud computing to scientific computing, with an emphasis on astronomy, including investigations of what types of applications can be run cheaply and efficiently on the cloud, and an example of an application well suited to the cloud: processing a large dataset to create a new science product.

The UCLA MEDLARS Computer System *

PubMed Central

Garvis, Francis J.

1966-01-01

Under a subcontract with UCLA the Planning Research Corporation has changed the MEDLARS system to make it possible to use the IBM 7094/7040 direct-couple computer instead of the Honeywell 800 for demand searches. The major tasks were the rewriting of the programs in COBOL and copying of the stored information on the narrower tapes that IBM computers require. (In the future NLM will copy the tapes for IBM computer users.) The differences in the software required by the two computers are noted. Major and costly revisions would be needed to adapt the large MEDLARS system to the smaller IBM 1401 and 1410 computers. In general, MEDLARS is transferrable to other computers of the IBM 7000 class, the new IBM 360, and those of like size, such as the CDC 1604 or UNIVAC 1108, although additional changes are necessary. Potential future improvements are suggested. PMID:5901355

[Monetary value of the human costs of road traffic injuries in Spain].

PubMed

Martínez Pérez, Jorge Eduardo; Sánchez Martínez, Fernando Ignacio; Abellán Perpiñán, José María; Pinto Prades, José Luis

2015-09-01

Cost-benefit analyses in the field of road safety compute human costs as a key component of total costs. The present article presents two studies promoted by the Directorate-General for Traffic aimed at obtaining official values for the costs associated with fatal and non-fatal traffic injuries in Spain. We combined the contingent valuation approach and the (modified) standard gamble technique in two surveys administered to large representative samples (n1=2,020, n2=2,000) of the Spanish population. The monetary value of preventing a fatality was estimated to be 1.4 million euros. Values of 219,000 and 6,100 euros were obtained for minor and severe non-fatal injuries, respectively. These figures are comparable to those observed in neighboring countries. Copyright © 2014 SESPAS. Published by Elsevier Espana. All rights reserved.

Cloud computing in medical imaging.

PubMed

Kagadis, George C; Kloukinas, Christos; Moore, Kevin; Philbin, Jim; Papadimitroulas, Panagiotis; Alexakos, Christos; Nagy, Paul G; Visvikis, Dimitris; Hendee, William R

2013-07-01

Over the past century technology has played a decisive role in defining, driving, and reinventing procedures, devices, and pharmaceuticals in healthcare. Cloud computing has been introduced only recently but is already one of the major topics of discussion in research and clinical settings. The provision of extensive, easily accessible, and reconfigurable resources such as virtual systems, platforms, and applications with low service cost has caught the attention of many researchers and clinicians. Healthcare researchers are moving their efforts to the cloud, because they need adequate resources to process, store, exchange, and use large quantities of medical data. This Vision 20/20 paper addresses major questions related to the applicability of advanced cloud computing in medical imaging. The paper also considers security and ethical issues that accompany cloud computing.

Acoustic environmental accuracy requirements for response determination

NASA Technical Reports Server (NTRS)

Pettitt, M. R.

1983-01-01

A general purpose computer program was developed for the prediction of vehicle interior noise. This program, named VIN, has both modal and statistical energy analysis capabilities for structural/acoustic interaction analysis. The analytic models and their computer implementation were verified through simple test cases with well-defined experimental results. The model was also applied in a space shuttle payload bay launch acoustics prediction study. The computer program processes large and small problems with equal efficiency because all arrays are dynamically sized by program input variables at run time. A data base is built and easily accessed for design studies. The data base significantly reduces the computational costs of such studies by allowing the reuse of the still-valid calculated parameters of previous iterations.

The HEPCloud Facility: elastic computing for High Energy Physics - The NOvA Use Case

NASA Astrophysics Data System (ADS)

Fuess, S.; Garzoglio, G.; Holzman, B.; Kennedy, R.; Norman, A.; Timm, S.; Tiradani, A.

2017-10-01

The need for computing in the HEP community follows cycles of peaks and valleys mainly driven by conference dates, accelerator shutdown, holiday schedules, and other factors. Because of this, the classical method of provisioning these resources at providing facilities has drawbacks such as potential overprovisioning. As the appetite for computing increases, however, so does the need to maximize cost efficiency by developing a model for dynamically provisioning resources only when needed. To address this issue, the HEPCloud project was launched by the Fermilab Scientific Computing Division in June 2015. Its goal is to develop a facility that provides a common interface to a variety of resources, including local clusters, grids, high performance computers, and community and commercial Clouds. Initially targeted experiments include CMS and NOvA, as well as other Fermilab stakeholders. In its first phase, the project has demonstrated the use of the “elastic” provisioning model offered by commercial clouds, such as Amazon Web Services. In this model, resources are rented and provisioned automatically over the Internet upon request. In January 2016, the project demonstrated the ability to increase the total amount of global CMS resources by 58,000 cores from 150,000 cores - a 38 percent increase - in preparation for the Recontres de Moriond. In March 2016, the NOvA experiment has also demonstrated resource burst capabilities with an additional 7,300 cores, achieving a scale almost four times as large as the local allocated resources and utilizing the local AWS s3 storage to optimize data handling operations and costs. NOvA was using the same familiar services used for local computations, such as data handling and job submission, in preparation for the Neutrino 2016 conference. In both cases, the cost was contained by the use of the Amazon Spot Instance Market and the Decision Engine, a HEPCloud component that aims at minimizing cost and job interruption. This paper describes the Fermilab HEPCloud Facility and the challenges overcome for the CMS and NOvA communities.

Activity-based costing: a practical model for cost calculation in radiotherapy.

PubMed

Lievens, Yolande; van den Bogaert, Walter; Kesteloot, Katrien

2003-10-01

The activity-based costing method was used to compute radiotherapy costs. This report describes the model developed, the calculated costs, and possible applications for the Leuven radiotherapy department. Activity-based costing is an advanced cost calculation technique that allocates resource costs to products based on activity consumption. In the Leuven model, a complex allocation principle with a large diversity of cost drivers was avoided by introducing an extra allocation step between activity groups and activities. A straightforward principle of time consumption, weighed by some factors of treatment complexity, was used. The model was developed in an iterative way, progressively defining the constituting components (costs, activities, products, and cost drivers). Radiotherapy costs are predominantly determined by personnel and equipment cost. Treatment-related activities consume the greatest proportion of the resource costs, with treatment delivery the most important component. This translates into products that have a prolonged total or daily treatment time being the most costly. The model was also used to illustrate the impact of changes in resource costs and in practice patterns. The presented activity-based costing model is a practical tool to evaluate the actual cost structure of a radiotherapy department and to evaluate possible resource or practice changes.

Petascale self-consistent electromagnetic computations using scalable and accurate algorithms for complex structures

NASA Astrophysics Data System (ADS)

Cary, John R.; Abell, D.; Amundson, J.; Bruhwiler, D. L.; Busby, R.; Carlsson, J. A.; Dimitrov, D. A.; Kashdan, E.; Messmer, P.; Nieter, C.; Smithe, D. N.; Spentzouris, P.; Stoltz, P.; Trines, R. M.; Wang, H.; Werner, G. R.

2006-09-01

As the size and cost of particle accelerators escalate, high-performance computing plays an increasingly important role; optimization through accurate, detailed computermodeling increases performance and reduces costs. But consequently, computer simulations face enormous challenges. Early approximation methods, such as expansions in distance from the design orbit, were unable to supply detailed accurate results, such as in the computation of wake fields in complex cavities. Since the advent of message-passing supercomputers with thousands of processors, earlier approximations are no longer necessary, and it is now possible to compute wake fields, the effects of dampers, and self-consistent dynamics in cavities accurately. In this environment, the focus has shifted towards the development and implementation of algorithms that scale to large numbers of processors. So-called charge-conserving algorithms evolve the electromagnetic fields without the need for any global solves (which are difficult to scale up to many processors). Using cut-cell (or embedded) boundaries, these algorithms can simulate the fields in complex accelerator cavities with curved walls. New implicit algorithms, which are stable for any time-step, conserve charge as well, allowing faster simulation of structures with details small compared to the characteristic wavelength. These algorithmic and computational advances have been implemented in the VORPAL7 Framework, a flexible, object-oriented, massively parallel computational application that allows run-time assembly of algorithms and objects, thus composing an application on the fly.

Sub-Selective Quantization for Learning Binary Codes in Large-Scale Image Search.

PubMed

Li, Yeqing; Liu, Wei; Huang, Junzhou

2018-06-01

Recently with the explosive growth of visual content on the Internet, large-scale image search has attracted intensive attention. It has been shown that mapping high-dimensional image descriptors to compact binary codes can lead to considerable efficiency gains in both storage and performing similarity computation of images. However, most existing methods still suffer from expensive training devoted to large-scale binary code learning. To address this issue, we propose a sub-selection based matrix manipulation algorithm, which can significantly reduce the computational cost of code learning. As case studies, we apply the sub-selection algorithm to several popular quantization techniques including cases using linear and nonlinear mappings. Crucially, we can justify the resulting sub-selective quantization by proving its theoretic properties. Extensive experiments are carried out on three image benchmarks with up to one million samples, corroborating the efficacy of the sub-selective quantization method in terms of image retrieval.

Parallel Simulation of Unsteady Turbulent Flames

NASA Technical Reports Server (NTRS)

Menon, Suresh

1996-01-01

Time-accurate simulation of turbulent flames in high Reynolds number flows is a challenging task since both fluid dynamics and combustion must be modeled accurately. To numerically simulate this phenomenon, very large computer resources (both time and memory) are required. Although current vector supercomputers are capable of providing adequate resources for simulations of this nature, the high cost and their limited availability, makes practical use of such machines less than satisfactory. At the same time, the explicit time integration algorithms used in unsteady flow simulations often possess a very high degree of parallelism, making them very amenable to efficient implementation on large-scale parallel computers. Under these circumstances, distributed memory parallel computers offer an excellent near-term solution for greatly increased computational speed and memory, at a cost that may render the unsteady simulations of the type discussed above more feasible and affordable.This paper discusses the study of unsteady turbulent flames using a simulation algorithm that is capable of retaining high parallel efficiency on distributed memory parallel architectures. Numerical studies are carried out using large-eddy simulation (LES). In LES, the scales larger than the grid are computed using a time- and space-accurate scheme, while the unresolved small scales are modeled using eddy viscosity based subgrid models. This is acceptable for the moment/energy closure since the small scales primarily provide a dissipative mechanism for the energy transferred from the large scales. However, for combustion to occur, the species must first undergo mixing at the small scales and then come into molecular contact. Therefore, global models cannot be used. Recently, a new model for turbulent combustion was developed, in which the combustion is modeled, within the subgrid (small-scales) using a methodology that simulates the mixing and the molecular transport and the chemical kinetics within each LES grid cell. Finite-rate kinetics can be included without any closure and this approach actually provides a means to predict the turbulent rates and the turbulent flame speed. The subgrid combustion model requires resolution of the local time scales associated with small-scale mixing, molecular diffusion and chemical kinetics and, therefore, within each grid cell, a significant amount of computations must be carried out before the large-scale (LES resolved) effects are incorporated. Therefore, this approach is uniquely suited for parallel processing and has been implemented on various systems such as: Intel Paragon, IBM SP-2, Cray T3D and SGI Power Challenge (PC) using the system independent Message Passing Interface (MPI) compiler. In this paper, timing data on these machines is reported along with some characteristic results.

Computer-Aided Surgical Simulation in Head and Neck Reconstruction: A Cost Comparison among Traditional, In-House, and Commercial Options.

PubMed

Li, Sean S; Copeland-Halperin, Libby R; Kaminsky, Alexander J; Li, Jihui; Lodhi, Fahad K; Miraliakbari, Reza

2018-06-01

 Computer-aided surgical simulation (CASS) has redefined surgery, improved precision and reduced the reliance on intraoperative trial-and-error manipulations. CASS is provided by third-party services; however, it may be cost-effective for some hospitals to develop in-house programs. This study provides the first cost analysis comparison among traditional (no CASS), commercial CASS, and in-house CASS for head and neck reconstruction.  The costs of three-dimensional (3D) pre-operative planning for mandibular and maxillary reconstructions were obtained from an in-house CASS program at our large tertiary care hospital in Northern Virginia, as well as a commercial provider (Synthes, Paoli, PA). A cost comparison was performed among these modalities and extrapolated in-house CASS costs were derived. The calculations were based on estimated CASS use with cost structures similar to our institution and sunk costs were amortized over 10 years.  Average operating room time was estimated at 10 hours, with an average of 2 hours saved with CASS. The hourly cost to the hospital for the operating room (including anesthesia and other ancillary costs) was estimated at $4,614/hour. Per case, traditional cases were $46,140, commercial CASS cases were $40,951, and in-house CASS cases were $38,212. Annual in-house CASS costs were $39,590.  CASS reduced operating room time, likely due to improved efficiency and accuracy. Our data demonstrate that hospitals with similar cost structure as ours, performing greater than 27 cases of 3D head and neck reconstructions per year can see a financial benefit from developing an in-house CASS program. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

A knowledge-based system with learning for computer communication network design

NASA Technical Reports Server (NTRS)

Pierre, Samuel; Hoang, Hai Hoc; Tropper-Hausen, Evelyne

1990-01-01

Computer communication network design is well-known as complex and hard. For that reason, the most effective methods used to solve it are heuristic. Weaknesses of these techniques are listed and a new approach based on artificial intelligence for solving this problem is presented. This approach is particularly recommended for large packet switched communication networks, in the sense that it permits a high degree of reliability and offers a very flexible environment dealing with many relevant design parameters such as link cost, link capacity, and message delay.

Putting Order Into the Cloud: Object-oriented UML-based Rule Enforcement for Document and Application Organization

DTIC Science & Technology

2010-09-01

Cloud computing describes a new distributed computing paradigm for IT data and services that involves over-the-Internet provision of dynamically scalable and often virtualized resources. While cost reduction and flexibility in storage, services, and maintenance are important considerations when deciding on whether or how to migrate data and applications to the cloud, large organizations like the Department of Defense need to consider the organization and structure of data on the cloud and the operations on such data in order to reap the full benefit of cloud

Air Force Information Management (IM): A 1990 Snapshot and 1995 Future Look at Air Force IM Needs and Preferred Education/Training Approaches

DTIC Science & Technology

1990-12-01

small powerful computers to businesses and homes on an international scale (29:74). Relatively low cost, high computing power , and ease of operation were...is performed. In large part, today’s AF IM professional has been inundated with powerful new technologies which were rapidly introduced and inserted...state that, "In a survey of five years of MIS research, we fouind the averane levels of statistical power to be relatively low (5:104). In their own

Large-Scale Calculations for Material Sciences Using Accelerators to Improve Time- and Energy-to-Solution

DOE PAGES

Eisenbach, Markus

2017-01-01

A major impediment to deploying next-generation high-performance computational systems is the required electrical power, often measured in units of megawatts. The solution to this problem is driving the introduction of novel machine architectures, such as those employing many-core processors and specialized accelerators. In this article, we describe the use of a hybrid accelerated architecture to achieve both reduced time to solution and the associated reduction in the electrical cost for a state-of-the-art materials science computation.

Distributed sensor networks: a cellular nonlinear network perspective.

PubMed

Haenggi, Martin

2003-12-01

Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.

Large Scale Many-Body Perturbation Theory calculations: methodological developments, data collections, validation

NASA Astrophysics Data System (ADS)

Govoni, Marco; Galli, Giulia

Green's function based many-body perturbation theory (MBPT) methods are well established approaches to compute quasiparticle energies and electronic lifetimes. However, their application to large systems - for instance to heterogeneous systems, nanostructured, disordered, and defective materials - has been hindered by high computational costs. We will discuss recent MBPT methodological developments leading to an efficient formulation of electron-electron and electron-phonon interactions, and that can be applied to systems with thousands of electrons. Results using a formulation that does not require the explicit calculation of virtual states, nor the storage and inversion of large dielectric matrices will be presented. We will discuss data collections obtained using the WEST code, the advantages of the algorithms used in WEST over standard techniques, and the parallel performance. Work done in collaboration with I. Hamada, R. McAvoy, P. Scherpelz, and H. Zheng. This work was supported by MICCoM, as part of the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division and by ANL.

Really Large Scale Computer Graphic Projection Using Lasers and Laser Substitutes

NASA Astrophysics Data System (ADS)

Rother, Paul

1989-07-01

This paper reflects on past laser projects to display vector scanned computer graphic images onto very large and irregular surfaces. Since the availability of microprocessors and high powered visible lasers, very large scale computer graphics projection have become a reality. Due to the independence from a focusing lens, lasers easily project onto distant and irregular surfaces and have been used for amusement parks, theatrical performances, concert performances, industrial trade shows and dance clubs. Lasers have been used to project onto mountains, buildings, 360° globes, clouds of smoke and water. These methods have proven successful in installations at: Epcot Theme Park in Florida; Stone Mountain Park in Georgia; 1984 Olympics in Los Angeles; hundreds of Corporate trade shows and thousands of musical performances. Using new ColorRayTM technology, the use of costly and fragile lasers is no longer necessary. Utilizing fiber optic technology, the functionality of lasers can be duplicated for new and exciting projection possibilities. The use of ColorRayTM technology has enjoyed worldwide recognition in conjunction with Pink Floyd and George Michaels' world wide tours.

Closha: bioinformatics workflow system for the analysis of massive sequencing data.

PubMed

Ko, GunHwan; Kim, Pan-Gyu; Yoon, Jongcheol; Han, Gukhee; Park, Seong-Jin; Song, Wangho; Lee, Byungwook

2018-02-19

While next-generation sequencing (NGS) costs have fallen in recent years, the cost and complexity of computation remain substantial obstacles to the use of NGS in bio-medical care and genomic research. The rapidly increasing amounts of data available from the new high-throughput methods have made data processing infeasible without automated pipelines. The integration of data and analytic resources into workflow systems provides a solution to the problem by simplifying the task of data analysis. To address this challenge, we developed a cloud-based workflow management system, Closha, to provide fast and cost-effective analysis of massive genomic data. We implemented complex workflows making optimal use of high-performance computing clusters. Closha allows users to create multi-step analyses using drag and drop functionality and to modify the parameters of pipeline tools. Users can also import the Galaxy pipelines into Closha. Closha is a hybrid system that enables users to use both analysis programs providing traditional tools and MapReduce-based big data analysis programs simultaneously in a single pipeline. Thus, the execution of analytics algorithms can be parallelized, speeding up the whole process. We also developed a high-speed data transmission solution, KoDS, to transmit a large amount of data at a fast rate. KoDS has a file transfer speed of up to 10 times that of normal FTP and HTTP. The computer hardware for Closha is 660 CPU cores and 800 TB of disk storage, enabling 500 jobs to run at the same time. Closha is a scalable, cost-effective, and publicly available web service for large-scale genomic data analysis. Closha supports the reliable and highly scalable execution of sequencing analysis workflows in a fully automated manner. Closha provides a user-friendly interface to all genomic scientists to try to derive accurate results from NGS platform data. The Closha cloud server is freely available for use from http://closha.kobic.re.kr/ .

A non-voxel-based broad-beam (NVBB) framework for IMRT treatment planning.

PubMed

Lu, Weiguo

2010-12-07

We present a novel framework that enables very large scale intensity-modulated radiation therapy (IMRT) planning in limited computation resources with improvements in cost, plan quality and planning throughput. Current IMRT optimization uses a voxel-based beamlet superposition (VBS) framework that requires pre-calculation and storage of a large amount of beamlet data, resulting in large temporal and spatial complexity. We developed a non-voxel-based broad-beam (NVBB) framework for IMRT capable of direct treatment parameter optimization (DTPO). In this framework, both objective function and derivative are evaluated based on the continuous viewpoint, abandoning 'voxel' and 'beamlet' representations. Thus pre-calculation and storage of beamlets are no longer needed. The NVBB framework has linear complexities (O(N(3))) in both space and time. The low memory, full computation and data parallelization nature of the framework render its efficient implementation on the graphic processing unit (GPU). We implemented the NVBB framework and incorporated it with the TomoTherapy treatment planning system (TPS). The new TPS runs on a single workstation with one GPU card (NVBB-GPU). Extensive verification/validation tests were performed in house and via third parties. Benchmarks on dose accuracy, plan quality and throughput were compared with the commercial TomoTherapy TPS that is based on the VBS framework and uses a computer cluster with 14 nodes (VBS-cluster). For all tests, the dose accuracy of these two TPSs is comparable (within 1%). Plan qualities were comparable with no clinically significant difference for most cases except that superior target uniformity was seen in the NVBB-GPU for some cases. However, the planning time using the NVBB-GPU was reduced many folds over the VBS-cluster. In conclusion, we developed a novel NVBB framework for IMRT optimization. The continuous viewpoint and DTPO nature of the algorithm eliminate the need for beamlets and lead to better plan quality. The computation parallelization on a GPU instead of a computer cluster significantly reduces hardware and service costs. Compared with using the current VBS framework on a computer cluster, the planning time is significantly reduced using the NVBB framework on a single workstation with a GPU card.

TethysCluster: A comprehensive approach for harnessing cloud resources for hydrologic modeling

NASA Astrophysics Data System (ADS)

Nelson, J.; Jones, N.; Ames, D. P.

2015-12-01

Advances in water resources modeling are improving the information that can be supplied to support decisions affecting the safety and sustainability of society. However, as water resources models become more sophisticated and data-intensive they require more computational power to run. Purchasing and maintaining the computing facilities needed to support certain modeling tasks has been cost-prohibitive for many organizations. With the advent of the cloud, the computing resources needed to address this challenge are now available and cost-effective, yet there still remains a significant technical barrier to leverage these resources. This barrier inhibits many decision makers and even trained engineers from taking advantage of the best science and tools available. Here we present the Python tools TethysCluster and CondorPy, that have been developed to lower the barrier to model computation in the cloud by providing (1) programmatic access to dynamically scalable computing resources, (2) a batch scheduling system to queue and dispatch the jobs to the computing resources, (3) data management for job inputs and outputs, and (4) the ability to dynamically create, submit, and monitor computing jobs. These Python tools leverage the open source, computing-resource management, and job management software, HTCondor, to offer a flexible and scalable distributed-computing environment. While TethysCluster and CondorPy can be used independently to provision computing resources and perform large modeling tasks, they have also been integrated into Tethys Platform, a development platform for water resources web apps, to enable computing support for modeling workflows and decision-support systems deployed as web apps.

Improving the performance of extreme learning machine for hyperspectral image classification

NASA Astrophysics Data System (ADS)

Li, Jiaojiao; Du, Qian; Li, Wei; Li, Yunsong

2015-05-01

Extreme learning machine (ELM) and kernel ELM (KELM) can offer comparable performance as the standard powerful classifier―support vector machine (SVM), but with much lower computational cost due to extremely simple training step. However, their performance may be sensitive to several parameters, such as the number of hidden neurons. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a relationship can be easily estimated with two small training sets and extended to large training sets so as to greatly reduce computational cost. Other parameters, such as the steepness parameter in the sigmodal activation function and regularization parameter in the KELM, are also investigated. The experimental results show that classification performance is sensitive to these parameters; fortunately, simple selections will result in suboptimal performance.

Computational solvation analysis of biomolecules in aqueous ionic liquid mixtures : From large flexible proteins to small rigid drugs.

PubMed

Zeindlhofer, Veronika; Schröder, Christian

2018-06-01

Based on their tunable properties, ionic liquids attracted significant interest to replace conventional, organic solvents in biomolecular applications. Following a Gartner cycle, the expectations on this new class of solvents dropped after the initial hype due to the high viscosity, hydrolysis, and toxicity problems as well as their high cost. Since not all possible combinations of cations and anions can be tested experimentally, fundamental knowledge on the interaction of the ionic liquid ions with water and with biomolecules is mandatory to optimize the solvation behavior, the biodegradability, and the costs of the ionic liquid. Here, we report on current computational approaches to characterize the impact of the ionic liquid ions on the structure and dynamics of the biomolecule and its solvation layer to explore the full potential of ionic liquids.

Using parallel banded linear system solvers in generalized eigenvalue problems

NASA Technical Reports Server (NTRS)

Zhang, Hong; Moss, William F.

1993-01-01

Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speed-up is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented.

Projectors: The Big Picture

ERIC Educational Resources Information Center

Gamble-Risley, Michelle

2006-01-01

In the past, projection systems were large, heavy, and unwieldy and cost $3,000 to $5,000. Setup was fraught with the challenges of multiple wires plugged into the backs of desktop computers, often causing confusion about what went where. Systems were sometimes so difficult to set up that teachers had to spend pre-class time putting them together.…

Markets and Models for Large-Scale Courseware Development.

ERIC Educational Resources Information Center

Bunderson, C. Victor

Computer-assisted instruction (CAI) is not making an important, visible impact on the educational system of this country. Though its instructional value has been proven time after time, the high cost of the hardware and the lack of quality courseware is preventing CAI from becoming a market success. In order for CAI to reach its market potential…

Situational Awareness from a Low-Cost Camera System

NASA Technical Reports Server (NTRS)

Freudinger, Lawrence C.; Ward, David; Lesage, John

2010-01-01

A method gathers scene information from a low-cost camera system. Existing surveillance systems using sufficient cameras for continuous coverage of a large field necessarily generate enormous amounts of raw data. Digitizing and channeling that data to a central computer and processing it in real time is difficult when using low-cost, commercially available components. A newly developed system is located on a combined power and data wire to form a string-of-lights camera system. Each camera is accessible through this network interface using standard TCP/IP networking protocols. The cameras more closely resemble cell-phone cameras than traditional security camera systems. Processing capabilities are built directly onto the camera backplane, which helps maintain a low cost. The low power requirements of each camera allow the creation of a single imaging system comprising over 100 cameras. Each camera has built-in processing capabilities to detect events and cooperatively share this information with neighboring cameras. The location of the event is reported to the host computer in Cartesian coordinates computed from data correlation across multiple cameras. In this way, events in the field of view can present low-bandwidth information to the host rather than high-bandwidth bitmap data constantly being generated by the cameras. This approach offers greater flexibility than conventional systems, without compromising performance through using many small, low-cost cameras with overlapping fields of view. This means significant increased viewing without ignoring surveillance areas, which can occur when pan, tilt, and zoom cameras look away. Additionally, due to the sharing of a single cable for power and data, the installation costs are lower. The technology is targeted toward 3D scene extraction and automatic target tracking for military and commercial applications. Security systems and environmental/ vehicular monitoring systems are also potential applications.

Improving Design Efficiency for Large-Scale Heterogeneous Circuits

NASA Astrophysics Data System (ADS)

Gregerson, Anthony

Despite increases in logic density, many Big Data applications must still be partitioned across multiple computing devices in order to meet their strict performance requirements. Among the most demanding of these applications is high-energy physics (HEP), which uses complex computing systems consisting of thousands of FPGAs and ASICs to process the sensor data created by experiments at particles accelerators such as the Large Hadron Collider (LHC). Designing such computing systems is challenging due to the scale of the systems, the exceptionally high-throughput and low-latency performance constraints that necessitate application-specific hardware implementations, the requirement that algorithms are efficiently partitioned across many devices, and the possible need to update the implemented algorithms during the lifetime of the system. In this work, we describe our research to develop flexible architectures for implementing such large-scale circuits on FPGAs. In particular, this work is motivated by (but not limited in scope to) high-energy physics algorithms for the Compact Muon Solenoid (CMS) experiment at the LHC. To make efficient use of logic resources in multi-FPGA systems, we introduce Multi-Personality Partitioning, a novel form of the graph partitioning problem, and present partitioning algorithms that can significantly improve resource utilization on heterogeneous devices while also reducing inter-chip connections. To reduce the high communication costs of Big Data applications, we also introduce Information-Aware Partitioning, a partitioning method that analyzes the data content of application-specific circuits, characterizes their entropy, and selects circuit partitions that enable efficient compression of data between chips. We employ our information-aware partitioning method to improve the performance of the hardware validation platform for evaluating new algorithms for the CMS experiment. Together, these research efforts help to improve the efficiency and decrease the cost of the developing large-scale, heterogeneous circuits needed to enable large-scale application in high-energy physics and other important areas.

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

Visualization assisted by parallel processing

NASA Astrophysics Data System (ADS)

Lange, B.; Rey, H.; Vasques, X.; Puech, W.; Rodriguez, N.

2011-01-01

This paper discusses the experimental results of our visualization model for data extracted from sensors. The objective of this paper is to find a computationally efficient method to produce a real time rendering visualization for a large amount of data. We develop visualization method to monitor temperature variance of a data center. Sensors are placed on three layers and do not cover all the room. We use particle paradigm to interpolate data sensors. Particles model the "space" of the room. In this work we use a partition of the particle set, using two mathematical methods: Delaunay triangulation and Voronoý cells. Avis and Bhattacharya present these two algorithms in. Particles provide information on the room temperature at different coordinates over time. To locate and update particles data we define a computational cost function. To solve this function in an efficient way, we use a client server paradigm. Server computes data and client display this data on different kind of hardware. This paper is organized as follows. The first part presents related algorithm used to visualize large flow of data. The second part presents different platforms and methods used, which was evaluated in order to determine the better solution for the task proposed. The benchmark use the computational cost of our algorithm that formed based on located particles compared to sensors and on update of particles value. The benchmark was done on a personal computer using CPU, multi core programming, GPU programming and hybrid GPU/CPU. GPU programming method is growing in the research field; this method allows getting a real time rendering instates of a precompute rendering. For improving our results, we compute our algorithm on a High Performance Computing (HPC), this benchmark was used to improve multi-core method. HPC is commonly used in data visualization (astronomy, physic, etc) for improving the rendering and getting real-time.

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

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

Van Beeumen, Roel; Williams-Young, David B.; Kasper, Joseph M.

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comesmore » at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.« less

A Machine Learning Method for the Prediction of Receptor Activation in the Simulation of Synapses

PubMed Central

Montes, Jesus; Gomez, Elena; Merchán-Pérez, Angel; DeFelipe, Javier; Peña, Jose-Maria

2013-01-01

Chemical synaptic transmission involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. However, these methods are computationally expensive, even when used with simplified models, preventing their use in large-scale and multi-scale simulations of complex neuronal systems that may involve large numbers of synaptic connections. We have developed a machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the percentage of open synaptic receptors as a function of time since the release of the neurotransmitter, with considerably lower computational cost compared with the conventional Monte Carlo alternative. The method is designed to learn patterns and general principles from a corpus of previously generated Monte Carlo simulations of synapses covering a wide range of structural and functional characteristics. These patterns are later used as a predictive model of the behavior of synapses under different conditions without the need for additional computationally expensive Monte Carlo simulations. This is performed in five stages: data sampling, fold creation, machine learning, validation and curve fitting. The resulting procedure is accurate, automatic, and it is general enough to predict synapse behavior under experimental conditions that are different to the ones it has been trained on. Since our method efficiently reproduces the results that can be obtained with Monte Carlo simulations at a considerably lower computational cost, it is suitable for the simulation of high numbers of synapses and it is therefore an excellent tool for multi-scale simulations. PMID:23894367

Transformational electronics: a powerful way to revolutionize our information world

NASA Astrophysics Data System (ADS)

Rojas, Jhonathan P.; Torres Sevilla, Galo A.; Ghoneim, Mohamed T.; Hussain, Aftab M.; Ahmed, Sally M.; Nassar, Joanna M.; Bahabry, Rabab R.; Nour, Maha; Kutbee, Arwa T.; Byas, Ernesto; Al-Saif, Bidoor; Alamri, Amal M.; Hussain, Muhammad M.

2014-06-01

With the emergence of cloud computation, we are facing the rising waves of big data. It is our time to leverage such opportunity by increasing data usage both by man and machine. We need ultra-mobile computation with high data processing speed, ultra-large memory, energy efficiency and multi-functionality. Additionally, we have to deploy energy-efficient multi-functional 3D ICs for robust cyber-physical system establishment. To achieve such lofty goals we have to mimic human brain, which is inarguably the world's most powerful and energy efficient computer. Brain's cortex has folded architecture to increase surface area in an ultra-compact space to contain its neuron and synapses. Therefore, it is imperative to overcome two integration challenges: (i) finding out a low-cost 3D IC fabrication process and (ii) foldable substrates creation with ultra-large-scale-integration of high performance energy efficient electronics. Hence, we show a low-cost generic batch process based on trench-protect-peel-recycle to fabricate rigid and flexible 3D ICs as well as high performance flexible electronics. As of today we have made every single component to make a fully flexible computer including non-planar state-of-the-art FinFETs. Additionally we have demonstrated various solid-state memory, movable MEMS devices, energy harvesting and storage components. To show the versatility of our process, we have extended our process towards other inorganic semiconductor substrates such as silicon germanium and III-V materials. Finally, we report first ever fully flexible programmable silicon based microprocessor towards foldable brain computation and wirelessly programmable stretchable and flexible thermal patch for pain management for smart bionics.

12 CFR 602.12 - Fees.

Code of Federal Regulations, 2011 CFR

2011-01-01

... of the employee doing the work. (2) For computer searches for records, the direct costs of computer... $15.00. Fee Amounts Table Type of fee Amount of fee Manual Search and Review Pro rated Salary Costs. Computer Search Direct Costs. Photocopy $0.15 a page. Other Reproduction Costs Direct Costs. Elective...

An Unequal Secure Encryption Scheme for H.264/AVC Video Compression Standard

NASA Astrophysics Data System (ADS)

Fan, Yibo; Wang, Jidong; Ikenaga, Takeshi; Tsunoo, Yukiyasu; Goto, Satoshi

H.264/AVC is the newest video coding standard. There are many new features in it which can be easily used for video encryption. In this paper, we propose a new scheme to do video encryption for H.264/AVC video compression standard. We define Unequal Secure Encryption (USE) as an approach that applies different encryption schemes (with different security strength) to different parts of compressed video data. This USE scheme includes two parts: video data classification and unequal secure video data encryption. Firstly, we classify the video data into two partitions: Important data partition and unimportant data partition. Important data partition has small size with high secure protection, while unimportant data partition has large size with low secure protection. Secondly, we use AES as a block cipher to encrypt the important data partition and use LEX as a stream cipher to encrypt the unimportant data partition. AES is the most widely used symmetric cryptography which can ensure high security. LEX is a new stream cipher which is based on AES and its computational cost is much lower than AES. In this way, our scheme can achieve both high security and low computational cost. Besides the USE scheme, we propose a low cost design of hybrid AES/LEX encryption module. Our experimental results show that the computational cost of the USE scheme is low (about 25% of naive encryption at Level 0 with VEA used). The hardware cost for hybrid AES/LEX module is 4678 Gates and the AES encryption throughput is about 50Mbps.

Improving the cost-effectiveness of a healthcare system for depressive disorders by implementing telemedicine: a health economic modeling study.

PubMed

Lokkerbol, Joran; Adema, Dirk; Cuijpers, Pim; Reynolds, Charles F; Schulz, Richard; Weehuizen, Rifka; Smit, Filip

2014-03-01

Depressive disorders are significant causes of disease burden and are associated with substantial economic costs. It is therefore important to design a healthcare system that can effectively manage depression at sustainable costs. This article computes the benefit-to-cost ratio of the current Dutch healthcare system for depression, and investigates whether offering more online preventive interventions improves the cost-effectiveness overall. A health economic (Markov) model was used to synthesize clinical and economic evidence and to compute population-level costs and effects of interventions. The model compared a base case scenario without preventive telemedicine and alternative scenarios with preventive telemedicine. The central outcome was the benefit-to-cost ratio, also known as return-on-investment (ROI). In terms of ROI, a healthcare system with preventive telemedicine for depressive disorders offers better value for money than a healthcare system without Internet-based prevention. Overall, the ROI increases from €1.45 ($1.72) in the base case scenario to €1.76 ($2.09) in the alternative scenario in which preventive telemedicine is offered. In a scenario in which the costs of offering preventive telemedicine are balanced by reducing the expenditures for curative interventions, ROI increases to €1.77 ($2.10), while keeping the healthcare budget constant. For a healthcare system for depressive disorders to remain economically sustainable, its cost-benefit ratio needs to be improved. Offering preventive telemedicine at a large scale is likely to introduce such an improvement. Copyright © 2014 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Projected role of advanced computational aerodynamic methods at the Lockheed-Georgia company

NASA Technical Reports Server (NTRS)

Lores, M. E.

1978-01-01

Experience with advanced computational methods being used at the Lockheed-Georgia Company to aid in the evaluation and design of new and modified aircraft indicates that large and specialized computers will be needed to make advanced three-dimensional viscous aerodynamic computations practical. The Numerical Aerodynamic Simulation Facility should be used to provide a tool for designing better aerospace vehicles while at the same time reducing development costs by performing computations using Navier-Stokes equations solution algorithms and permitting less sophisticated but nevertheless complex calculations to be made efficiently. Configuration definition procedures and data output formats can probably best be defined in cooperation with industry, therefore, the computer should handle many remote terminals efficiently. The capability of transferring data to and from other computers needs to be provided. Because of the significant amount of input and output associated with 3-D viscous flow calculations and because of the exceedingly fast computation speed envisioned for the computer, special attention should be paid to providing rapid, diversified, and efficient input and output.

2002 Computing and Interdisciplinary Systems Office Review and Planning Meeting

NASA Technical Reports Server (NTRS)

Lytle, John; Follen, Gregory; Lopez, Isaac; Veres, Joseph; Lavelle, Thomas; Sehra, Arun; Freeh, Josh; Hah, Chunill

2003-01-01

The technologies necessary to enable detailed numerical simulations of complete propulsion systems are being developed at the NASA Glenn Research Center in cooperation with NASA Glenn s Propulsion program, NASA Ames, industry, academia and other government agencies. Large scale, detailed simulations will be of great value to the nation because they eliminate some of the costly testing required to develop and certify advanced propulsion systems. In addition, time and cost savings will be achieved by enabling design details to be evaluated early in the development process before a commitment is made to a specific design. This year s review meeting describes the current status of the NPSS and the Object Oriented Development Kit with specific emphasis on the progress made over the past year on air breathing propulsion applications for aeronautics and space transportation applications. Major accomplishments include the first 3-D simulation of the primary flow path of a large turbofan engine in less than 15 hours, and the formal release of the NPSS Version 1.5 that includes elements of rocket engine systems and a visual based syntax layer. NPSS and the Development Kit are managed by the Computing and Interdisciplinary Systems Office (CISO) at the NASA Glenn Research Center and financially supported in fiscal year 2002 by the Computing, Networking and Information Systems (CNIS) project managed at NASA Ames, the Glenn Aerospace Propulsion and Power Program and the Advanced Space Transportation Program.

Addressing the computational cost of large EIT solutions.

PubMed

Boyle, Alistair; Borsic, Andrea; Adler, Andy

2012-05-01

Electrical impedance tomography (EIT) is a soft field tomography modality based on the application of electric current to a body and measurement of voltages through electrodes at the boundary. The interior conductivity is reconstructed on a discrete representation of the domain using a finite-element method (FEM) mesh and a parametrization of that domain. The reconstruction requires a sequence of numerically intensive calculations. There is strong interest in reducing the cost of these calculations. An improvement in the compute time for current problems would encourage further exploration of computationally challenging problems such as the incorporation of time series data, wide-spread adoption of three-dimensional simulations and correlation of other modalities such as CT and ultrasound. Multicore processors offer an opportunity to reduce EIT computation times but may require some restructuring of the underlying algorithms to maximize the use of available resources. This work profiles two EIT software packages (EIDORS and NDRM) to experimentally determine where the computational costs arise in EIT as problems scale. Sparse matrix solvers, a key component for the FEM forward problem and sensitivity estimates in the inverse problem, are shown to take a considerable portion of the total compute time in these packages. A sparse matrix solver performance measurement tool, Meagre-Crowd, is developed to interface with a variety of solvers and compare their performance over a range of two- and three-dimensional problems of increasing node density. Results show that distributed sparse matrix solvers that operate on multiple cores are advantageous up to a limit that increases as the node density increases. We recommend a selection procedure to find a solver and hardware arrangement matched to the problem and provide guidance and tools to perform that selection.

Secure data sharing in public cloud

NASA Astrophysics Data System (ADS)

Venkataramana, Kanaparti; Naveen Kumar, R.; Tatekalva, Sandhya; Padmavathamma, M.

2012-04-01

Secure multi-party protocols have been proposed for entities (organizations or individuals) that don't fully trust each other to share sensitive information. Many types of entities need to collect, analyze, and disseminate data rapidly and accurately, without exposing sensitive information to unauthorized or untrusted parties. Solutions based on secure multiparty computation guarantee privacy and correctness, at an extra communication (too costly in communication to be practical) and computation cost. The high overhead motivates us to extend this SMC to cloud environment which provides large computation and communication capacity which makes SMC to be used between multiple clouds (i.e., it may between private or public or hybrid clouds).Cloud may encompass many high capacity servers which acts as a hosts which participate in computation (IaaS and PaaS) for final result, which is controlled by Cloud Trusted Authority (CTA) for secret sharing within the cloud. The communication between two clouds is controlled by High Level Trusted Authority (HLTA) which is one of the hosts in a cloud which provides MgaaS (Management as a Service). Due to high risk for security in clouds, HLTA generates and distributes public keys and private keys by using Carmichael-R-Prime- RSA algorithm for exchange of private data in SMC between itself and clouds. In cloud, CTA creates Group key for Secure communication between the hosts in cloud based on keys sent by HLTA for exchange of Intermediate values and shares for computation of final result. Since this scheme is extended to be used in clouds( due to high availability and scalability to increase computation power) it is possible to implement SMC practically for privacy preserving in data mining at low cost for the clients.

Network Community Detection based on the Physarum-inspired Computational Framework.

PubMed

Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

2016-12-13

Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

Fast-SG: an alignment-free algorithm for hybrid assembly.

PubMed

Di Genova, Alex; Ruz, Gonzalo A; Sagot, Marie-France; Maass, Alejandro

2018-05-01

Long-read sequencing technologies are the ultimate solution for genome repeats, allowing near reference-level reconstructions of large genomes. However, long-read de novo assembly pipelines are computationally intense and require a considerable amount of coverage, thereby hindering their broad application to the assembly of large genomes. Alternatively, hybrid assembly methods that combine short- and long-read sequencing technologies can reduce the time and cost required to produce de novo assemblies of large genomes. Here, we propose a new method, called Fast-SG, that uses a new ultrafast alignment-free algorithm specifically designed for constructing a scaffolding graph using light-weight data structures. Fast-SG can construct the graph from either short or long reads. This allows the reuse of efficient algorithms designed for short-read data and permits the definition of novel modular hybrid assembly pipelines. Using comprehensive standard datasets and benchmarks, we show how Fast-SG outperforms the state-of-the-art short-read aligners when building the scaffoldinggraph and can be used to extract linking information from either raw or error-corrected long reads. We also show how a hybrid assembly approach using Fast-SG with shallow long-read coverage (5X) and moderate computational resources can produce long-range and accurate reconstructions of the genomes of Arabidopsis thaliana (Ler-0) and human (NA12878). Fast-SG opens a door to achieve accurate hybrid long-range reconstructions of large genomes with low effort, high portability, and low cost.

Design of transonic airfoil sections using a similarity theory

NASA Technical Reports Server (NTRS)

Nixon, D.

1978-01-01

A study of the available methods for transonic airfoil and wing design indicates that the most powerful technique is the numerical optimization procedure. However, the computer time for this method is relatively large because of the amount of computation required in the searches during optimization. The optimization method requires that base and calibration solutions be computed to determine a minimum drag direction. The design space is then computationally searched in this direction; it is these searches that dominate the computation time. A recent similarity theory allows certain transonic flows to be calculated rapidly from the base and calibration solutions. In this paper the application of the similarity theory to design problems is examined with the object of at least partially eliminating the costly searches of the design optimization method. An example of an airfoil design is presented.

Biomedical cloud computing with Amazon Web Services.

PubMed

Fusaro, Vincent A; Patil, Prasad; Gafni, Erik; Wall, Dennis P; Tonellato, Peter J

2011-08-01

In this overview to biomedical computing in the cloud, we discussed two primary ways to use the cloud (a single instance or cluster), provided a detailed example using NGS mapping, and highlighted the associated costs. While many users new to the cloud may assume that entry is as straightforward as uploading an application and selecting an instance type and storage options, we illustrated that there is substantial up-front effort required before an application can make full use of the cloud's vast resources. Our intention was to provide a set of best practices and to illustrate how those apply to a typical application pipeline for biomedical informatics, but also general enough for extrapolation to other types of computational problems. Our mapping example was intended to illustrate how to develop a scalable project and not to compare and contrast alignment algorithms for read mapping and genome assembly. Indeed, with a newer aligner such as Bowtie, it is possible to map the entire African genome using one m2.2xlarge instance in 48 hours for a total cost of approximately $48 in computation time. In our example, we were not concerned with data transfer rates, which are heavily influenced by the amount of available bandwidth, connection latency, and network availability. When transferring large amounts of data to the cloud, bandwidth limitations can be a major bottleneck, and in some cases it is more efficient to simply mail a storage device containing the data to AWS (http://aws.amazon.com/importexport/). More information about cloud computing, detailed cost analysis, and security can be found in references.

Remote access laboratories in Australia and Europe

NASA Astrophysics Data System (ADS)

Ku, H.; Ahfock, T.; Yusaf, T.

2011-06-01

Remote access laboratories (RALs) were first developed in 1994 in Australia and Switzerland. The main purposes of developing them are to enable students to do their experiments at their own pace, time and locations and to enable students and teaching staff to get access to facilities beyond their institutions. Currently, most of the experiments carried out through RALs in Australia are heavily biased towards electrical, electronic and computer engineering disciplines. However, the experiments carried out through RALs in Europe had more variety, in addition to the traditional electrical, electronic and computer engineering disciplines, there were experiments in mechanical and mechatronic disciplines. It was found that RALs are now being developed aggressively in Australia and Europe and it can be argued that RALs will develop further and faster in the future with improving Internet technology. The rising costs of real experimental equipment will also speed up their development because by making the equipment remotely accessible, the cost can be shared by more universities or institutions and this will improve their cost-effectiveness. Their development would be particularly rapid in large countries with small populations such as Australia, Canada and Russia, because of the scale of economy. Reusability of software, interoperability in software implementation, computer supported collaborative learning and convergence with learning management systems are the required development of future RALs.

CC2 oscillator strengths within the local framework for calculating excitation energies (LoFEx).

PubMed

Baudin, Pablo; Kjærgaard, Thomas; Kristensen, Kasper

2017-04-14

In a recent work [P. Baudin and K. Kristensen, J. Chem. Phys. 144, 224106 (2016)], we introduced a local framework for calculating excitation energies (LoFEx), based on second-order approximated coupled cluster (CC2) linear-response theory. LoFEx is a black-box method in which a reduced excitation orbital space (XOS) is optimized to provide coupled cluster (CC) excitation energies at a reduced computational cost. In this article, we present an extension of the LoFEx algorithm to the calculation of CC2 oscillator strengths. Two different strategies are suggested, in which the size of the XOS is determined based on the excitation energy or the oscillator strength of the targeted transitions. The two strategies are applied to a set of medium-sized organic molecules in order to assess both the accuracy and the computational cost of the methods. The results show that CC2 excitation energies and oscillator strengths can be calculated at a reduced computational cost, provided that the targeted transitions are local compared to the size of the molecule. To illustrate the potential of LoFEx for large molecules, both strategies have been successfully applied to the lowest transition of the bivalirudin molecule (4255 basis functions) and compared with time-dependent density functional theory.

Bicriteria Network Optimization Problem using Priority-based Genetic Algorithm

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Lin, Lin; Cheng, Runwei

Network optimization is being an increasingly important and fundamental issue in the fields such as engineering, computer science, operations research, transportation, telecommunication, decision support systems, manufacturing, and airline scheduling. In many applications, however, there are several criteria associated with traversing each edge of a network. For example, cost and flow measures are both important in the networks. As a result, there has been recent interest in solving Bicriteria Network Optimization Problem. The Bicriteria Network Optimization Problem is known a NP-hard. The efficient set of paths may be very large, possibly exponential in size. Thus the computational effort required to solve it can increase exponentially with the problem size in the worst case. In this paper, we propose a genetic algorithm (GA) approach used a priority-based chromosome for solving the bicriteria network optimization problem including maximum flow (MXF) model and minimum cost flow (MCF) model. The objective is to find the set of Pareto optimal solutions that give possible maximum flow with minimum cost. This paper also combines Adaptive Weight Approach (AWA) that utilizes some useful information from the current population to readjust weights for obtaining a search pressure toward a positive ideal point. Computer simulations show the several numerical experiments by using some difficult-to-solve network design problems, and show the effectiveness of the proposed method.

Approaches to eliminate waste and reduce cost for recycling glass.

PubMed

Chao, Chien-Wen; Liao, Ching-Jong

2011-12-01

In recent years, the issue of environmental protection has received considerable attention. This paper adds to the literature by investigating a scheduling problem in the manufacturing of a glass recycling factory in Taiwan. The objective is to minimize the sum of the total holding cost and loss cost. We first represent the problem as an integer programming (IP) model, and then develop two heuristics based on the IP model to find near-optimal solutions for the problem. To validate the proposed heuristics, comparisons between optimal solutions from the IP model and solutions from the current method are conducted. The comparisons involve two problem sizes, small and large, where the small problems range from 15 to 45 jobs, and the large problems from 50 to 100 jobs. Finally, a genetic algorithm is applied to evaluate the proposed heuristics. Computational experiments show that the proposed heuristics can find good solutions in a reasonable time for the considered problem. Copyright © 2011 Elsevier Ltd. All rights reserved.

IP-Based Video Modem Extender Requirements

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

Pierson, L G; Boorman, T M; Howe, R E

2003-12-16

Visualization is one of the keys to understanding large complex data sets such as those generated by the large computing resources purchased and developed by the Advanced Simulation and Computing program (aka ASCI). In order to be convenient to researchers, visualization data must be distributed to offices and large complex visualization theaters. Currently, local distribution of the visual data is accomplished by distance limited modems and RGB switches that simply do not scale to hundreds of users across the local, metropolitan, and WAN distances without incurring large costs in fiber plant installation and maintenance. Wide Area application over the DOEmore » Complex is infeasible using these limited distance RGB extenders. On the other hand, Internet Protocols (IP) over Ethernet is a scalable well-proven technology that can distribute large volumes of data over these distances. Visual data has been distributed at lower resolutions over IP in industrial applications. This document describes requirements of the ASCI program in visual signal distribution for the purpose of identifying industrial partners willing to develop products to meet ASCI's needs.« less

The economic burden of occupational non-melanoma skin cancer due to solar radiation.

PubMed

Mofidi, Amirabbas; Tompa, Emile; Spencer, James; Kalcevich, Christina; Peters, Cheryl E; Kim, Joanne; Song, Chaojie; Mortazavi, Seyed Bagher; Demers, Paul A

2018-06-01

Solar ultraviolet (UV) radiation is the second most prevalent carcinogenic exposure in Canada and is similarly important in other countries with large Caucasian populations. The objective of this article was to estimate the economic burden associated with newly diagnosed non-melanoma skin cancers (NMSCs) attributable to occupational solar radiation exposure. Key cost categories considered were direct costs (healthcare costs, out-of-pocket costs (OOPCs), and informal caregiver costs); indirect costs (productivity/output costs and home production costs); and intangible costs (monetary value of the loss of health-related quality of life (HRQoL)). To generate the burden estimates, we used secondary data from multiple sources applied to computational methods developed from an extensive review of the literature. An estimated 2,846 (5.3%) of the 53,696 newly diagnosed cases of basal cell carcinoma (BCC) and 1,710 (9.2%) of the 18,549 newly diagnosed cases of squamous cell carcinoma (SCC) in 2011 in Canada were attributable to occupational solar radiation exposure. The combined total for direct and indirect costs of occupational NMSC cases is $28.9 million ($15.9 million for BCC and $13.0 million for SCC), and for intangible costs is $5.7 million ($0.6 million for BCC and $5.1 million for SCC). On a per-case basis, the total costs are $5,670 for BCC and $10,555 for SCC. The higher per-case cost for SCC is largely a result of a lower survival rate, and hence higher indirect and intangible costs. Our estimates can be used to raise awareness of occupational solar UV exposure as an important causal factor in NMSCs and can highlight the importance of occupational BCC and SCC among other occupational cancers.

Learning Short Binary Codes for Large-scale Image Retrieval.

PubMed

Liu, Li; Yu, Mengyang; Shao, Ling

2017-03-01

Large-scale visual information retrieval has become an active research area in this big data era. Recently, hashing/binary coding algorithms prove to be effective for scalable retrieval applications. Most existing hashing methods require relatively long binary codes (i.e., over hundreds of bits, sometimes even thousands of bits) to achieve reasonable retrieval accuracies. However, for some realistic and unique applications, such as on wearable or mobile devices, only short binary codes can be used for efficient image retrieval due to the limitation of computational resources or bandwidth on these devices. In this paper, we propose a novel unsupervised hashing approach called min-cost ranking (MCR) specifically for learning powerful short binary codes (i.e., usually the code length shorter than 100 b) for scalable image retrieval tasks. By exploring the discriminative ability of each dimension of data, MCR can generate one bit binary code for each dimension and simultaneously rank the discriminative separability of each bit according to the proposed cost function. Only top-ranked bits with minimum cost-values are then selected and grouped together to compose the final salient binary codes. Extensive experimental results on large-scale retrieval demonstrate that MCR can achieve comparative performance as the state-of-the-art hashing algorithms but with significantly shorter codes, leading to much faster large-scale retrieval.

The social computing room: a multi-purpose collaborative visualization environment

NASA Astrophysics Data System (ADS)

Borland, David; Conway, Michael; Coposky, Jason; Ginn, Warren; Idaszak, Ray

2010-01-01

The Social Computing Room (SCR) is a novel collaborative visualization environment for viewing and interacting with large amounts of visual data. The SCR consists of a square room with 12 projectors (3 per wall) used to display a single 360-degree desktop environment that provides a large physical real estate for arranging visual information. The SCR was designed to be cost-effective, collaborative, configurable, widely applicable, and approachable for naive users. Because the SCR displays a single desktop, a wide range of applications is easily supported, making it possible for a variety of disciplines to take advantage of the room. We provide a technical overview of the room and highlight its application to scientific visualization, arts and humanities projects, research group meetings, and virtual worlds, among other uses.

Parallel scalability of Hartree-Fock calculations

NASA Astrophysics Data System (ADS)

Chow, Edmond; Liu, Xing; Smelyanskiy, Mikhail; Hammond, Jeff R.

2015-03-01

Quantum chemistry is increasingly performed using large cluster computers consisting of multiple interconnected nodes. For a fixed molecular problem, the efficiency of a calculation usually decreases as more nodes are used, due to the cost of communication between the nodes. This paper empirically investigates the parallel scalability of Hartree-Fock calculations. The construction of the Fock matrix and the density matrix calculation are analyzed separately. For the former, we use a parallelization of Fock matrix construction based on a static partitioning of work followed by a work stealing phase. For the latter, we use density matrix purification from the linear scaling methods literature, but without using sparsity. When using large numbers of nodes for moderately sized problems, density matrix computations are network-bandwidth bound, making purification methods potentially faster than eigendecomposition methods.

Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures

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

Brust, Frederick W.; Punch, Edward F.; Twombly, Elizabeth Kurth

This report summarizes the final product developed for the US DOE Small Business Innovation Research (SBIR) Phase II grant made to Engineering Mechanics Corporation of Columbus (Emc 2) between April 16, 2014 and August 31, 2016 titled ‘Adoption of High Performance Computational (HPC) Modeling Software for Widespread Use in the Manufacture of Welded Structures’. Many US companies have moved fabrication and production facilities off shore because of cheaper labor costs. A key aspect in bringing these jobs back to the US is the use of technology to render US-made fabrications more cost-efficient overall with higher quality. One significant advantage thatmore » has emerged in the US over the last two decades is the use of virtual design for fabrication of small and large structures in weld fabrication industries. Industries that use virtual design and analysis tools have reduced material part size, developed environmentally-friendly fabrication processes, improved product quality and performance, and reduced manufacturing costs. Indeed, Caterpillar Inc. (CAT), one of the partners in this effort, continues to have a large fabrication presence in the US because of the use of weld fabrication modeling to optimize fabrications by controlling weld residual stresses and distortions and improving fatigue, corrosion, and fracture performance. This report describes Emc 2’s DOE SBIR Phase II final results to extend an existing, state-of-the-art software code, Virtual Fabrication Technology (VFT®), currently used to design and model large welded structures prior to fabrication - to a broader range of products with widespread applications for small and medium-sized enterprises (SMEs). VFT® helps control distortion, can minimize and/or control residual stresses, control welding microstructure, and pre-determine welding parameters such as weld-sequencing, pre-bending, thermal-tensioning, etc. VFT® uses material properties, consumable properties, etc. as inputs. Through VFT®, manufacturing companies can avoid costly design changes after fabrication. This leads to the concept of joint design/fabrication where these important disciplines are intimately linked to minimize fabrication costs. Finally service performance (such as fatigue, corrosion, and fracture/damage) can be improved using this product. Emc 2’s DOE SBIR Phase II effort successfully adapted VFT® to perform efficiently in an HPC environment independent of commercial software on a platform to permit easy and cost effective access to the code. This provides the key for SMEs to access this sophisticated and proven methodology that is quick, accurate, cost effective and available “on-demand” to address weld-simulation and fabrication problems prior to manufacture. In addition, other organizations, such as Government agencies and large companies, may have a need for spot use of such a tool. The open source code, WARP3D, a high performance finite element code used in fracture and damage assessment of structures, was significantly modified so computational weld problems can be solved efficiently on multiple processors and threads with VFT®. The thermal solver for VFT®, based on a series of closed form solution approximations, was extensively enhanced for solution on multiple processors greatly increasing overall speed. In addition, the graphical user interface (GUI) was re-written to permit SMEs access to an HPC environment at the Ohio Super Computer Center (OSC) to integrate these solutions with WARP3D. The GUI is used to define all weld pass descriptions, number of passes, material properties, consumable properties, weld speed, etc. for the structure to be modeled. The GUI was enhanced to make it more user-friendly so that non-experts can perform weld modeling. Finally, an extensive outreach program to market this capability to fabrication companies was performed. This access will permit SMEs to perform weld modeling to improve their competitiveness at a reasonable cost.« less

Automated divertor target design by adjoint shape sensitivity analysis and a one-shot method

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

Dekeyser, W., E-mail: Wouter.Dekeyser@kuleuven.be; Reiter, D.; Baelmans, M.

As magnetic confinement fusion progresses towards the development of first reactor-scale devices, computational tokamak divertor design is a topic of high priority. Presently, edge plasma codes are used in a forward approach, where magnetic field and divertor geometry are manually adjusted to meet design requirements. Due to the complex edge plasma flows and large number of design variables, this method is computationally very demanding. On the other hand, efficient optimization-based design strategies have been developed in computational aerodynamics and fluid mechanics. Such an optimization approach to divertor target shape design is elaborated in the present paper. A general formulation ofmore » the design problems is given, and conditions characterizing the optimal designs are formulated. Using a continuous adjoint framework, design sensitivities can be computed at a cost of only two edge plasma simulations, independent of the number of design variables. Furthermore, by using a one-shot method the entire optimization problem can be solved at an equivalent cost of only a few forward simulations. The methodology is applied to target shape design for uniform power load, in simplified edge plasma geometry.« less

SU-E-T-314: The Application of Cloud Computing in Pencil Beam Scanning Proton Therapy Monte Carlo Simulation

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

Wang, Z; Gao, M

Purpose: Monte Carlo simulation plays an important role for proton Pencil Beam Scanning (PBS) technique. However, MC simulation demands high computing power and is limited to few large proton centers that can afford a computer cluster. We study the feasibility of utilizing cloud computing in the MC simulation of PBS beams. Methods: A GATE/GEANT4 based MC simulation software was installed on a commercial cloud computing virtual machine (Linux 64-bits, Amazon EC2). Single spot Integral Depth Dose (IDD) curves and in-air transverse profiles were used to tune the source parameters to simulate an IBA machine. With the use of StarCluster softwaremore » developed at MIT, a Linux cluster with 2–100 nodes can be conveniently launched in the cloud. A proton PBS plan was then exported to the cloud where the MC simulation was run. Results: The simulated PBS plan has a field size of 10×10cm{sup 2}, 20cm range, 10cm modulation, and contains over 10,000 beam spots. EC2 instance type m1.medium was selected considering the CPU/memory requirement and 40 instances were used to form a Linux cluster. To minimize cost, master node was created with on-demand instance and worker nodes were created with spot-instance. The hourly cost for the 40-node cluster was $0.63 and the projected cost for a 100-node cluster was $1.41. Ten million events were simulated to plot PDD and profile, with each job containing 500k events. The simulation completed within 1 hour and an overall statistical uncertainty of < 2% was achieved. Good agreement between MC simulation and measurement was observed. Conclusion: Cloud computing is a cost-effective and easy to maintain platform to run proton PBS MC simulation. When proton MC packages such as GATE and TOPAS are combined with cloud computing, it will greatly facilitate the pursuing of PBS MC studies, especially for newly established proton centers or individual researchers.« less

Folding Proteins at 500 ns/hour with Work Queue.

PubMed

Abdul-Wahid, Badi'; Yu, Li; Rajan, Dinesh; Feng, Haoyun; Darve, Eric; Thain, Douglas; Izaguirre, Jesús A

2012-10-01

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour.

Folding Proteins at 500 ns/hour with Work Queue

PubMed Central

Abdul-Wahid, Badi’; Yu, Li; Rajan, Dinesh; Feng, Haoyun; Darve, Eric; Thain, Douglas; Izaguirre, Jesús A.

2014-01-01

Molecular modeling is a field that traditionally has large computational costs. Until recently, most simulation techniques relied on long trajectories, which inherently have poor scalability. A new class of methods is proposed that requires only a large number of short calculations, and for which minimal communication between computer nodes is required. We considered one of the more accurate variants called Accelerated Weighted Ensemble Dynamics (AWE) and for which distributed computing can be made efficient. We implemented AWE using the Work Queue framework for task management and applied it to an all atom protein model (Fip35 WW domain). We can run with excellent scalability by simultaneously utilizing heterogeneous resources from multiple computing platforms such as clouds (Amazon EC2, Microsoft Azure), dedicated clusters, grids, on multiple architectures (CPU/GPU, 32/64bit), and in a dynamic environment in which processes are regularly added or removed from the pool. This has allowed us to achieve an aggregate sampling rate of over 500 ns/hour. As a comparison, a single process typically achieves 0.1 ns/hour. PMID:25540799

A Pipeline for Large Data Processing Using Regular Sampling for Unstructured Grids

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

Berres, Anne Sabine; Adhinarayanan, Vignesh; Turton, Terece

2017-05-12

Large simulation data requires a lot of time and computational resources to compute, store, analyze, visualize, and run user studies. Today, the largest cost of a supercomputer is not hardware but maintenance, in particular energy consumption. Our goal is to balance energy consumption and cognitive value of visualizations of resulting data. This requires us to go through the entire processing pipeline, from simulation to user studies. To reduce the amount of resources, data can be sampled or compressed. While this adds more computation time, the computational overhead is negligible compared to the simulation time. We built a processing pipeline atmore » the example of regular sampling. The reasons for this choice are two-fold: using a simple example reduces unnecessary complexity as we know what to expect from the results. Furthermore, it provides a good baseline for future, more elaborate sampling methods. We measured time and energy for each test we did, and we conducted user studies in Amazon Mechanical Turk (AMT) for a range of different results we produced through sampling.« less

Time-Shifted Boundary Conditions Used for Navier-Stokes Aeroelastic Solver

NASA Technical Reports Server (NTRS)

Srivastava, Rakesh

1999-01-01

Under the Advanced Subsonic Technology (AST) Program, an aeroelastic analysis code (TURBO-AE) based on Navier-Stokes equations is currently under development at NASA Lewis Research Center s Machine Dynamics Branch. For a blade row, aeroelastic instability can occur in any of the possible interblade phase angles (IBPA s). Analyzing small IBPA s is very computationally expensive because a large number of blade passages must be simulated. To reduce the computational cost of these analyses, we used time shifted, or phase-lagged, boundary conditions in the TURBO-AE code. These conditions can be used to reduce the computational domain to a single blade passage by requiring the boundary conditions across the passage to be lagged depending on the IBPA being analyzed. The time-shifted boundary conditions currently implemented are based on the direct-store method. This method requires large amounts of data to be stored over a period of the oscillation cycle. On CRAY computers this is not a major problem because solid-state devices can be used for fast input and output to read and write the data onto a disk instead of storing it in core memory.

On the study of control effectiveness and computational efficiency of reduced Saint-Venant model in model predictive control of open channel flow

NASA Astrophysics Data System (ADS)

Xu, M.; van Overloop, P. J.; van de Giesen, N. C.

2011-02-01

Model predictive control (MPC) of open channel flow is becoming an important tool in water management. The complexity of the prediction model has a large influence on the MPC application in terms of control effectiveness and computational efficiency. The Saint-Venant equations, called SV model in this paper, and the Integrator Delay (ID) model are either accurate but computationally costly, or simple but restricted to allowed flow changes. In this paper, a reduced Saint-Venant (RSV) model is developed through a model reduction technique, Proper Orthogonal Decomposition (POD), on the SV equations. The RSV model keeps the main flow dynamics and functions over a large flow range but is easier to implement in MPC. In the test case of a modeled canal reach, the number of states and disturbances in the RSV model is about 45 and 16 times less than the SV model, respectively. The computational time of MPC with the RSV model is significantly reduced, while the controller remains effective. Thus, the RSV model is a promising means to balance the control effectiveness and computational efficiency.

Delivering The Benefits of Chemical-Biological Integration in ...

EPA Pesticide Factsheets

Abstract: Researchers at the EPA’s National Center for Computational Toxicology integrate advances in biology, chemistry, and computer science to examine the toxicity of chemicals and help prioritize chemicals for further research based on potential human health risks. The intention of this research program is to quickly evaluate thousands of chemicals for potential risk but with much reduced cost relative to historical approaches. This work involves computational and data driven approaches including high-throughput screening, modeling, text-mining and the integration of chemistry, exposure and biological data. We have developed a number of databases and applications that are delivering on the vision of developing a deeper understanding of chemicals and their effects on exposure and biological processes that are supporting a large community of scientists in their research efforts. This presentation will provide an overview of our work to bring together diverse large scale data from the chemical and biological domains, our approaches to integrate and disseminate these data, and the delivery of models supporting computational toxicology. This abstract does not reflect U.S. EPA policy. Presentation at ACS TOXI session on Computational Chemistry and Toxicology in Chemical Discovery and Assessement (QSARs).

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU

PubMed Central

Xia, Yong; Zhang, Henggui

2015-01-01

Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations. PMID:26581957

Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.

PubMed

Xia, Yong; Wang, Kuanquan; Zhang, Henggui

2015-01-01

Large-scale 3D virtual heart model simulations are highly demanding in computational resources. This imposes a big challenge to the traditional computation resources based on CPU environment, which already cannot meet the requirement of the whole computation demands or are not easily available due to expensive costs. GPU as a parallel computing environment therefore provides an alternative to solve the large-scale computational problems of whole heart modeling. In this study, using a 3D sheep atrial model as a test bed, we developed a GPU-based simulation algorithm to simulate the conduction of electrical excitation waves in the 3D atria. In the GPU algorithm, a multicellular tissue model was split into two components: one is the single cell model (ordinary differential equation) and the other is the diffusion term of the monodomain model (partial differential equation). Such a decoupling enabled realization of the GPU parallel algorithm. Furthermore, several optimization strategies were proposed based on the features of the virtual heart model, which enabled a 200-fold speedup as compared to a CPU implementation. In conclusion, an optimized GPU algorithm has been developed that provides an economic and powerful platform for 3D whole heart simulations.

Distributed information system (water fact sheet)

USGS Publications Warehouse

Harbaugh, A.W.

1986-01-01

During 1982-85, the Water Resources Division (WRD) of the U.S. Geological Survey (USGS) installed over 70 large minicomputers in offices across the country to support its mission in the science of hydrology. These computers are connected by a communications network that allows information to be shared among computers in each office. The computers and network together are known as the Distributed Information System (DIS). The computers are accessed through the use of more than 1500 terminals and minicomputers. The WRD has three fundamentally different needs for computing: data management; hydrologic analysis; and administration. Data management accounts for 50% of the computational workload of WRD because hydrologic data are collected in all 50 states, Puerto Rico, and the Pacific trust territories. Hydrologic analysis consists of 40% of the computational workload of WRD. Cost accounting, payroll, personnel records, and planning for WRD programs occupies an estimated 10% of the computer workload. The DIS communications network is shown on a map. (Lantz-PTT)

The Hidden Costs of Owning a Microcomputer.

ERIC Educational Resources Information Center

McDole, Thomas L.

Before purchasing computer hardware, individuals must consider the costs associated with the setup and operation of a microcomputer system. Included among the initial costs of purchasing a computer are the costs of the computer, one or more disk drives, a monitor, and a printer as well as the costs of such optional peripheral devices as a plotter…

NGScloud: RNA-seq analysis of non-model species using cloud computing.

PubMed

Mora-Márquez, Fernando; Vázquez-Poletti, José Luis; López de Heredia, Unai

2018-05-03

RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution. unai.lopezdeheredia@upm.es.

Long Range Debye-Hückel Correction for Computation of Grid-based Electrostatic Forces Between Biomacromolecules

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

Mereghetti, Paolo; Martinez, M.; Wade, Rebecca C.

Brownian dynamics (BD) simulations can be used to study very large molecular systems, such as models of the intracellular environment, using atomic-detail structures. Such simulations require strategies to contain the computational costs, especially for the computation of interaction forces and energies. A common approach is to compute interaction forces between macromolecules by precomputing their interaction potentials on three-dimensional discretized grids. For long-range interactions, such as electrostatics, grid-based methods are subject to finite size errors. We describe here the implementation of a Debye-Hückel correction to the grid-based electrostatic potential used in the SDA BD simulation software that was applied to simulatemore » solutions of bovine serum albumin and of hen egg white lysozyme.« less

The Effect of Number of Ability Intervals on the Stability of Item Bias Detection.

ERIC Educational Resources Information Center

Loyd, Brenda

The chi-square procedure has been suggested as a viable index of test bias because it provides the best agreement with the three parameter item characteristic curve without the large sample requirement, computer complexity, and cost. This study examines the effect of using different numbers of ability intervals on the reliability of chi-square…

Multi-level optimization of a beam-like space truss utilizing a continuum model

NASA Technical Reports Server (NTRS)

Yates, K.; Gurdal, Z.; Thangjitham, S.

1992-01-01

A continuous beam model is developed for approximate analysis of a large, slender, beam-like truss. The model is incorporated in a multi-level optimization scheme for the weight minimization of such trusses. This scheme is tested against traditional optimization procedures for savings in computational cost. Results from both optimization methods are presented for comparison.

Visualizing Economic Development with ArcGIS Explorer

ERIC Educational Resources Information Center

Webster, Megan L.; Milson, Andrew J.

2011-01-01

Numerous educators have noted that Geographic Information Systems (GIS) is a powerful tool for social studies teaching and learning. Yet the use of GIS has been hampered by issues such as the cost of the software and the management of large spatial data files. One trend that shows great promise for GIS in education is the move to cloud computing.…

Are You Afraid of Taking an Online Foreign Language Test?

ERIC Educational Resources Information Center

Garcia Laborda, Jesus; Robles, Valencia

2017-01-01

Computer based testing has become a prevailing tendency in education. Each year, a large number of students take online language tests everywhere in the world. In fact, there is a tendency to make these tests more and more used due to their low cost of delivery. However, many students are forced to take them despite their interests, feelings and…

A Comparison between Two Automatic Assessment Approaches for Programming: An Empirical Study on MOOCs

ERIC Educational Resources Information Center

Bey, Anis; Jermann, Patrick; Dillenbourg, Pierre

2018-01-01

Computer-graders have been in regular use in the context of MOOCs (Massive Open Online Courses). The automatic grading of programs presents an opportunity to assess and provide tailored feedback to large classes, while featuring at the same time a number of benefits like: immediate feedback, unlimited submissions, as well as low cost of feedback.…

Large shipyard enlists EMS control capabilities. [Energy management system

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

Not Available

1981-10-01

The energy management plan of the Ingalls Shipbuilding in Pascagoula, Mississippi, featuring computer technology, is described. An integral component of the plan is a plus 300-point energy management system with Phase II expansion envisaging to bring additional points under control Within the first ten months of operation, the system saved more than /89,763 in electricity costs alone.

Jointly optimizing selection of fuel treatments and siting of forest biomass-based energy production facilities for landscape-scale fire hazard reduction.

Treesearch

Peter J. Daugherty; Jeremy S. Fried

2007-01-01

Landscape-scale fuel treatments for forest fire hazard reduction potentially produce large quantities of material suitable for biomass energy production. The analytic framework FIA BioSum addresses this situation by developing detailed data on forest conditions and production under alternative fuel treatment prescriptions, and computes haul costs to alternative sites...

Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

PubMed

Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

2018-02-20

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

Strategy alternatives for homeland air and cruise missile defense.

PubMed

Murphy, Eric M; Payne, Michael D; Vanderwoude, Glenn W

2010-10-01

Air and cruise missile defense of the U.S. homeland is characterized by a requirement to protect a large number of critical assets nonuniformly dispersed over a vast area with relatively few defensive systems. In this article, we explore strategy alternatives to make the best use of existing defense resources and suggest this approach as a means of reducing risk while mitigating the cost of developing and acquiring new systems. We frame the issue as an attacker-defender problem with simultaneous moves. First, we outline and examine the relatively simple problem of defending comparatively few locations with two surveillance systems. Second, we present our analysis and findings for a more realistic scenario that includes a representative list of U.S. critical assets. Third, we investigate sensitivity to defensive strategic choices in the more realistic scenario. As part of this investigation, we describe two complementary computational methods that, under certain circumstances, allow one to reduce large computational problems to a more manageable size. Finally, we demonstrate that strategic choices can be an important supplement to material solutions and can, in some cases, be a more cost-effective alternative. © 2010 Society for Risk Analysis.

Space Technology Mission Directorate: Game Changing Development

NASA Technical Reports Server (NTRS)

Gaddis, Stephen W.

2015-01-01

NASA and the aerospace community have deep roots in manufacturing technology and innovation. Through it's Game Changing Development Program and the Advanced Manufacturing Technology Project NASA develops and matures innovative, low-cost manufacturing processes and products. Launch vehicle propulsion systems are a particular area of interest since they typically comprise a large percentage of the total vehicle cost and development schedule. NASA is currently working to develop and utilize emerging technologies such as additive manufacturing (i.e. 3D printing) and computational materials and processing tools that could dramatically improve affordability, capability, and reduce schedule for rocket propulsion hardware.

Analysis, design, and testing of a low cost, direct force command linear proof mass actuator for structural control

NASA Technical Reports Server (NTRS)

Slater, G. L.; Shelley, Stuart; Jacobson, Mark

1993-01-01

In this paper, the design, analysis, and test of a low cost, linear proof mass actuator for vibration control is presented. The actuator is based on a linear induction coil from a large computer disk drive. Such disk drives are readily available and provide the linear actuator, current feedback amplifier, and power supply for a highly effective, yet inexpensive, experimental laboratory actuator. The device is implemented as a force command input system, and the performance is virtually the same as other, more sophisticated, linear proof mass systems.

Use of optimization to predict the effect of selected parameters on commuter aircraft performance

NASA Technical Reports Server (NTRS)

Wells, V. L.; Shevell, R. S.

1982-01-01

An optimizing computer program determined the turboprop aircraft with lowest direct operating cost for various sets of cruise speed and field length constraints. External variables included wing area, wing aspect ratio and engine sea level static horsepower; tail sizes, climb speed and cruise altitude were varied within the function evaluation program. Direct operating cost was minimized for a 150 n.mi typical mission. Generally, DOC increased with increasing speed and decreasing field length but not by a large amount. Ride roughness, however, increased considerably as speed became higher and field length became shorter.

Identification of cost effective energy conservation measures

NASA Technical Reports Server (NTRS)

Bierenbaum, H. S.; Boggs, W. H.

1978-01-01

In addition to a successful program of readily implemented conservation actions for reducing building energy consumption at Kennedy Space Center, recent detailed analyses have identified further substantial savings for buildings representative of technical facilities designed when energy costs were low. The techniques employed for determination of these energy savings consisted of facility configuration analysis, power and lighting measurements, detailed computer simulations and simulation verifications. Use of these methods resulted in identification of projected energy savings as large as $330,000 a year (approximately two year break-even period) in a single building. Application of these techniques to other commercial buildings is discussed

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.

Conception et analyse d'un systeme d'optimisation de plans de vol pour les avions

NASA Astrophysics Data System (ADS)

Maazoun, Wissem

The main objective of this thesis is to develop an optimization method for the preparation of flight plans for aircrafts. The flight plan minimizes all costs associated with the flight. We determine an optimal path for an airplane from a departure airport to a destination airport. The optimal path minimizes the sum of all costs, i.e. the cost of fuel added to the cost of time (wages, rental of the aircraft, arrival delays, etc.). The optimal trajectory is obtained by considering all possible trajectories on a 3D graph (longitude, latitude and altitude) where the altitude levels are separated by 2,000 feet, and by applying a shortest path algorithm. The main task was to accurately compute fuel consumption on each edge of the graph, making sure that each arc has a minimal cost and is covered in a realistic way from the point of view of control, i.e. in accordance with the rules of navigation. To compute the cost of an arc, we take into account weather conditions (temperature, pressure, wind components, etc.). The optimization of each arc is done via the evaluation of an optimum speed that takes all costs into account. Each arc of the graph typically includes several sub-phases of the flight, e.g. altitude change, speed change, and constant speed and altitude. In the initial climb and the final descent phases, the costs are determined by considering altitude changes at constant CAS (Calibrated Air Speed) or constant Mach number. CAS and Mach number are adjusted to minimize cost. The aerodynamic model used is the one proposed by Eurocontrol, which uses the BADA (Base of Aircraft Data) tables. This model is based on the total energy equation that determines the instantaneous fuel consumption. Calculations on each arc are done by solving a system of differential equations that systematically takes all costs into account. To compute the cost of an arc, we must know the time to go through it, which is generally unknown. To have well-posed boundary conditions, we use the horizontal displacement as the independent variable of the system of differential equations. We consider the velocity components of the wind in a 3D system of coordinates to compute the instantaneous ground speed of the aircraft. To consider the cost of time, we use the cost index. The cost of an arc depends on the aircraft mass at the beginning of this arc, and this mass depends on the path. As we consider all possible paths, the cost of an arc must be computed for each trajectory to which it belongs. For a long-distance flight, the number of arcs to be considered in the graph is large and therefore the cost of an arc is typically computed many times. Our algorithm computes the costs of one million arcs in seconds while having a high accuracy. The determination of the optimal trajectory can therefore be done in a short time. To get the optimal path, the mass of the aircraft at the departure point must also be optimal. It is therefore necessary to know the optimal amount of fuel for the journey. The aircraft mass is known only at the arrival point. This mass is the mass of the aircraft including passengers, cargo and reserve fuel mass. The optimal path is determined by calculating backwards, i.e. from the arrival point to the departure point. For the determination of the optimal trajectory, we use an elliptical grid that has focal points at the departure and arrival points. The use of this grid is essential for the construction of a direct and acyclic graph. We use the Bellman-Ford algorithm on a DAG to determine the shortest path. This algorithm is easy to implement and results in short computation times. Our algorithm computes an optimal trajectory with an optimal cost for each arc. Altitude changes are done optimally with respect to the mass of the aircraft and the cost of time. Our algorithm gives the mass, speed, altitude and total cost at any point of the trajectory as well as the optimal profiles of climb and descent. A prototype has been implemented in C. We made simulations of all types of possible arcs and of several complete trajectories to illustrate the behaviour of the algorithm.

The microcomputer workstation - An alternate hardware architecture for remotely sensed image analysis

NASA Technical Reports Server (NTRS)

Erickson, W. K.; Hofman, L. B.; Donovan, W. E.

1984-01-01

Difficulties regarding the digital image analysis of remotely sensed imagery can arise in connection with the extensive calculations required. In the past, an expensive large to medium mainframe computer system was needed for performing these calculations. For image-processing applications smaller minicomputer-based systems are now used by many organizations. The costs for such systems are still in the range from $100K to $300K. Recently, as a result of new developments, the use of low-cost microcomputers for image processing and display systems appeared to have become feasible. These developments are related to the advent of the 16-bit microprocessor and the concept of the microcomputer workstation. Earlier 8-bit microcomputer-based image processing systems are briefly examined, and a computer workstation architecture is discussed. Attention is given to a microcomputer workstation developed by Stanford University, and the design and implementation of a workstation network.

The forensic holodeck: an immersive display for forensic crime scene reconstructions.

PubMed

Ebert, Lars C; Nguyen, Tuan T; Breitbeck, Robert; Braun, Marcel; Thali, Michael J; Ross, Steffen

2014-12-01

In forensic investigations, crime scene reconstructions are created based on a variety of three-dimensional image modalities. Although the data gathered are three-dimensional, their presentation on computer screens and paper is two-dimensional, which incurs a loss of information. By applying immersive virtual reality (VR) techniques, we propose a system that allows a crime scene to be viewed as if the investigator were present at the scene. We used a low-cost VR headset originally developed for computer gaming in our system. The headset offers a large viewing volume and tracks the user's head orientation in real-time, and an optical tracker is used for positional information. In addition, we created a crime scene reconstruction to demonstrate the system. In this article, we present a low-cost system that allows immersive, three-dimensional and interactive visualization of forensic incident scene reconstructions.

The influence of fuel type to combustion characteristic in diffusion flame drying by computational fluid dynamics simulation

NASA Astrophysics Data System (ADS)

Septiani, Eka Lutfi; Widiyastuti, W.; Machmudah, Siti; Nurtono, Tantular; Winardi, Sugeng

2017-05-01

Diffusion flame spray drying has become promising method in nanoparticles synthesis giving several advantages and low operation cost. In order to scale up the process which needs high experimentation time and cost, Computational Fluid Dynamics (CFD) by Ansys Fluent 15.0 software has been used. Combustion characteristic in diffusion flame reactor may affects particle size distribution. This study aims to observe influence of fuel type to combustion characteristic in the reactor. Large Eddy Simulation (LES) and non-premixed combustion model are selected for the turbulence and combustion model respectively. Methane, propane, and LPG in 0.5 L/min were used as type of fuel. While the oxidizer is air with 200% excess of O2. Simulation result shown that the maximum temperature was obtained from propane-air combustion in 2268 K. However, the stable temperature contour was achieved by methane-air combustion.

Arduino: a low-cost multipurpose lab equipment.

PubMed

D'Ausilio, Alessandro

2012-06-01

Typical experiments in psychological and neurophysiological settings often require the accurate control of multiple input and output signals. These signals are often generated or recorded via computer software and/or external dedicated hardware. Dedicated hardware is usually very expensive and requires additional software to control its behavior. In the present article, I present some accuracy tests on a low-cost and open-source I/O board (Arduino family) that may be useful in many lab environments. One of the strengths of Arduinos is the possibility they afford to load the experimental script on the board's memory and let it run without interfacing with computers or external software, thus granting complete independence, portability, and accuracy. Furthermore, a large community has arisen around the Arduino idea and offers many hardware add-ons and hundreds of free scripts for different projects. Accuracy tests show that Arduino boards may be an inexpensive tool for many psychological and neurophysiological labs.

Distributed computing feasibility in a non-dedicated homogeneous distributed system

NASA Technical Reports Server (NTRS)

Leutenegger, Scott T.; Sun, Xian-He

1993-01-01

The low cost and availability of clusters of workstations have lead researchers to re-explore distributed computing using independent workstations. This approach may provide better cost/performance than tightly coupled multiprocessors. In practice, this approach often utilizes wasted cycles to run parallel jobs. The feasibility of such a non-dedicated parallel processing environment assuming workstation processes have preemptive priority over parallel tasks is addressed. An analytical model is developed to predict parallel job response times. Our model provides insight into how significantly workstation owner interference degrades parallel program performance. A new term task ratio, which relates the parallel task demand to the mean service demand of nonparallel workstation processes, is introduced. It was proposed that task ratio is a useful metric for determining how large the demand of a parallel applications must be in order to make efficient use of a non-dedicated distributed system.

A Markov computer simulation model of the economics of neuromuscular blockade in patients with acute respiratory distress syndrome

PubMed Central

Macario, Alex; Chow, John L; Dexter, Franklin

2006-01-01

Background Management of acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU) is clinically challenging and costly. Neuromuscular blocking agents may facilitate mechanical ventilation and improve oxygenation, but may result in prolonged recovery of neuromuscular function and acute quadriplegic myopathy syndrome (AQMS). The goal of this study was to address a hypothetical question via computer modeling: Would a reduction in intubation time of 6 hours and/or a reduction in the incidence of AQMS from 25% to 21%, provide enough benefit to justify a drug with an additional expenditure of $267 (the difference in acquisition cost between a generic and brand name neuromuscular blocker)? Methods The base case was a 55 year-old man in the ICU with ARDS who receives neuromuscular blockade for 3.5 days. A Markov model was designed with hypothetical patients in 1 of 6 mutually exclusive health states: ICU-intubated, ICU-extubated, hospital ward, long-term care, home, or death, over a period of 6 months. The net monetary benefit was computed. Results Our computer simulation modeling predicted the mean cost for ARDS patients receiving standard care for 6 months to be $62,238 (5% – 95% percentiles $42,259 – $83,766), with an overall 6-month mortality of 39%. Assuming a ceiling ratio of $35,000, even if a drug (that cost $267 more) hypothetically reduced AQMS from 25% to 21% and decreased intubation time by 6 hours, the net monetary benefit would only equal $137. Conclusion ARDS patients receiving a neuromuscular blocker have a high mortality, and unpredictable outcome, which results in large variability in costs per case. If a patient dies, there is no benefit to any drug that reduces ventilation time or AQMS incidence. A prospective, randomized pharmacoeconomic study of neuromuscular blockers in the ICU to asses AQMS or intubation times is impractical because of the highly variable clinical course of patients with ARDS. PMID:16539706

Data Structures for Extreme Scale Computing

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

Kahan, Simon

As computing problems of national importance grow, the government meets the increased demand by funding the development of ever larger systems. The overarching goal of the work supported in part by this grant is to increase efficiency of programming and performing computations on these large computing systems. In past work, we have demonstrated that some of these computations once thought to require expensive hardware designs and/or complex, special-purpose programming may be executed efficiently on low-cost commodity cluster computing systems using a general-purpose “latency-tolerant” programming framework. One important developed application of the ideas underlying this framework is graph database technology supportingmore » social network pattern matching used by US intelligence agencies to more quickly identify potential terrorist threats. This database application has been spun out by the Pacific Northwest National Laboratory, a Department of Energy Laboratory, into a commercial start-up, Trovares Inc. We explore an alternative application of the same underlying ideas to a well-studied challenge arising in engineering: solving unstructured sparse linear equations. Solving these equations is key to predicting the behavior of large electronic circuits before they are fabricated. Predicting that behavior ahead of fabrication means that designs can optimized and errors corrected ahead of the expense of manufacture.« less

Antibiotic pharmacoeconomics: an attempt to find the real cost of hospital antibiotic prescribing.

PubMed Central

Kerr, J. R.; Barr, J. G.; Smyth, E. T.; O'Hare, J.; Bell, P. M.; Callender, M. E.

1993-01-01

Antibiotics account for a large part of all hospital pharmacy budgets, but the actual cost of their prescription is unknown. These costs include intravenous administration, labour, serum antibiotic assay, monitoring of haematological and biochemical indices, disposal of sharps and adverse effects. An in-house method of costing antibiotic therapy is presented, to quantify these hidden expenses. Since not only an awareness, but an accurate quantification, of hidden costs is required, a study of various hospital procedures relating directly to antibiotic therapy was undertaken in an acute medical ward; this involved the identification of particular staff members performing various procedures, consumables used and time taken. The cost of five-day courses of gentamicin, penicillin G, ampicillin, flucloxacillin, cefuroxime, ceftotaxime and erythromycin has been calculated; drug and hidden costs for each are presented graphically for comparison. The breakdown cost for gentamicin is presented to illustrate the method. The costing of adverse effects has not been attempted. We suggest that costings of this sort are used in cost-benefit analysis of antibiotic use. These calculations have been incorporated into a computer spreadsheet and this costing service will be offered to clinical areas of our hospital. PMID:8516976

One laptop per child, local refurbishment or overseas donations? Sustainability assessment of computer supply scenarios for schools in Colombia.

PubMed

Streicher-Porte, Martin; Marthaler, Christian; Böni, Heinz; Schluep, Mathias; Camacho, Angel; Hilty, Lorenz M

2009-08-01

With the intention of bridging the 'digital divide' many programmes have been launched to provide computers for educational institutions, ranging from refurbishing second hand computers to delivering low cost new computers. The fast and economical provision of large quantities of equipment is one of the many challenges faced by such programmes. If an increase is to be achieved in the sustainability of computer supplies for schools, not only must equipment be provided, but also suitable training and maintenance delivered. Furthermore, appropriate recycling has to be ensured, so that end-of-life equipment can be dealt with properly. This study has evaluated the suitability of three computer supply scenarios to schools in Colombia: (i) 'Colombian refurbishment', -refurbishment of computers donated in Colombia, (ii) 'Overseas refurbishment', -import of computers which were donated and refurbished abroad, and (iii) 'XO Laptop', -purchase of low cost computers manufactured in Korea. The methods applied were: Material Flow Assessment, -to assess the quantities-, Life Cycle Assessment, -to assess the environmental impacts, and the application of the Multiple Attribute Utility Theory, -to analyse, evaluate and compare different scenarios. The most sustainable solution proved to be the local refurbishment of second hand computers of Colombian origin to an appropriate technical standard. The environmental impacts of such practices need to be evaluated carefully, as second hand appliances have to be maintained, require spare parts and sometimes use more energy than newer equipment. Providing schools with second hand computers from overseas and through programmes such as 'One Laptop Per Child' has the disadvantage that the potential for social improvements - such as creation of jobs and local industry involvement - is very low.

Concave utility, transaction costs, and risk in measuring discounting of delayed rewards.

PubMed

Kirby, Kris N; Santiesteban, Mariana

2003-01-01

Research has consistently found that the decline in the present values of delayed rewards as delay increases is better fit by hyperbolic than by exponential delay-discounting functions. However, concave utility, transaction costs, and risk each could produce hyperbolic-looking data, even when the underlying discounting function is exponential. In Experiments 1 (N = 45) and 2 (N = 103), participants placed bids indicating their present values of real future monetary rewards in computer-based 2nd-price auctions. Both experiments suggest that utility is not sufficiently concave to account for the superior fit of hyperbolic functions. Experiment 2 provided no evidence that the effects of transaction costs and risk are large enough to account for the superior fit of hyperbolic functions.

DENA: A Configurable Microarchitecture and Design Flow for Biomedical DNA-Based Logic Design.

PubMed

Beiki, Zohre; Jahanian, Ali

2017-10-01

DNA is known as the building block for storing the life codes and transferring the genetic features through the generations. However, it is found that DNA strands can be used for a new type of computation that opens fascinating horizons in computational medicine. Significant contributions are addressed on design of DNA-based logic gates for medical and computational applications but there are serious challenges for designing the medium and large-scale DNA circuits. In this paper, a new microarchitecture and corresponding design flow is proposed to facilitate the design of multistage large-scale DNA logic systems. Feasibility and efficiency of the proposed microarchitecture are evaluated by implementing a full adder and, then, its cascadability is determined by implementing a multistage 8-bit adder. Simulation results show the highlight features of the proposed design style and microarchitecture in terms of the scalability, implementation cost, and signal integrity of the DNA-based logic system compared to the traditional approaches.

GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering

PubMed Central

Suzuki, Shuji; Kakuta, Masanori; Ishida, Takashi; Akiyama, Yutaka

2016-01-01

Sequence homology searches are used in various fields and require large amounts of computation time, especially for metagenomic analysis, owing to the large number of queries and the database size. To accelerate computing analyses, graphics processing units (GPUs) are widely used as a low-cost, high-performance computing platform. Therefore, we mapped the time-consuming steps involved in GHOSTZ, which is a state-of-the-art homology search algorithm for protein sequences, onto a GPU and implemented it as GHOSTZ-GPU. In addition, we optimized memory access for GPU calculations and for communication between the CPU and GPU. As per results of the evaluation test involving metagenomic data, GHOSTZ-GPU with 12 CPU threads and 1 GPU was approximately 3.0- to 4.1-fold faster than GHOSTZ with 12 CPU threads. Moreover, GHOSTZ-GPU with 12 CPU threads and 3 GPUs was approximately 5.8- to 7.7-fold faster than GHOSTZ with 12 CPU threads. PMID:27482905

Large scale Brownian dynamics of confined suspensions of rigid particles

NASA Astrophysics Data System (ADS)

Sprinkle, Brennan; Balboa Usabiaga, Florencio; Patankar, Neelesh A.; Donev, Aleksandar

2017-12-01

We introduce methods for large-scale Brownian Dynamics (BD) simulation of many rigid particles of arbitrary shape suspended in a fluctuating fluid. Our method adds Brownian motion to the rigid multiblob method [F. Balboa Usabiaga et al., Commun. Appl. Math. Comput. Sci. 11(2), 217-296 (2016)] at a cost comparable to the cost of deterministic simulations. We demonstrate that we can efficiently generate deterministic and random displacements for many particles using preconditioned Krylov iterative methods, if kernel methods to efficiently compute the action of the Rotne-Prager-Yamakawa (RPY) mobility matrix and its "square" root are available for the given boundary conditions. These kernel operations can be computed with near linear scaling for periodic domains using the positively split Ewald method. Here we study particles partially confined by gravity above a no-slip bottom wall using a graphical processing unit implementation of the mobility matrix-vector product, combined with a preconditioned Lanczos iteration for generating Brownian displacements. We address a major challenge in large-scale BD simulations, capturing the stochastic drift term that arises because of the configuration-dependent mobility. Unlike the widely used Fixman midpoint scheme, our methods utilize random finite differences and do not require the solution of resistance problems or the computation of the action of the inverse square root of the RPY mobility matrix. We construct two temporal schemes which are viable for large-scale simulations, an Euler-Maruyama traction scheme and a trapezoidal slip scheme, which minimize the number of mobility problems to be solved per time step while capturing the required stochastic drift terms. We validate and compare these schemes numerically by modeling suspensions of boomerang-shaped particles sedimented near a bottom wall. Using the trapezoidal scheme, we investigate the steady-state active motion in dense suspensions of confined microrollers, whose height above the wall is set by a combination of thermal noise and active flows. We find the existence of two populations of active particles, slower ones closer to the bottom and faster ones above them, and demonstrate that our method provides quantitative accuracy even with relatively coarse resolutions of the particle geometry.

Implementation of highly parallel and large scale GW calculations within the OpenAtom software

NASA Astrophysics Data System (ADS)

Ismail-Beigi, Sohrab

The need to describe electronic excitations with better accuracy than provided by band structures produced by Density Functional Theory (DFT) has been a long-term enterprise for the computational condensed matter and materials theory communities. In some cases, appropriate theoretical frameworks have existed for some time but have been difficult to apply widely due to computational cost. For example, the GW approximation incorporates a great deal of important non-local and dynamical electronic interaction effects but has been too computationally expensive for routine use in large materials simulations. OpenAtom is an open source massively parallel ab initiodensity functional software package based on plane waves and pseudopotentials (http://charm.cs.uiuc.edu/OpenAtom/) that takes advantage of the Charm + + parallel framework. At present, it is developed via a three-way collaboration, funded by an NSF SI2-SSI grant (ACI-1339804), between Yale (Ismail-Beigi), IBM T. J. Watson (Glenn Martyna) and the University of Illinois at Urbana Champaign (Laxmikant Kale). We will describe the project and our current approach towards implementing large scale GW calculations with OpenAtom. Potential applications of large scale parallel GW software for problems involving electronic excitations in semiconductor and/or metal oxide systems will be also be pointed out.

Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction

NASA Astrophysics Data System (ADS)

Qiao, Baijie; Zhang, Xingwu; Gao, Jiawei; Liu, Ruonan; Chen, Xuefeng

2017-01-01

Most previous regularization methods for solving the inverse problem of force reconstruction are to minimize the l2-norm of the desired force. However, these traditional regularization methods such as Tikhonov regularization and truncated singular value decomposition, commonly fail to solve the large-scale ill-posed inverse problem in moderate computational cost. In this paper, taking into account the sparse characteristic of impact force, the idea of sparse deconvolution is first introduced to the field of impact force reconstruction and a general sparse deconvolution model of impact force is constructed. Second, a novel impact force reconstruction method based on the primal-dual interior point method (PDIPM) is proposed to solve such a large-scale sparse deconvolution model, where minimizing the l2-norm is replaced by minimizing the l1-norm. Meanwhile, the preconditioned conjugate gradient algorithm is used to compute the search direction of PDIPM with high computational efficiency. Finally, two experiments including the small-scale or medium-scale single impact force reconstruction and the relatively large-scale consecutive impact force reconstruction are conducted on a composite wind turbine blade and a shell structure to illustrate the advantage of PDIPM. Compared with Tikhonov regularization, PDIPM is more efficient, accurate and robust whether in the single impact force reconstruction or in the consecutive impact force reconstruction.

VCF-Explorer: filtering and analysing whole genome VCF files.

PubMed

Akgün, Mete; Demirci, Hüseyin

2017-11-01

The decreasing cost in high-throughput technologies led to a number of sequencing projects consisting of thousands of whole genomes. The paradigm shift from exome to whole genome brings a significant increase in the size of output files. Most of the existing tools which are developed to analyse exome files are not adequate for larger VCF files produced by whole genome studies. In this work we present VCF-Explorer, a variant analysis software capable of handling large files. Memory efficiency and avoiding computationally costly pre-processing step enable to carry out the analysis to be performed with ordinary computers. VCF-Explorer provides an easy to use environment where users can define various types of queries based on variant and sample genotype level annotations. VCF-Explorer can be run in different environments and computational platforms ranging from a standard laptop to a high performance server. VCF-Explorer is freely available at: http://vcfexplorer.sourceforge.net/. mete.akgun@tubitak.gov.tr. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Clustering molecular dynamics trajectories for optimizing docking experiments.

PubMed

De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

Reconstruction of large cranial defects with poly-methyl-methacrylate (PMMA) using a rapid prototyping model and a new technique for intraoperative implant modeling.

PubMed

Unterhofer, Claudia; Wipplinger, Christoph; Verius, Michael; Recheis, Wolfgang; Thomé, Claudius; Ortler, Martin

Reconstruction of large cranial defects after craniectomy can be accomplished by free-hand poly-methyl-methacrylate (PMMA) or industrially manufactured implants. The free-hand technique often does not achieve satisfactory cosmetic results but is inexpensive. In an attempt to combine the accuracy of specifically manufactured implants with low cost of PMMA. Forty-six consecutive patients with large skull defects after trauma or infection were retrospectively analyzed. The defects were reconstructed using computer-aided design/computer-aided manufacturing (CAD/CAM) techniques. The computer file was imported into a rapid prototyping (RP) machine to produce an acrylonitrile-butadiene-styrene model (ABS) of the patient's bony head. The gas-sterilized model was used as a template for the intraoperative modeling of the PMMA cranioplasty. Thus, not the PMMA implant was generated by CAD/CAM technique but the model of the patients head to easily form a well-fitting implant. Cosmetic outcome was rated on a six-tiered scale by the patients after a minimum follow-up of three months. The mean size of the defect was 74.36cm 2 . The implants fitted well in all patients. Seven patients had a postoperative complication and underwent reoperation. Mean follow-up period was 41 months (range 2-91 months). Results were excellent in 42, good in three and not satisfactory in one patient. Costs per implant were approximately 550 Euros. PMMA implants fabricated in-house by direct molding using a bio-model of the patients bony head are easily produced, fit properly and are inexpensive compared to cranial implants fabricated with other RP or milling techniques. Copyright © 2017 Polish Neurological Society. Published by Elsevier Urban & Partner Sp. z o.o. All rights reserved.

Building devices from colloidal quantum dots.

PubMed

Kagan, Cherie R; Lifshitz, Efrat; Sargent, Edward H; Talapin, Dmitri V

2016-08-26

The continued growth of mobile and interactive computing requires devices manufactured with low-cost processes, compatible with large-area and flexible form factors, and with additional functionality. We review recent advances in the design of electronic and optoelectronic devices that use colloidal semiconductor quantum dots (QDs). The properties of materials assembled of QDs may be tailored not only by the atomic composition but also by the size, shape, and surface functionalization of the individual QDs and by the communication among these QDs. The chemical and physical properties of QD surfaces and the interfaces in QD devices are of particular importance, and these enable the solution-based fabrication of low-cost, large-area, flexible, and functional devices. We discuss challenges that must be addressed in the move to solution-processed functional optoelectronic nanomaterials. Copyright © 2016, American Association for the Advancement of Science.

CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community

PubMed Central

Smith, Andy; Southgate, Joel; Poplawski, Radoslaw; Bull, Matthew J.; Richardson, Emily; Ismail, Matthew; Thompson, Simon Elwood-; Kitchen, Christine; Guest, Martyn; Bakke, Marius

2016-01-01

The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data. PMID:28785418

CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community.

PubMed

Connor, Thomas R; Loman, Nicholas J; Thompson, Simon; Smith, Andy; Southgate, Joel; Poplawski, Radoslaw; Bull, Matthew J; Richardson, Emily; Ismail, Matthew; Thompson, Simon Elwood-; Kitchen, Christine; Guest, Martyn; Bakke, Marius; Sheppard, Samuel K; Pallen, Mark J

2016-09-01

The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data.

Computer numeric control generation of toric surfaces

NASA Astrophysics Data System (ADS)

Bradley, Norman D.; Ball, Gary A.; Keller, John R.

1994-05-01

Until recently, the manufacture of toric ophthalmic lenses relied largely upon expensive, manual techniques for generation and polishing. Recent gains in computer numeric control (CNC) technology and tooling enable lens designers to employ single- point diamond, fly-cutting methods in the production of torics. Fly-cutting methods continue to improve, significantly expanding lens design possibilities while lowering production costs. Advantages of CNC fly cutting include precise control of surface geometry, rapid production with high throughput, and high-quality lens surface finishes requiring minimal polishing. As accessibility and affordability increase within the ophthalmic market, torics promise to dramatically expand lens design choices available to consumers.

Cost Considerations in Nonlinear Finite-Element Computing

NASA Technical Reports Server (NTRS)

Utku, S.; Melosh, R. J.; Islam, M.; Salama, M.

1985-01-01

Conference paper discusses computational requirements for finiteelement analysis using quasi-linear approach to nonlinear problems. Paper evaluates computational efficiency of different computer architecturtural types in terms of relative cost and computing time.

GPU-accelerated computing for Lagrangian coherent structures of multi-body gravitational regimes

NASA Astrophysics Data System (ADS)

Lin, Mingpei; Xu, Ming; Fu, Xiaoyu

2017-04-01

Based on a well-established theoretical foundation, Lagrangian Coherent Structures (LCSs) have elicited widespread research on the intrinsic structures of dynamical systems in many fields, including the field of astrodynamics. Although the application of LCSs in dynamical problems seems straightforward theoretically, its associated computational cost is prohibitive. We propose a block decomposition algorithm developed on Compute Unified Device Architecture (CUDA) platform for the computation of the LCSs of multi-body gravitational regimes. In order to take advantage of GPU's outstanding computing properties, such as Shared Memory, Constant Memory, and Zero-Copy, the algorithm utilizes a block decomposition strategy to facilitate computation of finite-time Lyapunov exponent (FTLE) fields of arbitrary size and timespan. Simulation results demonstrate that this GPU-based algorithm can satisfy double-precision accuracy requirements and greatly decrease the time needed to calculate final results, increasing speed by approximately 13 times. Additionally, this algorithm can be generalized to various large-scale computing problems, such as particle filters, constellation design, and Monte-Carlo simulation.

Assessing Tax Form Distribution Costs: A Proposed Method for Computing the Dollar Value of Tax Form Distribution in a Public Library.

ERIC Educational Resources Information Center

Casey, James B.

1998-01-01

Explains how a public library can compute the actual cost of distributing tax forms to the public by listing all direct and indirect costs and demonstrating the formulae and necessary computations. Supplies directions for calculating costs involved for all levels of staff as well as associated public relations efforts, space, and utility costs.…

Network placement optimization for large-scale distributed system

NASA Astrophysics Data System (ADS)

Ren, Yu; Liu, Fangfang; Fu, Yunxia; Zhou, Zheng

2018-01-01

The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment.

PubMed

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Abdulhamid, Shafi'i Muhammad; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.

Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment

PubMed Central

Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Usman, Mohammed Joda

2017-01-01

Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing. PMID:28467505

Clocks in Feynman's computer and Kitaev's local Hamiltonian: Bias, gaps, idling, and pulse tuning

NASA Astrophysics Data System (ADS)

Caha, Libor; Landau, Zeph; Nagaj, Daniel

2018-06-01

We present a collection of results about the clock in Feynman's computer construction and Kitaev's local Hamiltonian problem. First, by analyzing the spectra of quantum walks on a line with varying end-point terms, we find a better lower bound on the gap of the Feynman Hamiltonian, which translates into a less strict promise gap requirement for the quantum-Merlin-Arthur-complete local Hamiltonian problem. We also translate this result into the language of adiabatic quantum computation. Second, introducing an idling clock construction with a large state space but fast Cesaro mixing, we provide a way for achieving an arbitrarily high success probability of computation with Feynman's computer with only a logarithmic increase in the number of clock qubits. Finally, we tune and thus improve the costs (locality and gap scaling) of implementing a (pulse) clock with a single excitation.

GPU-based High-Performance Computing for Radiation Therapy

PubMed Central

Jia, Xun; Ziegenhein, Peter; Jiang, Steve B.

2014-01-01

Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. Graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past a few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of studies have been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this article, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented. PMID:24486639

The NASA computer aided design and test system

NASA Technical Reports Server (NTRS)

Gould, J. M.; Juergensen, K.

1973-01-01

A family of computer programs facilitating the design, layout, evaluation, and testing of digital electronic circuitry is described. CADAT (computer aided design and test system) is intended for use by NASA and its contractors and is aimed predominantly at providing cost effective microelectronic subsystems based on custom designed metal oxide semiconductor (MOS) large scale integrated circuits (LSIC's). CADAT software can be easily adopted by installations with a wide variety of computer hardware configurations. Its structure permits ease of update to more powerful component programs and to newly emerging LSIC technologies. The components of the CADAT system are described stressing the interaction of programs rather than detail of coding or algorithms. The CADAT system provides computer aids to derive and document the design intent, includes powerful automatic layout software, permits detailed geometry checks and performance simulation based on mask data, and furnishes test pattern sequences for hardware testing.

Efficient experimental design for uncertainty reduction in gene regulatory networks.

PubMed

Dehghannasiri, Roozbeh; Yoon, Byung-Jun; Dougherty, Edward R

2015-01-01

An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/.

Efficient experimental design for uncertainty reduction in gene regulatory networks

PubMed Central

2015-01-01

Background An accurate understanding of interactions among genes plays a major role in developing therapeutic intervention methods. Gene regulatory networks often contain a significant amount of uncertainty. The process of prioritizing biological experiments to reduce the uncertainty of gene regulatory networks is called experimental design. Under such a strategy, the experiments with high priority are suggested to be conducted first. Results The authors have already proposed an optimal experimental design method based upon the objective for modeling gene regulatory networks, such as deriving therapeutic interventions. The experimental design method utilizes the concept of mean objective cost of uncertainty (MOCU). MOCU quantifies the expected increase of cost resulting from uncertainty. The optimal experiment to be conducted first is the one which leads to the minimum expected remaining MOCU subsequent to the experiment. In the process, one must find the optimal intervention for every gene regulatory network compatible with the prior knowledge, which can be prohibitively expensive when the size of the network is large. In this paper, we propose a computationally efficient experimental design method. This method incorporates a network reduction scheme by introducing a novel cost function that takes into account the disruption in the ranking of potential experiments. We then estimate the approximate expected remaining MOCU at a lower computational cost using the reduced networks. Conclusions Simulation results based on synthetic and real gene regulatory networks show that the proposed approximate method has close performance to that of the optimal method but at lower computational cost. The proposed approximate method also outperforms the random selection policy significantly. A MATLAB software implementing the proposed experimental design method is available at http://gsp.tamu.edu/Publications/supplementary/roozbeh15a/. PMID:26423515

Software Solution Saves Dollars

ERIC Educational Resources Information Center

Trotter, Andrew

2004-01-01

This article discusses computer software that can give classrooms and computer labs the capabilities of costly PC's at a small fraction of the cost. A growing number of cost-conscious school districts are finding budget relief in low-cost computer software known as "open source" that can do everything from manage school Web sites to equip…

The Evolutionary Origins of Hierarchy

PubMed Central

Huizinga, Joost; Clune, Jeff

2016-01-01

Hierarchical organization—the recursive composition of sub-modules—is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force–the cost of connections–promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics. PMID:27280881

The Evolutionary Origins of Hierarchy.

PubMed

Mengistu, Henok; Huizinga, Joost; Mouret, Jean-Baptiste; Clune, Jeff

2016-06-01

Hierarchical organization-the recursive composition of sub-modules-is ubiquitous in biological networks, including neural, metabolic, ecological, and genetic regulatory networks, and in human-made systems, such as large organizations and the Internet. To date, most research on hierarchy in networks has been limited to quantifying this property. However, an open, important question in evolutionary biology is why hierarchical organization evolves in the first place. It has recently been shown that modularity evolves because of the presence of a cost for network connections. Here we investigate whether such connection costs also tend to cause a hierarchical organization of such modules. In computational simulations, we find that networks without a connection cost do not evolve to be hierarchical, even when the task has a hierarchical structure. However, with a connection cost, networks evolve to be both modular and hierarchical, and these networks exhibit higher overall performance and evolvability (i.e. faster adaptation to new environments). Additional analyses confirm that hierarchy independently improves adaptability after controlling for modularity. Overall, our results suggest that the same force-the cost of connections-promotes the evolution of both hierarchy and modularity, and that these properties are important drivers of network performance and adaptability. In addition to shedding light on the emergence of hierarchy across the many domains in which it appears, these findings will also accelerate future research into evolving more complex, intelligent computational brains in the fields of artificial intelligence and robotics.

A cost-utility analysis of the use of preoperative computed tomographic angiography in abdomen-based perforator flap breast reconstruction.

PubMed

Offodile, Anaeze C; Chatterjee, Abhishek; Vallejo, Sergio; Fisher, Carla S; Tchou, Julia C; Guo, Lifei

2015-04-01

Computed tomographic angiography is a diagnostic tool increasingly used for preoperative vascular mapping in abdomen-based perforator flap breast reconstruction. This study compared the use of computed tomographic angiography and the conventional practice of Doppler ultrasonography only in postmastectomy reconstruction using a cost-utility model. Following a comprehensive literature review, a decision analytic model was created using the three most clinically relevant health outcomes in free autologous breast reconstruction with computed tomographic angiography versus Doppler ultrasonography only. Cost and utility estimates for each health outcome were used to derive the quality-adjusted life-years and incremental cost-utility ratio. One-way sensitivity analysis was performed to scrutinize the robustness of the authors' results. Six studies and 782 patients were identified. Cost-utility analysis revealed a baseline cost savings of $3179, a gain in quality-adjusted life-years of 0.25. This yielded an incremental cost-utility ratio of -$12,716, implying a dominant choice favoring preoperative computed tomographic angiography. Sensitivity analysis revealed that computed tomographic angiography was costlier when the operative time difference between the two techniques was less than 21.3 minutes. However, the clinical advantage of computed tomographic angiography over Doppler ultrasonography only showed that computed tomographic angiography would still remain the cost-effective option even if it offered no additional operating time advantage. The authors' results show that computed tomographic angiography is a cost-effective technology for identifying lower abdominal perforators for autologous breast reconstruction. Although the perfect study would be a randomized controlled trial of the two approaches with true cost accrual, the authors' results represent the best available evidence.

Estimating the economic opportunity cost of water use with river basin simulators in a computationally efficient way

NASA Astrophysics Data System (ADS)

Rougé, Charles; Harou, Julien J.; Pulido-Velazquez, Manuel; Matrosov, Evgenii S.

2017-04-01

The marginal opportunity cost of water refers to benefits forgone by not allocating an additional unit of water to its most economically productive use at a specific location in a river basin at a specific moment in time. Estimating the opportunity cost of water is an important contribution to water management as it can be used for better water allocation or better system operation, and can suggest where future water infrastructure could be most beneficial. Opportunity costs can be estimated using 'shadow values' provided by hydro-economic optimization models. Yet, such models' use of optimization means the models had difficulty accurately representing the impact of operating rules and regulatory and institutional mechanisms on actual water allocation. In this work we use more widely available river basin simulation models to estimate opportunity costs. This has been done before by adding in the model a small quantity of water at the place and time where the opportunity cost should be computed, then running a simulation and comparing the difference in system benefits. The added system benefits per unit of water added to the system then provide an approximation of the opportunity cost. This approximation can then be used to design efficient pricing policies that provide incentives for users to reduce their water consumption. Yet, this method requires one simulation run per node and per time step, which is demanding computationally for large-scale systems and short time steps (e.g., a day or a week). Besides, opportunity cost estimates are supposed to reflect the most productive use of an additional unit of water, yet the simulation rules do not necessarily use water that way. In this work, we propose an alternative approach, which computes the opportunity cost through a double backward induction, first recursively from outlet to headwaters within the river network at each time step, then recursively backwards in time. Both backward inductions only require linear operations, and the resulting algorithm tracks the maximal benefit that can be obtained by having an additional unit of water at any node in the network and at any date in time. Results 1) can be obtained from the results of a rule-based simulation using a single post-processing run, and 2) are exactly the (gross) benefit forgone by not allocating an additional unit of water to its most productive use. The proposed method is applied to London's water resource system to track the value of storage in the city's water supply reservoirs on the Thames River throughout a weekly 85-year simulation. Results, obtained in 0.4 seconds on a single processor, reflect the environmental cost of water shortage. This fast computation allows visualizing the seasonal variations of the opportunity cost depending on reservoir levels, demonstrating the potential of this approach for exploring water values and its variations using simulation models with multiple runs (e.g. of stochastically generated plausible future river inflows).

High-efficiency wavefunction updates for large scale Quantum Monte Carlo

NASA Astrophysics Data System (ADS)

Kent, Paul; McDaniel, Tyler; Li, Ying Wai; D'Azevedo, Ed

Within ab intio Quantum Monte Carlo (QMC) simulations, the leading numerical cost for large systems is the computation of the values of the Slater determinants in the trial wavefunctions. The evaluation of each Monte Carlo move requires finding the determinant of a dense matrix, which is traditionally iteratively evaluated using a rank-1 Sherman-Morrison updating scheme to avoid repeated explicit calculation of the inverse. For calculations with thousands of electrons, this operation dominates the execution profile. We propose a novel rank- k delayed update scheme. This strategy enables probability evaluation for multiple successive Monte Carlo moves, with application of accepted moves to the matrices delayed until after a predetermined number of moves, k. Accepted events grouped in this manner are then applied to the matrices en bloc with enhanced arithmetic intensity and computational efficiency. This procedure does not change the underlying Monte Carlo sampling or the sampling efficiency. For large systems and algorithms such as diffusion Monte Carlo where the acceptance ratio is high, order of magnitude speedups can be obtained on both multi-core CPU and on GPUs, making this algorithm highly advantageous for current petascale and future exascale computations.

An Examination of Parameters Affecting Large Eddy Simulations of Flow Past a Square Cylinder

NASA Technical Reports Server (NTRS)

Mankbadi, M. R.; Georgiadis, N. J.

2014-01-01

Separated flow over a bluff body is analyzed via large eddy simulations. The turbulent flow around a square cylinder features a variety of complex flow phenomena such as highly unsteady vortical structures, reverse flow in the near wall region, and wake turbulence. The formation of spanwise vortices is often times artificially suppressed in computations by either insufficient depth or a coarse spanwise resolution. As the resolution is refined and the domain extended, the artificial turbulent energy exchange between spanwise and streamwise turbulence is eliminated within the wake region. A parametric study is performed highlighting the effects of spanwise vortices where the spanwise computational domain's resolution and depth are varied. For Re=22,000, the mean and turbulent statistics computed from the numerical large eddy simulations (NLES) are in good agreement with experimental data. Von-Karman shedding is observed in the wake of the cylinder. Mesh independence is illustrated by comparing a mesh resolution of 2 million to 16 million. Sensitivities to time stepping were minimized and sampling frequency sensitivities were nonpresent. While increasing the spanwise depth and resolution can be costly, this practice was found to be necessary to eliminating the artificial turbulent energy exchange.

Using Swarming Agents for Scalable Security in Large Network Environments

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

Crouse, Michael; White, Jacob L.; Fulp, Errin W.

2011-09-23

The difficulty of securing computer infrastructures increases as they grow in size and complexity. Network-based security solutions such as IDS and firewalls cannot scale because of exponentially increasing computational costs inherent in detecting the rapidly growing number of threat signatures. Hostbased solutions like virus scanners and IDS suffer similar issues, and these are compounded when enterprises try to monitor these in a centralized manner. Swarm-based autonomous agent systems like digital ants and artificial immune systems can provide a scalable security solution for large network environments. The digital ants approach offers a biologically inspired design where each ant in the virtualmore » colony can detect atoms of evidence that may help identify a possible threat. By assembling the atomic evidences from different ant types the colony may detect the threat. This decentralized approach can require, on average, fewer computational resources than traditional centralized solutions; however there are limits to its scalability. This paper describes how dividing a large infrastructure into smaller managed enclaves allows the digital ant framework to effectively operate in larger environments. Experimental results will show that using smaller enclaves allows for more consistent distribution of agents and results in faster response times.« less

Distributed solar radiation fast dynamic measurement for PV cells

NASA Astrophysics Data System (ADS)

Wan, Xuefen; Yang, Yi; Cui, Jian; Du, Xingjing; Zheng, Tao; Sardar, Muhammad Sohail

2017-10-01

To study the operating characteristics about PV cells, attention must be given to the dynamic behavior of the solar radiation. The dynamic behaviors of annual, monthly, daily and hourly averages of solar radiation have been studied in detail. But faster dynamic behaviors of solar radiation need more researches. The solar radiation random fluctuations in minute-long or second-long range, which lead to alternating radiation and cool down/warm up PV cell frequently, decrease conversion efficiency. Fast dynamic processes of solar radiation are mainly relevant to stochastic moving of clouds. Even in clear sky condition, the solar irradiations show a certain degree of fast variation. To evaluate operating characteristics of PV cells under fast dynamic irradiation, a solar radiation measuring array (SRMA) based on large active area photodiode, LoRa spread spectrum communication and nanoWatt MCU is proposed. This cross photodiodes structure tracks fast stochastic moving of clouds. To compensate response time of pyranometer and reduce system cost, the terminal nodes with low-cost fast-responded large active area photodiode are placed besides positions of tested PV cells. A central node, consists with pyranometer, large active area photodiode, wind detector and host computer, is placed in the center of the central topologies coordinate to scale temporal envelope of solar irradiation and get calibration information between pyranometer and large active area photodiodes. In our SRMA system, the terminal nodes are designed based on Microchip's nanoWatt XLP PIC16F1947. FDS-100 is adopted for large active area photodiode in terminal nodes and host computer. The output current and voltage of each PV cell are monitored by I/V measurement. AS62-T27/SX1278 LoRa communication modules are used for communicating between terminal nodes and host computer. Because the LoRa LPWAN (Low Power Wide Area Network) specification provides seamless interoperability among Smart Things without the need of complex local installations, configuring of our SRMA system is very easy. Lora also provides SRMA a means to overcome the short communication distance and weather signal propagation decline such as in ZigBee and WiFi. The host computer in SRMA system uses the low power single-board PC EMB-3870 which was produced by NORCO. Wind direction sensor SM5386B and wind-force sensor SM5387B are installed to host computer through RS-485 bus for wind reference data collection. And Davis 6450 solar radiation sensor, which is a precision instrument that detects radiation at wavelengths of 300 to 1100 nanometers, allow host computer to follow real-time solar radiation. A LoRa polling scheme is adopt for the communication between host computer and terminal nodes in SRMA. An experimental SRMA has been established. This system was tested in Ganyu, Jiangshu province from May to August, 2016. In the test, the distances between the nodes and the host computer were between 100m and 1900m. At work, SRMA system showed higher reliability. Terminal nodes could follow the instructions from host computer and collect solar radiation data of distributed PV cells effectively. And the host computer managed the SRAM and achieves reference parameters well. Communications between the host computer and terminal nodes were almost unaffected by the weather. In conclusion, the testing results show that SRMA could be a capable method for fast dynamic measuring about solar radiation and related PV cell operating characteristics.

'Big data', Hadoop and cloud computing in genomics.

PubMed

O'Driscoll, Aisling; Daugelaite, Jurate; Sleator, Roy D

2013-10-01

Since the completion of the Human Genome project at the turn of the Century, there has been an unprecedented proliferation of genomic sequence data. A consequence of this is that the medical discoveries of the future will largely depend on our ability to process and analyse large genomic data sets, which continue to expand as the cost of sequencing decreases. Herein, we provide an overview of cloud computing and big data technologies, and discuss how such expertise can be used to deal with biology's big data sets. In particular, big data technologies such as the Apache Hadoop project, which provides distributed and parallelised data processing and analysis of petabyte (PB) scale data sets will be discussed, together with an overview of the current usage of Hadoop within the bioinformatics community. Copyright © 2013 Elsevier Inc. All rights reserved.

An effective and secure key-management scheme for hierarchical access control in E-medicine system.

PubMed

Odelu, Vanga; Das, Ashok Kumar; Goswami, Adrijit

2013-04-01

Recently several hierarchical access control schemes are proposed in the literature to provide security of e-medicine systems. However, most of them are either insecure against 'man-in-the-middle attack' or they require high storage and computational overheads. Wu and Chen proposed a key management method to solve dynamic access control problems in a user hierarchy based on hybrid cryptosystem. Though their scheme improves computational efficiency over Nikooghadam et al.'s approach, it suffers from large storage space for public parameters in public domain and computational inefficiency due to costly elliptic curve point multiplication. Recently, Nikooghadam and Zakerolhosseini showed that Wu-Chen's scheme is vulnerable to man-in-the-middle attack. In order to remedy this security weakness in Wu-Chen's scheme, they proposed a secure scheme which is again based on ECC (elliptic curve cryptography) and efficient one-way hash function. However, their scheme incurs huge computational cost for providing verification of public information in the public domain as their scheme uses ECC digital signature which is costly when compared to symmetric-key cryptosystem. In this paper, we propose an effective access control scheme in user hierarchy which is only based on symmetric-key cryptosystem and efficient one-way hash function. We show that our scheme reduces significantly the storage space for both public and private domains, and computational complexity when compared to Wu-Chen's scheme, Nikooghadam-Zakerolhosseini's scheme, and other related schemes. Through the informal and formal security analysis, we further show that our scheme is secure against different attacks and also man-in-the-middle attack. Moreover, dynamic access control problems in our scheme are also solved efficiently compared to other related schemes, making our scheme is much suitable for practical applications of e-medicine systems.

Overview of the LINCS architecture

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

Fletcher, J.G.; Watson, R.W.

1982-01-13

Computing at the Lawrence Livermore National Laboratory (LLNL) has evolved over the past 15 years with a computer network based resource sharing environment. The increasing use of low cost and high performance micro, mini and midi computers and commercially available local networking systems will accelerate this trend. Further, even the large scale computer systems, on which much of the LLNL scientific computing depends, are evolving into multiprocessor systems. It is our belief that the most cost effective use of this environment will depend on the development of application systems structured into cooperating concurrent program modules (processes) distributed appropriately over differentmore » nodes of the environment. A node is defined as one or more processors with a local (shared) high speed memory. Given the latter view, the environment can be characterized as consisting of: multiple nodes communicating over noisy channels with arbitrary delays and throughput, heterogenous base resources and information encodings, no single administration controlling all resources, distributed system state, and no uniform time base. The system design problem is - how to turn the heterogeneous base hardware/firmware/software resources of this environment into a coherent set of resources that facilitate development of cost effective, reliable, and human engineered applications. We believe the answer lies in developing a layered, communication oriented distributed system architecture; layered and modular to support ease of understanding, reconfiguration, extensibility, and hiding of implementation or nonessential local details; communication oriented because that is a central feature of the environment. The Livermore Interactive Network Communication System (LINCS) is a hierarchical architecture designed to meet the above needs. While having characteristics in common with other architectures, it differs in several respects.« less

Computer-assisted Behavioral Therapy and Contingency Management for Cannabis Use Disorder

PubMed Central

Budney, Alan J.; Stanger, Catherine; Tilford, J. Mick; Scherer, Emily; Brown, Pamela C.; Li, Zhongze; Li, Zhigang; Walker, Denise

2015-01-01

Computer-assisted behavioral treatments hold promise for enhancing access to and reducing costs of treatments for substance use disorders. This study assessed the efficacy of a computer-assisted version of an efficacious, multicomponent treatment for cannabis use disorders (CUD), i.e., motivational enhancement therapy, cognitive-behavioral therapy, and abstinence-based contingency-management (MET/CBT/CM). An initial cost comparison was also performed. Seventy-five adult participants, 59% African Americans, seeking treatment for CUD received either, MET only (BRIEF), therapist-delivered MET/CBT/CM (THERAPIST), or computer-delivered MET/CBT/CM (COMPUTER). During treatment, the THERAPIST and COMPUTER conditions engendered longer durations of continuous cannabis abstinence than BRIEF (p < .05), but did not differ from each other. Abstinence rates and reduction in days of use over time were maintained in COMPUTER at least as well as in THERAPIST. COMPUTER averaged approximately $130 (p < .05) less per case than THERAPIST in therapist costs, which offset most of the costs of CM. Results add to promising findings that illustrate potential for computer-assisted delivery methods to enhance access to evidence-based care, reduce costs, and possibly improve outcomes. The observed maintenance effects and the cost findings require replication in larger clinical trials. PMID:25938629

Spacelab experiment computer study. Volume 1: Executive summary (presentation)

NASA Technical Reports Server (NTRS)

Lewis, J. L.; Hodges, B. C.; Christy, J. O.

1976-01-01

A quantitative cost for various Spacelab flight hardware configurations is provided along with varied software development options. A cost analysis of Spacelab computer hardware and software is presented. The cost study is discussed based on utilization of a central experiment computer with optional auxillary equipment. Groundrules and assumptions used in deriving the costing methods for all options in the Spacelab experiment study are presented. The groundrules and assumptions, are analysed and the options along with their cost considerations, are discussed. It is concluded that Spacelab program cost for software development and maintenance is independent of experimental hardware and software options, that distributed standard computer concept simplifies software integration without a significant increase in cost, and that decisions on flight computer hardware configurations should not be made until payload selection for a given mission and a detailed analysis of the mission requirements are completed.

Intelligent redundant actuation system requirements and preliminary system design

NASA Technical Reports Server (NTRS)

Defeo, P.; Geiger, L. J.; Harris, J.

1985-01-01

Several redundant actuation system configurations were designed and demonstrated to satisfy the stringent operational requirements of advanced flight control systems. However, this has been accomplished largely through brute force hardware redundancy, resulting in significantly increased computational requirements on the flight control computers which perform the failure analysis and reconfiguration management. Modern technology now provides powerful, low-cost microprocessors which are effective in performing failure isolation and configuration management at the local actuator level. One such concept, called an Intelligent Redundant Actuation System (IRAS), significantly reduces the flight control computer requirements and performs the local tasks more comprehensively than previously feasible. The requirements and preliminary design of an experimental laboratory system capable of demonstrating the concept and sufficiently flexible to explore a variety of configurations are discussed.

Cloud Computing Boosts Business Intelligence of Telecommunication Industry

NASA Astrophysics Data System (ADS)

Xu, Meng; Gao, Dan; Deng, Chao; Luo, Zhiguo; Sun, Shaoling

Business Intelligence becomes an attracting topic in today's data intensive applications, especially in telecommunication industry. Meanwhile, Cloud Computing providing IT supporting Infrastructure with excellent scalability, large scale storage, and high performance becomes an effective way to implement parallel data processing and data mining algorithms. BC-PDM (Big Cloud based Parallel Data Miner) is a new MapReduce based parallel data mining platform developed by CMRI (China Mobile Research Institute) to fit the urgent requirements of business intelligence in telecommunication industry. In this paper, the architecture, functionality and performance of BC-PDM are presented, together with the experimental evaluation and case studies of its applications. The evaluation result demonstrates both the usability and the cost-effectiveness of Cloud Computing based Business Intelligence system in applications of telecommunication industry.

Natural laminar flow airfoil analysis and trade studies

NASA Technical Reports Server (NTRS)

1979-01-01

An analysis of an airfoil for a large commercial transport cruising at Mach 0.8 and the use of advanced computer techniques to perform the analysis are described. Incorporation of the airfoil into a natural laminar flow transport configuration is addressed and a comparison of fuel requirements and operating costs between the natural laminar flow transport and an equivalent turbulent flow transport is addressed.

Strategic Implications of Cloud Computing for Modeling and Simulation (Briefing)

DTIC Science & Technology

2016-04-01

of Promises with Cloud • Cost efficiency • Unlimited storage • Backup and recovery • Automatic software integration • Easy access to information...activities that wrap the actual exercise itself (e.g., travel for exercise support, data collection, integration , etc.). Cloud -based simulation would...requiring quick delivery rather than fewer large messages requiring high bandwidth. Cloud environments tend to be better at providing high-bandwidth

Genetic algorithm approaches for conceptual design of spacecraft systems including multi-objective optimization and design under uncertainty

NASA Astrophysics Data System (ADS)

Hassan, Rania A.

In the design of complex large-scale spacecraft systems that involve a large number of components and subsystems, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-architecting process. Currently, spacecraft design is heavily based on the heritage of the industry. Old spacecraft designs are modified to adapt to new mission requirements, and feasible solutions---rather than optimal ones---are often all that is achieved. During the conceptual phase of the design, the choices available to designers are predominantly discrete variables describing major subsystems' technology options and redundancy levels. The complexity of spacecraft configurations makes the number of the system design variables that need to be traded off in an optimization process prohibitive when manual techniques are used. Such a discrete problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. This research presents a systems engineering framework that places design requirements at the core of the design activities and transforms the design paradigm for spacecraft systems to a top-down approach rather than the current bottom-up approach. To facilitate decision-making in the early phases of the design process, the population-based search nature of the Genetic Algorithm is exploited to provide computationally inexpensive---compared to the state-of-the-practice---tools for both multi-objective design optimization and design optimization under uncertainty. In terms of computational cost, those tools are nearly on the same order of magnitude as that of standard single-objective deterministic Genetic Algorithm. The use of a multi-objective design approach provides system designers with a clear tradeoff optimization surface that allows them to understand the effect of their decisions on all the design objectives under consideration simultaneously. Incorporating uncertainties avoids large safety margins and unnecessary high redundancy levels. The focus on low computational cost for the optimization tools stems from the objective that improving the design of complex systems should not be achieved at the expense of a costly design methodology.

Utility and prevalence of imaging for underlying cancer in unprovoked pulmonary embolism.

PubMed

Homewood, R; Medford, A R

2015-01-01

Current guidelines state that patients over 40 years of age with a first unprovoked pulmonary embolism should be offered limited screening for possible cancer and considered for intensive screening (abdomino-pelvic computed tomography and mammography), despite no evidence for the latter. The aim of this study was to evaluate the clinical utility and cost of intensive screening in routine clinical practice. Methods All patients diagnosed with a first unprovoked pulmonary embolism between January 2014 and June 2014 in a single large UK teaching hospital were included. The information management department searched for patients with an International Classification of Diseases 10 discharge diagnosis of pulmonary embolism and limited to 'acute pulmonary embolism with/without cor pulmonale'. Only patients with unprovoked pulmonary embolism were included. Patients with chronic medical conditions predisposing to pulmonary embolism were excluded. NHS costs were obtained from the Trust Finance Department. These costs were used to generate the costs of limited versus intensive screening, and then scaled up using adult population census information and assuming the same incidence of idiopathic pulmonary embolism to estimate the annual NHS cost of intensive screening. Results Ninety-two patients were diagnosed with pulmonary embolism, and 25 met the inclusion criteria. Clinical examination was often incomplete (84%). Limited screening was often missed (urinalysis 100%, serum calcium 64%). Intensive screening was performed in the majority of cases (68%, all abdomino-pelvic computed tomography with no cancer detected) with an £88 excess cost per patient. Conclusion Intensive screening in first unprovoked pulmonary embolism has a low yield, is costly and should not replace thorough clinical examination and basic screening.

The HEPCloud Facility: elastic computing for High Energy Physics – The NOvA Use Case

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

Fuess, S.; Garzoglio, G.; Holzman, B.

The need for computing in the HEP community follows cycles of peaks and valleys mainly driven by conference dates, accelerator shutdown, holiday schedules, and other factors. Because of this, the classical method of provisioning these resources at providing facilities has drawbacks such as potential overprovisioning. As the appetite for computing increases, however, so does the need to maximize cost efficiency by developing a model for dynamically provisioning resources only when needed. To address this issue, the HEPCloud project was launched by the Fermilab Scientific Computing Division in June 2015. Its goal is to develop a facility that provides a commonmore » interface to a variety of resources, including local clusters, grids, high performance computers, and community and commercial Clouds. Initially targeted experiments include CMS and NOvA, as well as other Fermilab stakeholders. In its first phase, the project has demonstrated the use of the “elastic” provisioning model offered by commercial clouds, such as Amazon Web Services. In this model, resources are rented and provisioned automatically over the Internet upon request. In January 2016, the project demonstrated the ability to increase the total amount of global CMS resources by 58,000 cores from 150,000 cores - a 25 percent increase - in preparation for the Recontres de Moriond. In March 2016, the NOvA experiment has also demonstrated resource burst capabilities with an additional 7,300 cores, achieving a scale almost four times as large as the local allocated resources and utilizing the local AWS s3 storage to optimize data handling operations and costs. NOvA was using the same familiar services used for local computations, such as data handling and job submission, in preparation for the Neutrino 2016 conference. In both cases, the cost was contained by the use of the Amazon Spot Instance Market and the Decision Engine, a HEPCloud component that aims at minimizing cost and job interruption. This paper describes the Fermilab HEPCloud Facility and the challenges overcome for the CMS and NOvA communities.« less

MIDAS, prototype Multivariate Interactive Digital Analysis System for large area earth resources surveys. Volume 1: System description

NASA Technical Reports Server (NTRS)

Christenson, D.; Gordon, M.; Kistler, R.; Kriegler, F.; Lampert, S.; Marshall, R.; Mclaughlin, R.

1977-01-01

A third-generation, fast, low cost, multispectral recognition system (MIDAS) able to keep pace with the large quantity and high rates of data acquisition from large regions with present and projected sensots is described. The program can process a complete ERTS frame in forty seconds and provide a color map of sixteen constituent categories in a few minutes. A principle objective of the MIDAS program is to provide a system well interfaced with the human operator and thus to obtain large overall reductions in turn-around time and significant gains in throughput. The hardware and software generated in the overall program is described. The system contains a midi-computer to control the various high speed processing elements in the data path, a preprocessor to condition data, and a classifier which implements an all digital prototype multivariate Gaussian maximum likelihood or a Bayesian decision algorithm. Sufficient software was developed to perform signature extraction, control the preprocessor, compute classifier coefficients, control the classifier operation, operate the color display and printer, and diagnose operation.

DEM Based Modeling: Grid or TIN? The Answer Depends

NASA Astrophysics Data System (ADS)

Ogden, F. L.; Moreno, H. A.

2015-12-01

The availability of petascale supercomputing power has enabled process-based hydrological simulations on large watersheds and two-way coupling with mesoscale atmospheric models. Of course with increasing watershed scale come corresponding increases in watershed complexity, including wide ranging water management infrastructure and objectives, and ever increasing demands for forcing data. Simulations of large watersheds using grid-based models apply a fixed resolution over the entire watershed. In large watersheds, this means an enormous number of grids, or coarsening of the grid resolution to reduce memory requirements. One alternative to grid-based methods is the triangular irregular network (TIN) approach. TINs provide the flexibility of variable resolution, which allows optimization of computational resources by providing high resolution where necessary and low resolution elsewhere. TINs also increase required effort in model setup, parameter estimation, and coupling with forcing data which are often gridded. This presentation discusses the costs and benefits of the use of TINs compared to grid-based methods, in the context of large watershed simulations within the traditional gridded WRF-HYDRO framework and the new TIN-based ADHydro high performance computing watershed simulator.

Megamap: flexible representation of a large space embedded with nonspatial information by a hippocampal attractor network

PubMed Central

Zhang, Kechen

2016-01-01

The problem of how the hippocampus encodes both spatial and nonspatial information at the cellular network level remains largely unresolved. Spatial memory is widely modeled through the theoretical framework of attractor networks, but standard computational models can only represent spaces that are much smaller than the natural habitat of an animal. We propose that hippocampal networks are built on a basic unit called a “megamap,” or a cognitive attractor map in which place cells are flexibly recombined to represent a large space. Its inherent flexibility gives the megamap a huge representational capacity and enables the hippocampus to simultaneously represent multiple learned memories and naturally carry nonspatial information at no additional cost. On the other hand, the megamap is dynamically stable, because the underlying network of place cells robustly encodes any location in a large environment given a weak or incomplete input signal from the upstream entorhinal cortex. Our results suggest a general computational strategy by which a hippocampal network enjoys the stability of attractor dynamics without sacrificing the flexibility needed to represent a complex, changing world. PMID:27193320

Design and implementation of a UNIX based distributed computing system

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

Love, J.S.; Michael, M.W.

1994-12-31

We have designed, implemented, and are running a corporate-wide distributed processing batch queue on a large number of networked workstations using the UNIX{reg_sign} operating system. Atlas Wireline researchers and scientists have used the system for over a year. The large increase in available computer power has greatly reduced the time required for nuclear and electromagnetic tool modeling. Use of remote distributed computing has simultaneously reduced computation costs and increased usable computer time. The system integrates equipment from different manufacturers, using various CPU architectures, distinct operating system revisions, and even multiple processors per machine. Various differences between the machines have tomore » be accounted for in the master scheduler. These differences include shells, command sets, swap spaces, memory sizes, CPU sizes, and OS revision levels. Remote processing across a network must be performed in a manner that is seamless from the users` perspective. The system currently uses IBM RISC System/6000{reg_sign}, SPARCstation{sup TM}, HP9000s700, HP9000s800, and DEC Alpha AXP{sup TM} machines. Each CPU in the network has its own speed rating, allowed working hours, and workload parameters. The system if designed so that all of the computers in the network can be optimally scheduled without adversely impacting the primary users of the machines. The increase in the total usable computational capacity by means of distributed batch computing can change corporate computing strategy. The integration of disparate computer platforms eliminates the need to buy one type of computer for computations, another for graphics, and yet another for day-to-day operations. It might be possible, for example, to meet all research and engineering computing needs with existing networked computers.« less

Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.

PubMed

Penas, David R; González, Patricia; Egea, Jose A; Doallo, Ramón; Banga, Julio R

2017-01-21

The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.

Improved Quasi-Newton method via PSB update for solving systems of nonlinear equations

NASA Astrophysics Data System (ADS)

Mamat, Mustafa; Dauda, M. K.; Waziri, M. Y.; Ahmad, Fadhilah; Mohamad, Fatma Susilawati

2016-10-01

The Newton method has some shortcomings which includes computation of the Jacobian matrix which may be difficult or even impossible to compute and solving the Newton system in every iteration. Also, the common setback with some quasi-Newton methods is that they need to compute and store an n × n matrix at each iteration, this is computationally costly for large scale problems. To overcome such drawbacks, an improved Method for solving systems of nonlinear equations via PSB (Powell-Symmetric-Broyden) update is proposed. In the proposed method, the approximate Jacobian inverse Hk of PSB is updated and its efficiency has improved thereby require low memory storage, hence the main aim of this paper. The preliminary numerical results show that the proposed method is practically efficient when applied on some benchmark problems.

Hyperswitch communication network

NASA Technical Reports Server (NTRS)

Peterson, J.; Pniel, M.; Upchurch, E.

1991-01-01

The Hyperswitch Communication Network (HCN) is a large scale parallel computer prototype being developed at JPL. Commercial versions of the HCN computer are planned. The HCN computer being designed is a message passing multiple instruction multiple data (MIMD) computer, and offers many advantages in price-performance ratio, reliability and availability, and manufacturing over traditional uniprocessors and bus based multiprocessors. The design of the HCN operating system is a uniquely flexible environment that combines both parallel processing and distributed processing. This programming paradigm can achieve a balance among the following competing factors: performance in processing and communications, user friendliness, and fault tolerance. The prototype is being designed to accommodate a maximum of 64 state of the art microprocessors. The HCN is classified as a distributed supercomputer. The HCN system is described, and the performance/cost analysis and other competing factors within the system design are reviewed.

Observations on computational methodologies for use in large-scale, gradient-based, multidisciplinary design incorporating advanced CFD codes

NASA Technical Reports Server (NTRS)

Newman, P. A.; Hou, G. J.-W.; Jones, H. E.; Taylor, A. C., III; Korivi, V. M.

1992-01-01

How a combination of various computational methodologies could reduce the enormous computational costs envisioned in using advanced CFD codes in gradient based optimized multidisciplinary design (MdD) procedures is briefly outlined. Implications of these MdD requirements upon advanced CFD codes are somewhat different than those imposed by a single discipline design. A means for satisfying these MdD requirements for gradient information is presented which appear to permit: (1) some leeway in the CFD solution algorithms which can be used; (2) an extension to 3-D problems; and (3) straightforward use of other computational methodologies. Many of these observations have previously been discussed as possibilities for doing parts of the problem more efficiently; the contribution here is observing how they fit together in a mutually beneficial way.

Using an Adjoint Approach to Eliminate Mesh Sensitivities in Computational Design

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.; Park, Michael A.

2006-01-01

An algorithm for efficiently incorporating the effects of mesh sensitivities in a computational design framework is introduced. The method is based on an adjoint approach and eliminates the need for explicit linearizations of the mesh movement scheme with respect to the geometric parameterization variables, an expense that has hindered practical large-scale design optimization using discrete adjoint methods. The effects of the mesh sensitivities can be accounted for through the solution of an adjoint problem equivalent in cost to a single mesh movement computation, followed by an explicit matrix-vector product scaling with the number of design variables and the resolution of the parameterized surface grid. The accuracy of the implementation is established and dramatic computational savings obtained using the new approach are demonstrated using several test cases. Sample design optimizations are also shown.

Using an Adjoint Approach to Eliminate Mesh Sensitivities in Computational Design

NASA Technical Reports Server (NTRS)

Nielsen, Eric J.; Park, Michael A.

2005-01-01

An algorithm for efficiently incorporating the effects of mesh sensitivities in a computational design framework is introduced. The method is based on an adjoint approach and eliminates the need for explicit linearizations of the mesh movement scheme with respect to the geometric parameterization variables, an expense that has hindered practical large-scale design optimization using discrete adjoint methods. The effects of the mesh sensitivities can be accounted for through the solution of an adjoint problem equivalent in cost to a single mesh movement computation, followed by an explicit matrix-vector product scaling with the number of design variables and the resolution of the parameterized surface grid. The accuracy of the implementation is established and dramatic computational savings obtained using the new approach are demonstrated using several test cases. Sample design optimizations are also shown.

ACCURATE CHEMICAL MASTER EQUATION SOLUTION USING MULTI-FINITE BUFFERS

PubMed Central

Cao, Youfang; Terebus, Anna; Liang, Jie

2016-01-01

The discrete chemical master equation (dCME) provides a fundamental framework for studying stochasticity in mesoscopic networks. Because of the multi-scale nature of many networks where reaction rates have large disparity, directly solving dCMEs is intractable due to the exploding size of the state space. It is important to truncate the state space effectively with quantified errors, so accurate solutions can be computed. It is also important to know if all major probabilistic peaks have been computed. Here we introduce the Accurate CME (ACME) algorithm for obtaining direct solutions to dCMEs. With multi-finite buffers for reducing the state space by O(n!), exact steady-state and time-evolving network probability landscapes can be computed. We further describe a theoretical framework of aggregating microstates into a smaller number of macrostates by decomposing a network into independent aggregated birth and death processes, and give an a priori method for rapidly determining steady-state truncation errors. The maximal sizes of the finite buffers for a given error tolerance can also be pre-computed without costly trial solutions of dCMEs. We show exactly computed probability landscapes of three multi-scale networks, namely, a 6-node toggle switch, 11-node phage-lambda epigenetic circuit, and 16-node MAPK cascade network, the latter two with no known solutions. We also show how probabilities of rare events can be computed from first-passage times, another class of unsolved problems challenging for simulation-based techniques due to large separations in time scales. Overall, the ACME method enables accurate and efficient solutions of the dCME for a large class of networks. PMID:27761104

cuTauLeaping: A GPU-Powered Tau-Leaping Stochastic Simulator for Massive Parallel Analyses of Biological Systems

PubMed Central

Besozzi, Daniela; Pescini, Dario; Mauri, Giancarlo

2014-01-01

Tau-leaping is a stochastic simulation algorithm that efficiently reconstructs the temporal evolution of biological systems, modeled according to the stochastic formulation of chemical kinetics. The analysis of dynamical properties of these systems in physiological and perturbed conditions usually requires the execution of a large number of simulations, leading to high computational costs. Since each simulation can be executed independently from the others, a massive parallelization of tau-leaping can bring to relevant reductions of the overall running time. The emerging field of General Purpose Graphic Processing Units (GPGPU) provides power-efficient high-performance computing at a relatively low cost. In this work we introduce cuTauLeaping, a stochastic simulator of biological systems that makes use of GPGPU computing to execute multiple parallel tau-leaping simulations, by fully exploiting the Nvidia's Fermi GPU architecture. We show how a considerable computational speedup is achieved on GPU by partitioning the execution of tau-leaping into multiple separated phases, and we describe how to avoid some implementation pitfalls related to the scarcity of memory resources on the GPU streaming multiprocessors. Our results show that cuTauLeaping largely outperforms the CPU-based tau-leaping implementation when the number of parallel simulations increases, with a break-even directly depending on the size of the biological system and on the complexity of its emergent dynamics. In particular, cuTauLeaping is exploited to investigate the probability distribution of bistable states in the Schlögl model, and to carry out a bidimensional parameter sweep analysis to study the oscillatory regimes in the Ras/cAMP/PKA pathway in S. cerevisiae. PMID:24663957

The System of Inventory Forecasting in PT. XYZ by using the Method of Holt Winter Multiplicative

NASA Astrophysics Data System (ADS)

Shaleh, W.; Rasim; Wahyudin

2018-01-01

Problems at PT. XYZ currently only rely on manual bookkeeping, then the cost of production will swell and all investments invested to be less to predict sales and inventory of goods. If the inventory prediction of goods is to large, then the cost of production will swell and all investments invested to be less efficient. Vice versa, if the inventory prediction is too small it will impact on consumers, so that consumers are forced to wait for the desired product. Therefore, in this era of globalization, the development of computer technology has become a very important part in every business plan. Almost of all companies, both large and small, use computer technology. By utilizing computer technology, people can make time in solving complex business problems. Computer technology for companies has become an indispensable activity to provide enhancements to the business services they manage but systems and technologies are not limited to the distribution model and data processing but the existing system must be able to analyze the possibilities of future company capabilities. Therefore, the company must be able to forecast conditions and circumstances, either from inventory of goods, force, or profits to be obtained. To forecast it, the data of total sales from December 2014 to December 2016 will be calculated by using the method of Holt Winters, which is the method of time series prediction (Multiplicative Seasonal Method) it is seasonal data that has increased and decreased, also has 4 equations i.e. Single Smoothing, Trending Smoothing, Seasonal Smoothing and Forecasting. From the results of research conducted, error value in the form of MAPE is below 1%, so it can be concluded that forecasting with the method of Holt Winter Multiplicative.

24 CFR 908.108 - Cost.

Code of Federal Regulations, 2010 CFR

2010-04-01

... computer hardware or software, or both, the cost of contracting for those services, or the cost of... operating budget. At the HA's option, the cost of the computer software may include service contracts to...

Better than $l/Mflops sustained: a scalable PC-based parallel computer for lattice QCD

NASA Astrophysics Data System (ADS)

Fodor, Zoltán; Katz, Sándor D.; Papp, Gábor

2003-05-01

We study the feasibility of a PC-based parallel computer for medium to large scale lattice QCD simulations. The Eötvös Univ., Inst. Theor. Phys. cluster consists of 137 Intel P4-1.7GHz nodes with 512 MB RDRAM. The 32-bit, single precision sustained performance for dynamical QCD without communication is 1510 Mflops/node with Wilson and 970 Mflops/node with staggered fermions. This gives a total performance of 208 Gflops for Wilson and 133 Gflops for staggered QCD, respectively (for 64-bit applications the performance is approximately halved). The novel feature of our system is its communication architecture. In order to have a scalable, cost-effective machine we use Gigabit Ethernet cards for nearest-neighbor communications in a two-dimensional mesh. This type of communication is cost effective (only 30% of the hardware costs is spent on the communication). According to our benchmark measurements this type of communication results in around 40% communication time fraction for lattices upto 48 3·96 in full QCD simulations. The price/sustained-performance ratio for full QCD is better than l/Mflops for Wilson (and around 1.5/Mflops for staggered) quarks for practically any lattice size, which can fit in our parallel computer. The communication software is freely available upon request for non-profit organizations.

Distributed Kalman filtering compared to Fourier domain preconditioned conjugate gradient for laser guide star tomography on extremely large telescopes.

PubMed

Gilles, Luc; Massioni, Paolo; Kulcsár, Caroline; Raynaud, Henri-François; Ellerbroek, Brent

2013-05-01

This paper discusses the performance and cost of two computationally efficient Fourier-based tomographic wavefront reconstruction algorithms for wide-field laser guide star (LGS) adaptive optics (AO). The first algorithm is the iterative Fourier domain preconditioned conjugate gradient (FDPCG) algorithm developed by Yang et al. [Appl. Opt.45, 5281 (2006)], combined with pseudo-open-loop control (POLC). FDPCG's computational cost is proportional to N log(N), where N denotes the dimensionality of the tomography problem. The second algorithm is the distributed Kalman filter (DKF) developed by Massioni et al. [J. Opt. Soc. Am. A28, 2298 (2011)], which is a noniterative spatially invariant controller. When implemented in the Fourier domain, DKF's cost is also proportional to N log(N). Both algorithms are capable of estimating spatial frequency components of the residual phase beyond the wavefront sensor (WFS) cutoff frequency thanks to regularization, thereby reducing WFS spatial aliasing at the expense of more computations. We present performance and cost analyses for the LGS multiconjugate AO system under design for the Thirty Meter Telescope, as well as DKF's sensitivity to uncertainties in wind profile prior information. We found that, provided the wind profile is known to better than 10% wind speed accuracy and 20 deg wind direction accuracy, DKF, despite its spatial invariance assumptions, delivers a significantly reduced wavefront error compared to the static FDPCG minimum variance estimator combined with POLC. Due to its nonsequential nature and high degree of parallelism, DKF is particularly well suited for real-time implementation on inexpensive off-the-shelf graphics processing units.

Numerical Propulsion System Simulation: An Overview

NASA Technical Reports Server (NTRS)

Lytle, John K.

2000-01-01

The cost of implementing new technology in aerospace propulsion systems is becoming prohibitively expensive and time consuming. One of the main contributors to the high cost and lengthy time is the need to perform many large-scale hardware tests and the inability to integrate all appropriate subsystems early in the design process. The NASA Glenn Research Center is developing the technologies required to enable simulations of full aerospace propulsion systems in sufficient detail to resolve critical design issues early in the design process before hardware is built. This concept, called the Numerical Propulsion System Simulation (NPSS), is focused on the integration of multiple disciplines such as aerodynamics, structures and heat transfer with computing and communication technologies to capture complex physical processes in a timely and cost-effective manner. The vision for NPSS, as illustrated, is to be a "numerical test cell" that enables full engine simulation overnight on cost-effective computing platforms. There are several key elements within NPSS that are required to achieve this capability: 1) clear data interfaces through the development and/or use of data exchange standards, 2) modular and flexible program construction through the use of object-oriented programming, 3) integrated multiple fidelity analysis (zooming) techniques that capture the appropriate physics at the appropriate fidelity for the engine systems, 4) multidisciplinary coupling techniques and finally 5) high performance parallel and distributed computing. The current state of development in these five area focuses on air breathing gas turbine engines and is reported in this paper. However, many of the technologies are generic and can be readily applied to rocket based systems and combined cycles currently being considered for low-cost access-to-space applications. Recent accomplishments include: (1) the development of an industry-standard engine cycle analysis program and plug 'n play architecture, called NPSS Version 1, (2) A full engine simulation that combines a 3D low-pressure subsystem with a 0D high pressure core simulation. This demonstrates the ability to integrate analyses at different levels of detail and to aerodynamically couple components, the fan/booster and low-pressure turbine, through a 3D computational fluid dynamics simulation. (3) Simulation of all of the turbomachinery in a modern turbofan engine on parallel computing platform for rapid and cost-effective execution. This capability can also be used to generate full compressor map, requiring both design and off-design simulation. (4) Three levels of coupling characterize the multidisciplinary analysis under NPSS: loosely coupled, process coupled and tightly coupled. The loosely coupled and process coupled approaches require a common geometry definition to link CAD to analysis tools. The tightly coupled approach is currently validating the use of arbitrary Lagrangian/Eulerian formulation for rotating turbomachinery. The validation includes both centrifugal and axial compression systems. The results of the validation will be reported in the paper. (5) The demonstration of significant computing cost/performance reduction for turbine engine applications using PC clusters. The NPSS Project is supported under the NASA High Performance Computing and Communications Program.

Computer Vision for High-Throughput Quantitative Phenotyping: A Case Study of Grapevine Downy Mildew Sporulation and Leaf Trichomes.

PubMed

Divilov, Konstantin; Wiesner-Hanks, Tyr; Barba, Paola; Cadle-Davidson, Lance; Reisch, Bruce I

2017-12-01

Quantitative phenotyping of downy mildew sporulation is frequently used in plant breeding and genetic studies, as well as in studies focused on pathogen biology such as chemical efficacy trials. In these scenarios, phenotyping a large number of genotypes or treatments can be advantageous but is often limited by time and cost. We present a novel computational pipeline dedicated to estimating the percent area of downy mildew sporulation from images of inoculated grapevine leaf discs in a manner that is time and cost efficient. The pipeline was tested on images from leaf disc assay experiments involving two F 1 grapevine families, one that had glabrous leaves (Vitis rupestris B38 × 'Horizon' [RH]) and another that had leaf trichomes (Horizon × V. cinerea B9 [HC]). Correlations between computer vision and manual visual ratings reached 0.89 in the RH family and 0.43 in the HC family. Additionally, we were able to use the computer vision system prior to sporulation to measure the percent leaf trichome area. We estimate that an experienced rater scoring sporulation would spend at least 90% less time using the computer vision system compared with the manual visual method. This will allow more treatments to be phenotyped in order to better understand the genetic architecture of downy mildew resistance and of leaf trichome density. We anticipate that this computer vision system will find applications in other pathosystems or traits where responses can be imaged with sufficient contrast from the background.

AN OVERVIEW OF REDUCED ORDER MODELING TECHNIQUES FOR SAFETY APPLICATIONS

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

Mandelli, D.; Alfonsi, A.; Talbot, P.

2016-10-01

The RISMC project is developing new advanced simulation-based tools to perform Computational Risk Analysis (CRA) for the existing fleet of U.S. nuclear power plants (NPPs). These tools numerically model not only the thermal-hydraulic behavior of the reactors primary and secondary systems, but also external event temporal evolution and component/system ageing. Thus, this is not only a multi-physics problem being addressed, but also a multi-scale problem (both spatial, µm-mm-m, and temporal, seconds-hours-years). As part of the RISMC CRA approach, a large amount of computationally-expensive simulation runs may be required. An important aspect is that even though computational power is growing, themore » overall computational cost of a RISMC analysis using brute-force methods may be not viable for certain cases. A solution that is being evaluated to assist the computational issue is the use of reduced order modeling techniques. During the FY2015, we investigated and applied reduced order modeling techniques to decrease the RISMC analysis computational cost by decreasing the number of simulation runs; for this analysis improvement we used surrogate models instead of the actual simulation codes. This article focuses on the use of reduced order modeling techniques that can be applied to RISMC analyses in order to generate, analyze, and visualize data. In particular, we focus on surrogate models that approximate the simulation results but in a much faster time (microseconds instead of hours/days).« less

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

NASA Astrophysics Data System (ADS)

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-05-01

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructs high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss-Lobatto-Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

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

Gao, Kai; Fu, Shubin; Chung, Eric T.

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

When Machines Think: Radiology's Next Frontier.

PubMed

Dreyer, Keith J; Geis, J Raymond

2017-12-01

Artificial intelligence (AI), machine learning, and deep learning are terms now seen frequently, all of which refer to computer algorithms that change as they are exposed to more data. Many of these algorithms are surprisingly good at recognizing objects in images. The combination of large amounts of machine-consumable digital data, increased and cheaper computing power, and increasingly sophisticated statistical models combine to enable machines to find patterns in data in ways that are not only cost-effective but also potentially beyond humans' abilities. Building an AI algorithm can be surprisingly easy. Understanding the associated data structures and statistics, on the other hand, is often difficult and obscure. Converting the algorithm into a sophisticated product that works consistently in broad, general clinical use is complex and incompletely understood. To show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow. Radiology has the chance to leverage AI to become a center of intelligently aggregated, quantitative, diagnostic information. Centaur radiologists, formed as a synergy of human plus computer, will provide interpretations using data extracted from images by humans and image-analysis computer algorithms, as well as the electronic health record, genomics, and other disparate sources. These interpretations will form the foundation of precision health care, or care customized to an individual patient. © RSNA, 2017.

A high-order multiscale finite-element method for time-domain acoustic-wave modeling

DOE PAGES

Gao, Kai; Fu, Shubin; Chung, Eric T.

2018-02-04

Accurate and efficient wave equation modeling is vital for many applications in such as acoustics, electromagnetics, and seismology. However, solving the wave equation in large-scale and highly heterogeneous models is usually computationally expensive because the computational cost is directly proportional to the number of grids in the model. We develop a novel high-order multiscale finite-element method to reduce the computational cost of time-domain acoustic-wave equation numerical modeling by solving the wave equation on a coarse mesh based on the multiscale finite-element theory. In contrast to existing multiscale finite-element methods that use only first-order multiscale basis functions, our new method constructsmore » high-order multiscale basis functions from local elliptic problems which are closely related to the Gauss–Lobatto–Legendre quadrature points in a coarse element. Essentially, these basis functions are not only determined by the order of Legendre polynomials, but also by local medium properties, and therefore can effectively convey the fine-scale information to the coarse-scale solution with high-order accuracy. Numerical tests show that our method can significantly reduce the computation time while maintain high accuracy for wave equation modeling in highly heterogeneous media by solving the corresponding discrete system only on the coarse mesh with the new high-order multiscale basis functions.« less

Energy Efficiency Challenges of 5G Small Cell Networks.

PubMed

Ge, Xiaohu; Yang, Jing; Gharavi, Hamid; Sun, Yang

2017-05-01

The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks.

Energy Efficiency Challenges of 5G Small Cell Networks

PubMed Central

Ge, Xiaohu; Yang, Jing; Gharavi, Hamid; Sun, Yang

2017-01-01

The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell base stations (BSs). Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks. PMID:28757670

Semi-Lagrangian particle methods for high-dimensional Vlasov-Poisson systems

NASA Astrophysics Data System (ADS)

Cottet, Georges-Henri

2018-07-01

This paper deals with the implementation of high order semi-Lagrangian particle methods to handle high dimensional Vlasov-Poisson systems. It is based on recent developments in the numerical analysis of particle methods and the paper focuses on specific algorithmic features to handle large dimensions. The methods are tested with uniform particle distributions in particular against a recent multi-resolution wavelet based method on a 4D plasma instability case and a 6D gravitational case. Conservation properties, accuracy and computational costs are monitored. The excellent accuracy/cost trade-off shown by the method opens new perspective for accurate simulations of high dimensional kinetic equations by particle methods.

Automatic vehicle monitoring systems study. Report of phase O. Volume 1: Executive summary

NASA Technical Reports Server (NTRS)

1977-01-01

A set of planning guidelines is presented to help law enforcement agencies and vehicle fleet operators decide which automatic vehicle monitoring (AVM) system could best meet their performance requirements. Improvements in emergency response times and resultant cost benefits obtainable with various operational and planned AVM systems may be synthesized and simulated by means of special computer programs for model city parameters applicable to small, medium, and large urban areas. Design characteristics of various AVM systems and the implementation requirements are illustrated and cost estimated for the vehicles, the fixed sites, and the base equipments. Vehicle location accuracies for different RF links and polling intervals are analyzed.

Optimization of Aerospace Structure Subject to Damage Tolerance Criteria

NASA Technical Reports Server (NTRS)

Akgun, Mehmet A.

1999-01-01

The objective of this cooperative agreement was to seek computationally efficient ways to optimize aerospace structures subject to damage tolerance criteria. Optimization was to involve sizing as well as topology optimization. The work was done in collaboration with Steve Scotti, Chauncey Wu and Joanne Walsh at the NASA Langley Research Center. Computation of constraint sensitivity is normally the most time-consuming step of an optimization procedure. The cooperative work first focused on this issue and implemented the adjoint method of sensitivity computation in an optimization code (runstream) written in Engineering Analysis Language (EAL). The method was implemented both for bar and plate elements including buckling sensitivity for the latter. Lumping of constraints was investigated as a means to reduce the computational cost. Adjoint sensitivity computation was developed and implemented for lumped stress and buckling constraints. Cost of the direct method and the adjoint method was compared for various structures with and without lumping. The results were reported in two papers. It is desirable to optimize topology of an aerospace structure subject to a large number of damage scenarios so that a damage tolerant structure is obtained. Including damage scenarios in the design procedure is critical in order to avoid large mass penalties at later stages. A common method for topology optimization is that of compliance minimization which has not been used for damage tolerant design. In the present work, topology optimization is treated as a conventional problem aiming to minimize the weight subject to stress constraints. Multiple damage configurations (scenarios) are considered. Each configuration has its own structural stiffness matrix and, normally, requires factoring of the matrix and solution of the system of equations. Damage that is expected to be tolerated is local and represents a small change in the stiffness matrix compared to the baseline (undamaged) structure. The exact solution to a slightly modified set of equations can be obtained from the baseline solution economically without actually solving the modified system. Sherrnan-Morrison-Woodbury (SMW) formulas are matrix update formulas that allow this. SMW formulas were therefore used here to compute adjoint displacements for sensitivity computation and structural displacements in damaged configurations.

Parallel implementation of the particle simulation method with dynamic load balancing: Toward realistic geodynamical simulation

NASA Astrophysics Data System (ADS)

Furuichi, M.; Nishiura, D.

2015-12-01

Fully Lagrangian methods such as Smoothed Particle Hydrodynamics (SPH) and Discrete Element Method (DEM) have been widely used to solve the continuum and particles motions in the computational geodynamics field. These mesh-free methods are suitable for the problems with the complex geometry and boundary. In addition, their Lagrangian nature allows non-diffusive advection useful for tracking history dependent properties (e.g. rheology) of the material. These potential advantages over the mesh-based methods offer effective numerical applications to the geophysical flow and tectonic processes, which are for example, tsunami with free surface and floating body, magma intrusion with fracture of rock, and shear zone pattern generation of granular deformation. In order to investigate such geodynamical problems with the particle based methods, over millions to billion particles are required for the realistic simulation. Parallel computing is therefore important for handling such huge computational cost. An efficient parallel implementation of SPH and DEM methods is however known to be difficult especially for the distributed-memory architecture. Lagrangian methods inherently show workload imbalance problem for parallelization with the fixed domain in space, because particles move around and workloads change during the simulation. Therefore dynamic load balance is key technique to perform the large scale SPH and DEM simulation. In this work, we present the parallel implementation technique of SPH and DEM method utilizing dynamic load balancing algorithms toward the high resolution simulation over large domain using the massively parallel super computer system. Our method utilizes the imbalances of the executed time of each MPI process as the nonlinear term of parallel domain decomposition and minimizes them with the Newton like iteration method. In order to perform flexible domain decomposition in space, the slice-grid algorithm is used. Numerical tests show that our approach is suitable for solving the particles with different calculation costs (e.g. boundary particles) as well as the heterogeneous computer architecture. We analyze the parallel efficiency and scalability on the super computer systems (K-computer, Earth simulator 3, etc.).

A Process Management System for Networked Manufacturing

NASA Astrophysics Data System (ADS)

Liu, Tingting; Wang, Huifen; Liu, Linyan

With the development of computer, communication and network, networked manufacturing has become one of the main manufacturing paradigms in the 21st century. Under the networked manufacturing environment, there exist a large number of cooperative tasks susceptible to alterations, conflicts caused by resources and problems of cost and quality. This increases the complexity of administration. Process management is a technology used to design, enact, control, and analyze networked manufacturing processes. It supports efficient execution, effective management, conflict resolution, cost containment and quality control. In this paper we propose an integrated process management system for networked manufacturing. Requirements of process management are analyzed and architecture of the system is presented. And a process model considering process cost and quality is developed. Finally a case study is provided to explain how the system runs efficiently.

Optimization of Angular-Momentum Biases of Reaction Wheels

NASA Technical Reports Server (NTRS)

Lee, Clifford; Lee, Allan

2008-01-01

RBOT [RWA Bias Optimization Tool (wherein RWA signifies Reaction Wheel Assembly )] is a computer program designed for computing angular momentum biases for reaction wheels used for providing spacecraft pointing in various directions as required for scientific observations. RBOT is currently deployed to support the Cassini mission to prevent operation of reaction wheels at unsafely high speeds while minimizing time in undesirable low-speed range, where elasto-hydrodynamic lubrication films in bearings become ineffective, leading to premature bearing failure. The problem is formulated as a constrained optimization problem in which maximum wheel speed limit is a hard constraint and a cost functional that increases as speed decreases below a low-speed threshold. The optimization problem is solved using a parametric search routine known as the Nelder-Mead simplex algorithm. To increase computational efficiency for extended operation involving large quantity of data, the algorithm is designed to (1) use large time increments during intervals when spacecraft attitudes or rates of rotation are nearly stationary, (2) use sinusoidal-approximation sampling to model repeated long periods of Earth-point rolling maneuvers to reduce computational loads, and (3) utilize an efficient equation to obtain wheel-rate profiles as functions of initial wheel biases based on conservation of angular momentum (in an inertial frame) using pre-computed terms.

Digital robust active control law synthesis for large order systems using constrained optimization

NASA Technical Reports Server (NTRS)

Mukhopadhyay, Vivek

1987-01-01

This paper presents a direct digital control law synthesis procedure for a large order, sampled data, linear feedback system using constrained optimization techniques to meet multiple design requirements. A linear quadratic Gaussian type cost function is minimized while satisfying a set of constraints on the design loads and responses. General expressions for gradients of the cost function and constraints, with respect to the digital control law design variables are derived analytically and computed by solving a set of discrete Liapunov equations. The designer can choose the structure of the control law and the design variables, hence a stable classical control law as well as an estimator-based full or reduced order control law can be used as an initial starting point. Selected design responses can be treated as constraints instead of lumping them into the cost function. This feature can be used to modify a control law, to meet individual root mean square response limitations as well as minimum single value restrictions. Low order, robust digital control laws were synthesized for gust load alleviation of a flexible remotely piloted drone aircraft.

Manual of phosphoric acid fuel cell power plant cost model and computer program

NASA Technical Reports Server (NTRS)

Lu, C. Y.; Alkasab, K. A.

1984-01-01

Cost analysis of phosphoric acid fuel cell power plant includes two parts: a method for estimation of system capital costs, and an economic analysis which determines the levelized annual cost of operating the system used in the capital cost estimation. A FORTRAN computer has been developed for this cost analysis.

Indoor Pedestrian Localization Using iBeacon and Improved Kalman Filter.

PubMed

Sung, Kwangjae; Lee, Dong Kyu 'Roy'; Kim, Hwangnam

2018-05-26

The reliable and accurate indoor pedestrian positioning is one of the biggest challenges for location-based systems and applications. Most pedestrian positioning systems have drift error and large bias due to low-cost inertial sensors and random motions of human being, as well as unpredictable and time-varying radio-frequency (RF) signals used for position determination. To solve this problem, many indoor positioning approaches that integrate the user's motion estimated by dead reckoning (DR) method and the location data obtained by RSS fingerprinting through Bayesian filter, such as the Kalman filter (KF), unscented Kalman filter (UKF), and particle filter (PF), have recently been proposed to achieve higher positioning accuracy in indoor environments. Among Bayesian filtering methods, PF is the most popular integrating approach and can provide the best localization performance. However, since PF uses a large number of particles for the high performance, it can lead to considerable computational cost. This paper presents an indoor positioning system implemented on a smartphone, which uses simple dead reckoning (DR), RSS fingerprinting using iBeacon and machine learning scheme, and improved KF. The core of the system is the enhanced KF called a sigma-point Kalman particle filter (SKPF), which localize the user leveraging both the unscented transform of UKF and the weighting method of PF. The SKPF algorithm proposed in this study is used to provide the enhanced positioning accuracy by fusing positional data obtained from both DR and fingerprinting with uncertainty. The SKPF algorithm can achieve better positioning accuracy than KF and UKF and comparable performance compared to PF, and it can provide higher computational efficiency compared with PF. iBeacon in our positioning system is used for energy-efficient localization and RSS fingerprinting. We aim to design the localization scheme that can realize the high positioning accuracy, computational efficiency, and energy efficiency through the SKPF and iBeacon indoors. Empirical experiments in real environments show that the use of the SKPF algorithm and iBeacon in our indoor localization scheme can achieve very satisfactory performance in terms of localization accuracy, computational cost, and energy efficiency.

A New Framework for Effective and Efficient Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin

2015-04-01

Earth and Environmental Systems (EES) models are essential components of research, development, and decision-making in science and engineering disciplines. With continuous advances in understanding and computing power, such models are becoming more complex with increasingly more factors to be specified (model parameters, forcings, boundary conditions, etc.). To facilitate better understanding of the role and importance of different factors in producing the model responses, the procedure known as 'Sensitivity Analysis' (SA) can be very helpful. Despite the availability of a large body of literature on the development and application of various SA approaches, two issues continue to pose major challenges: (1) Ambiguous Definition of Sensitivity - Different SA methods are based in different philosophies and theoretical definitions of sensitivity, and can result in different, even conflicting, assessments of the underlying sensitivities for a given problem, (2) Computational Cost - The cost of carrying out SA can be large, even excessive, for high-dimensional problems and/or computationally intensive models. In this presentation, we propose a new approach to sensitivity analysis that addresses the dual aspects of 'effectiveness' and 'efficiency'. By effective, we mean achieving an assessment that is both meaningful and clearly reflective of the objective of the analysis (the first challenge above), while by efficiency we mean achieving statistically robust results with minimal computational cost (the second challenge above). Based on this approach, we develop a 'global' sensitivity analysis framework that efficiently generates a newly-defined set of sensitivity indices that characterize a range of important properties of metric 'response surfaces' encountered when performing SA on EES models. Further, we show how this framework embraces, and is consistent with, a spectrum of different concepts regarding 'sensitivity', and that commonly-used SA approaches (e.g., Sobol, Morris, etc.) are actually limiting cases of our approach under specific conditions. Multiple case studies are used to demonstrate the value of the new framework. The results show that the new framework provides a fundamental understanding of the underlying sensitivities for any given problem, while requiring orders of magnitude fewer model runs.

Classification of large-sized hyperspectral imagery using fast machine learning algorithms

NASA Astrophysics Data System (ADS)

Xia, Junshi; Yokoya, Naoto; Iwasaki, Akira

2017-07-01

We present a framework of fast machine learning algorithms in the context of large-sized hyperspectral images classification from the theoretical to a practical viewpoint. In particular, we assess the performance of random forest (RF), rotation forest (RoF), and extreme learning machine (ELM) and the ensembles of RF and ELM. These classifiers are applied to two large-sized hyperspectral images and compared to the support vector machines. To give the quantitative analysis, we pay attention to comparing these methods when working with high input dimensions and a limited/sufficient training set. Moreover, other important issues such as the computational cost and robustness against the noise are also discussed.

Assessment of distributed solar power systems: Issues and impacts

NASA Astrophysics Data System (ADS)

Moyle, R. A.; Chernoff, H.; Schweizer, T. C.; Patton, J. B.

1982-11-01

The installation of distributed solar-power systems presents electric utilities with a host of questions. Some of the technical and economic impacts of these systems are discussed. Among the technical interconnect issues are isolated operation, power quality, line safety, and metering options. Economic issues include user purchase criteria, structures and installation costs, marketing and product distribution costs, and interconnect costs. An interactive computer program that allows easy calculation of allowable system prices and allowable generation-equipment prices was developed as part of this project. It is concluded that the technical problems raised by distributed solar systems are surmountable, but their resolution may be costly. The stringent purchase criteria likely to be imposed by many potential system users and the economies of large-scale systems make small systems (less than 10 to 20 kW) less attractive than larger systems. Utilities that consider life-cycle costs in making investment decisions and third-party investors who have tax and financial advantages are likely to place the highest value on solar-power systems.

Infection risk and cost-effectiveness of commercial bags or glass bottles for total parenteral nutrition.

PubMed

Durand-Zaleski, I; Delaunay, L; Langeron, O; Belda, E; Astier, A; Brun-Buisson, C

1997-03-01

To determine whether the greater daily expense of administering total parenteral nutrition (TPN) via plastic bags changed once daily, compared to glass bottles changed thrice daily, could be offset by savings from a reduction in nosocomial infections. The costs and potential benefits of commercially available TPN bags and TPN in glass containers were compared. Costs were computed from the viewpoint of the hospital, first in a general model and then for two specific examples, Crohn's disease and intensive-care unit (ICU) patients. The extra cost of using bags was $20 per day. The total cost of nosocomial bacteremia was estimated at $6,000. The monetary benefits of using TPN bags were $6,000XT, where XT was the percentage of nosocomial infections averted. We also considered that reduction in intravenous (IV)-line manipulation could reduce bacteremia-related mortality and computed a cost-per-life-saved ratio. Modeling showed that TPN in bags could yield a net benefit when the absolute reduction in the daily risk of nosocomial bacteremia reached the threshold value of 0.3%. Such a reduction could not be attained in patients with Crohn's disease, and corresponded to a 50% to 60% reduction of infection rates in ICU patients. Varying the risk of mortality attributable to IV-line-related infection from 1% to 13% resulted in a cost effectiveness of using TPN bags ranging from $90,000 to $7,000 per life saved in ICU, assuming a two-thirds reduction in IV-line infections, and from $180,000 to $14,000 if the infection rate was reduced by one third. The baseline cost-minimization analysis concluded that the extra cost of TPN bags was not justified by the extra savings. The cost-effectiveness analysis, however, found that the cost per life saved fell within the accepted range of public health interventions, provided a large fraction of infections are averted using TPN bags.

Economic Burden of Heart Failure: Investigating Outpatient and Inpatient Costs in Abeokuta, Southwest Nigeria

PubMed Central

Ogah, Okechukwu S.; Stewart, Simon; Onwujekwe, Obinna E.; Falase, Ayodele O.; Adebayo, Saheed O.; Olunuga, Taiwo; Sliwa, Karen

2014-01-01

Background: Heart failure (HF) is a deadly, disabling and often costly syndrome world-wide. Unfortunately, there is a paucity of data describing its economic impact in sub Saharan Africa; a region in which the number of relatively younger cases will inevitably rise. Methods: Heath economic data were extracted from a prospective HF registry in a tertiary hospital situated in Abeokuta, southwest Nigeria. Outpatient and inpatient costs were computed from a representative cohort of 239 HF cases including personnel, diagnostic and treatment resources used for their management over a 12-month period. Indirect costs were also calculated. The annual cost per person was then calculated. Results: Mean age of the cohort was 58.0±15.1 years and 53.1% were men. The total computed cost of care of HF in Abeokuta was 76, 288,845 Nigerian Naira (US$508, 595) translating to 319,200 Naira (US$2,128 US Dollars) per patient per year. The total cost of in-patient care (46% of total health care expenditure) was estimated as 34,996,477 Naira (about 301,230 US dollars). This comprised of 17,899,977 Naira- 50.9% ($US114,600) and 17,806,500 naira −49.1%($US118,710) for direct and in-direct costs respectively. Out-patient cost was estimated as 41,292,368 Naira ($US 275,282). The relatively high cost of outpatient care was largely due to cost of transportation for monthly follow up visits. Payments were mostly made through out-of-pocket spending. Conclusion: The economic burden of HF in Nigeria is particularly high considering, the relatively young age of affected cases, a minimum wage of 18,000 Naira ($US120) per month and considerable component of out-of-pocket spending for those affected. Health reforms designed to mitigate the individual to societal burden imposed by the syndrome are required. PMID:25415310

Economic burden of heart failure: investigating outpatient and inpatient costs in Abeokuta, Southwest Nigeria.

PubMed

Ogah, Okechukwu S; Stewart, Simon; Onwujekwe, Obinna E; Falase, Ayodele O; Adebayo, Saheed O; Olunuga, Taiwo; Sliwa, Karen

2014-01-01

Heart failure (HF) is a deadly, disabling and often costly syndrome world-wide. Unfortunately, there is a paucity of data describing its economic impact in sub Saharan Africa; a region in which the number of relatively younger cases will inevitably rise. Heath economic data were extracted from a prospective HF registry in a tertiary hospital situated in Abeokuta, southwest Nigeria. Outpatient and inpatient costs were computed from a representative cohort of 239 HF cases including personnel, diagnostic and treatment resources used for their management over a 12-month period. Indirect costs were also calculated. The annual cost per person was then calculated. Mean age of the cohort was 58.0 ± 15.1 years and 53.1% were men. The total computed cost of care of HF in Abeokuta was 76, 288,845 Nigerian Naira (US$508, 595) translating to 319,200 Naira (US$2,128 US Dollars) per patient per year. The total cost of in-patient care (46% of total health care expenditure) was estimated as 34,996,477 Naira (about 301,230 US dollars). This comprised of 17,899,977 Naira- 50.9% ($US114,600) and 17,806,500 naira -49.1%($US118,710) for direct and in-direct costs respectively. Out-patient cost was estimated as 41,292,368 Naira ($US 275,282). The relatively high cost of outpatient care was largely due to cost of transportation for monthly follow up visits. Payments were mostly made through out-of-pocket spending. The economic burden of HF in Nigeria is particularly high considering, the relatively young age of affected cases, a minimum wage of 18,000 Naira ($US120) per month and considerable component of out-of-pocket spending for those affected. Health reforms designed to mitigate the individual to societal burden imposed by the syndrome are required.

Improving Barotropic Tides by Two-way Nesting High and Low Resolution Domains

NASA Astrophysics Data System (ADS)

Jeon, C. H.; Buijsman, M. C.; Wallcraft, A. J.; Shriver, J. F.; Hogan, P. J.; Arbic, B. K.; Richman, J. G.

2017-12-01

In a realistically forced global ocean model, relatively large sea-surface-height root-mean-square (RMS) errors are observed in the North Atlantic near the Hudson Strait. These may be associated with large tidal resonances interacting with coastal bathymetry that are not correctly represented with a low resolution grid. This issue can be overcome by using high resolution grids, but at a high computational cost. In this paper we apply two-way nesting as an alternative solution. This approach applies high resolution to the area with large RMS errors and a lower resolution to the rest. It is expected to improve the tidal solution as well as reduce the computational cost. To minimize modification of the original source codes of the ocean circulation model (HYCOM), we apply the coupler OASIS3-MCT. This coupler is used to exchange barotropic pressures and velocity fields through its APIs (Application Programming Interface) between the parent and the child components. The developed two-way nesting framework has been validated with an idealized test case where the parent and the child domains have identical grid resolutions. The result of the idealized case shows very small RMS errors between the child and parent solutions. We plan to show results for a case with realistic tidal forcing in which the resolution of the child grid is three times that of the parent grid. The numerical results of this realistic case are compared to TPXO data.

Costs of rearing children in agricultural economies: an alternative estimation approach and findings from rural Bangladesh.

PubMed

Khan, M M; Magnani, R J; Mock, N B; Saadat, Y S

1993-03-01

There are changes in child costs during demographic transition. This study examines household time allocation from 66 agricultural households in 3 villages in Tangail District in rural north central Bangladesh in 1984-85 (371 days). Component and total child-rearing costs are estimated in alternative ways. Conventional "opportunity wage" measures are considered overestimated. The methodological shortcomings of direct cost accounting procedures and consumer demand methods in computing time cost and monetary cost of child rearing are pointed out. In this study's alternative computation, age standardized equivalent costs are generated. Child food consumption costs were generated from a large national survey conducted in 1983. Nonfood expenditures were estimated by food to nonfood expenditure ratios taken from the aforementioned survey. For estimating breast-feeding costs, an estimate was produced based on the assumption that costs for infant food consumption were a fixed proportion of food costs for older children. Land ownership groups were set up to reflect socioeconomic status: 1) landless households, 2) marginal farm households with 1 acre or .4 hectares of land, 3) middle income households with 1-2 acres of land, 4) upper middle income households with 2-4 acres of land, and 5) upper income or rich households with over 4 acres of land. The nonmarket wage rate for hired household help was used to determine the value of cooking, fetching water, and household cleaning and repairing. The results confirm the low costs of child rearing in high fertility societies. Productive nonmarket activities are effective in subsidizing the costs of children. The addition of a child into households already with children has a low impact on time costs of children; "this economies of scale effect is estimated ... at 20%." The highest relative costs were found in the lowest income households, and the lowest costs were in the highest income households. 5% of total household income is devoted to child rearing in the lowest income households compared to 1% of income in the highest income households. The implications are that fertility decline is more directly related to structural changes in the economy, satisfaction of existing demand for family planning, and the producing additional demand for fertility control.

An evaluation of superminicomputers for thermal analysis

NASA Technical Reports Server (NTRS)

Storaasli, O. O.; Vidal, J. B.; Jones, G. K.

1982-01-01

The use of superminicomputers for solving a series of increasingly complex thermal analysis problems is investigated. The approach involved (1) installation and verification of the SPAR thermal analyzer software on superminicomputers at Langley Research Center and Goddard Space Flight Center, (2) solution of six increasingly complex thermal problems on this equipment, and (3) comparison of solution (accuracy, CPU time, turnaround time, and cost) with solutions on large mainframe computers.

Electronic and mechanical improvement of the receiving terminal of a free-space microwave power transmission system

NASA Technical Reports Server (NTRS)

Brown, W. C.

1977-01-01

Significant advancements were made in a number of areas: improved efficiency of basic receiving element at low power density levels, improved resolution and confidence in efficiency measurements mathematical modelling and computer simulation of the receiving element and the design, construction, and testing of an environmentally protected two-plane construction suitable for low cost, highly automated construction of large receiving arrays.

Computer Modeling of Thoracic Response to Blast

DTIC Science & Technology

1988-01-01

be solved at reasonable cost. intrathoracic pressure responses for subjects wearing In order to determine if the gas content of the sheep ballistic...spatial and temporal ries were compared with data. Two extreme cases had distribution of the load can be reasonably predicted by the rumen filled with...to the ap- is that sheep have large, multiple stomachs that have a proximate location where intrathoracic pressure meas- considerable air content . It

Simulation-based optimization of lattice support structures for offshore wind energy converters with the simultaneous perturbation algorithm

NASA Astrophysics Data System (ADS)

Molde, H.; Zwick, D.; Muskulus, M.

2014-12-01

Support structures for offshore wind turbines are contributing a large part to the total project cost, and a cost saving of a few percent would have considerable impact. At present support structures are designed with simplified methods, e.g., spreadsheet analysis, before more detailed load calculations are performed. Due to the large number of loadcases only a few semimanual design iterations are typically executed. Computer-assisted optimization algorithms could help to further explore design limits and avoid unnecessary conservatism. In this study the simultaneous perturbation stochastic approximation method developed by Spall in the 1990s was assessed with respect to its suitability for support structure optimization. The method depends on a few parameters and an objective function that need to be chosen carefully. In each iteration the structure is evaluated by time-domain analyses, and joint fatigue lifetimes and ultimate strength utilization are computed from stress concentration factors. A pseudo-gradient is determined from only two analysis runs and the design is adjusted in the direction that improves it the most. The algorithm is able to generate considerably improved designs, compared to other methods, in a few hundred iterations, which is demonstrated for the NOWITECH 10 MW reference turbine.

A computer simulator for development of engineering system design methodologies

NASA Technical Reports Server (NTRS)

Padula, S. L.; Sobieszczanski-Sobieski, J.

1987-01-01

A computer program designed to simulate and improve engineering system design methodology is described. The simulator mimics the qualitative behavior and data couplings occurring among the subsystems of a complex engineering system. It eliminates the engineering analyses in the subsystems by replacing them with judiciously chosen analytical functions. With the cost of analysis eliminated, the simulator is used for experimentation with a large variety of candidate algorithms for multilevel design optimization to choose the best ones for the actual application. Thus, the simulator serves as a development tool for multilevel design optimization strategy. The simulator concept, implementation, and status are described and illustrated with examples.

Fast Reduction Method in Dominance-Based Information Systems

NASA Astrophysics Data System (ADS)

Li, Yan; Zhou, Qinghua; Wen, Yongchuan

2018-01-01

In real world applications, there are often some data with continuous values or preference-ordered values. Rough sets based on dominance relations can effectively deal with these kinds of data. Attribute reduction can be done in the framework of dominance-relation based approach to better extract decision rules. However, the computational cost of the dominance classes greatly affects the efficiency of attribute reduction and rule extraction. This paper presents an efficient method of computing dominance classes, and further compares it with traditional method with increasing attributes and samples. Experiments on UCI data sets show that the proposed algorithm obviously improves the efficiency of the traditional method, especially for large-scale data.

Characteristic analysis and simulation for polysilicon comb micro-accelerometer

NASA Astrophysics Data System (ADS)

Liu, Fengli; Hao, Yongping

2008-10-01

High force update rate is a key factor for achieving high performance haptic rendering, which imposes a stringent real time requirement upon the execution environment of the haptic system. This requirement confines the haptic system to simplified environment for reducing the computation cost of haptic rendering algorithms. In this paper, we present a novel "hyper-threading" architecture consisting of several threads for haptic rendering. The high force update rate is achieved with relatively large computation time interval for each haptic loop. The proposed method was testified and proved to be effective with experiments on virtual wall prototype haptic system via Delta Haptic Device.

Cogeneration technology alternatives study. Volume 6: Computer data

NASA Technical Reports Server (NTRS)

1980-01-01

The potential technical capabilities of energy conversion systems in the 1985 - 2000 time period were defined with emphasis on systems using coal, coal-derived fuels or alternate fuels. Industrial process data developed for the large energy consuming industries serve as a framework for the cogeneration applications. Ground rules for the study were established and other necessary equipment (balance-of-plant) was defined. This combination of technical information, energy conversion system data ground rules, industrial process information and balance-of-plant characteristics was analyzed to evaluate energy consumption, capital and operating costs and emissions. Data in the form of computer printouts developed for 3000 energy conversion system-industrial process combinations are presented.

Computationally efficient method for Fourier transform of highly chirped pulses for laser and parametric amplifier modeling.

PubMed

Andrianov, Alexey; Szabo, Aron; Sergeev, Alexander; Kim, Arkady; Chvykov, Vladimir; Kalashnikov, Mikhail

2016-11-14

We developed an improved approach to calculate the Fourier transform of signals with arbitrary large quadratic phase which can be efficiently implemented in numerical simulations utilizing Fast Fourier transform. The proposed algorithm significantly reduces the computational cost of Fourier transform of a highly chirped and stretched pulse by splitting it into two separate transforms of almost transform limited pulses, thereby reducing the required grid size roughly by a factor of the pulse stretching. The application of our improved Fourier transform algorithm in the split-step method for numerical modeling of CPA and OPCPA shows excellent agreement with standard algorithms.

Secure public cloud platform for medical images sharing.

PubMed

Pan, Wei; Coatrieux, Gouenou; Bouslimi, Dalel; Prigent, Nicolas

2015-01-01

Cloud computing promises medical imaging services offering large storage and computing capabilities for limited costs. In this data outsourcing framework, one of the greatest issues to deal with is data security. To do so, we propose to secure a public cloud platform devoted to medical image sharing by defining and deploying a security policy so as to control various security mechanisms. This policy stands on a risk assessment we conducted so as to identify security objectives with a special interest for digital content protection. These objectives are addressed by means of different security mechanisms like access and usage control policy, partial-encryption and watermarking.

Two-Level Chebyshev Filter Based Complementary Subspace Method: Pushing the Envelope of Large-Scale Electronic Structure Calculations.

PubMed

Banerjee, Amartya S; Lin, Lin; Suryanarayana, Phanish; Yang, Chao; Pask, John E

2018-06-12

We describe a novel iterative strategy for Kohn-Sham density functional theory calculations aimed at large systems (>1,000 electrons), applicable to metals and insulators alike. In lieu of explicit diagonalization of the Kohn-Sham Hamiltonian on every self-consistent field (SCF) iteration, we employ a two-level Chebyshev polynomial filter based complementary subspace strategy to (1) compute a set of vectors that span the occupied subspace of the Hamiltonian; (2) reduce subspace diagonalization to just partially occupied states; and (3) obtain those states in an efficient, scalable manner via an inner Chebyshev filter iteration. By reducing the necessary computation to just partially occupied states and obtaining these through an inner Chebyshev iteration, our approach reduces the cost of large metallic calculations significantly, while eliminating subspace diagonalization for insulating systems altogether. We describe the implementation of the method within the framework of the discontinuous Galerkin (DG) electronic structure method and show that this results in a computational scheme that can effectively tackle bulk and nano systems containing tens of thousands of electrons, with chemical accuracy, within a few minutes or less of wall clock time per SCF iteration on large-scale computing platforms. We anticipate that our method will be instrumental in pushing the envelope of large-scale ab initio molecular dynamics. As a demonstration of this, we simulate a bulk silicon system containing 8,000 atoms at finite temperature, and obtain an average SCF step wall time of 51 s on 34,560 processors; thus allowing us to carry out 1.0 ps of ab initio molecular dynamics in approximately 28 h (of wall time).

The Quake Catcher Network: Cyberinfrastructure Bringing Seismology into Schools and Homes

NASA Astrophysics Data System (ADS)

Lawrence, J. F.; Cochran, E. S.

2007-12-01

We propose to implement a high density, low cost strong-motion network for rapid response and early warning by placing sensors in schools, homes, and offices. The Quake Catcher Network (QCN) will employ existing networked laptops and desktops to form the world's largest high-density, distributed computing seismic network. Costs for this network will be minimal because the QCN will use 1) strong motion sensors (accelerometers) already internal to many laptops and 2) nearly identical low-cost universal serial bus (USB) accelerometers for use with desktops. The Berkeley Open Infrastructure for Network Computing (BOINC!) provides a free, proven paradigm for involving the public in large-scale computational research projects. As evidenced by the SETI@home program and others, individuals are especially willing to donate their unused computing power to projects that they deem relevant, worthwhile, and educational. The client- and server-side software will rapidly monitor incoming seismic signals, detect the magnitudes and locations of significant earthquakes, and may even provide early warnings to other computers and users before they can feel the earthquake. The software will provide the client-user with a screen-saver displaying seismic data recorded on their laptop, recently detected earthquakes, and general information about earthquakes and the geosciences. Furthermore, this project will install USB sensors in K-12 classrooms as an educational tool for teaching science. Through a variety of interactive experiments students will learn about earthquakes and the hazards earthquakes pose. For example, students can learn how the vibrations of an earthquake decrease with distance by jumping up and down at increasing distances from the sensor and plotting the decreased amplitude of the seismic signal measured on their computer. We hope to include an audio component so that students can hear and better understand the difference between low and high frequency seismic signals. The QCN will provide a natural way to engage students and the public in earthquake detection and research.

Do Clouds Compute? A Framework for Estimating the Value of Cloud Computing

NASA Astrophysics Data System (ADS)

Klems, Markus; Nimis, Jens; Tai, Stefan

On-demand provisioning of scalable and reliable compute services, along with a cost model that charges consumers based on actual service usage, has been an objective in distributed computing research and industry for a while. Cloud Computing promises to deliver on this objective: consumers are able to rent infrastructure in the Cloud as needed, deploy applications and store data, and access them via Web protocols on a pay-per-use basis. The acceptance of Cloud Computing, however, depends on the ability for Cloud Computing providers and consumers to implement a model for business value co-creation. Therefore, a systematic approach to measure costs and benefits of Cloud Computing is needed. In this paper, we discuss the need for valuation of Cloud Computing, identify key components, and structure these components in a framework. The framework assists decision makers in estimating Cloud Computing costs and to compare these costs to conventional IT solutions. We demonstrate by means of representative use cases how our framework can be applied to real world scenarios.

Parallel algorithm for multiscale atomistic/continuum simulations using LAMMPS

NASA Astrophysics Data System (ADS)

Pavia, F.; Curtin, W. A.

2015-07-01

Deformation and fracture processes in engineering materials often require simultaneous descriptions over a range of length and time scales, with each scale using a different computational technique. Here we present a high-performance parallel 3D computing framework for executing large multiscale studies that couple an atomic domain, modeled using molecular dynamics and a continuum domain, modeled using explicit finite elements. We use the robust Coupled Atomistic/Discrete-Dislocation (CADD) displacement-coupling method, but without the transfer of dislocations between atoms and continuum. The main purpose of the work is to provide a multiscale implementation within an existing large-scale parallel molecular dynamics code (LAMMPS) that enables use of all the tools associated with this popular open-source code, while extending CADD-type coupling to 3D. Validation of the implementation includes the demonstration of (i) stability in finite-temperature dynamics using Langevin dynamics, (ii) elimination of wave reflections due to large dynamic events occurring in the MD region and (iii) the absence of spurious forces acting on dislocations due to the MD/FE coupling, for dislocations further than 10 Å from the coupling boundary. A first non-trivial example application of dislocation glide and bowing around obstacles is shown, for dislocation lengths of ∼50 nm using fewer than 1 000 000 atoms but reproducing results of extremely large atomistic simulations at much lower computational cost.

Performance Comparison of Mainframe, Workstations, Clusters, and Desktop Computers

NASA Technical Reports Server (NTRS)

Farley, Douglas L.

2005-01-01

A performance evaluation of a variety of computers frequently found in a scientific or engineering research environment was conducted using a synthetic and application program benchmarks. From a performance perspective, emerging commodity processors have superior performance relative to legacy mainframe computers. In many cases, the PC clusters exhibited comparable performance with traditional mainframe hardware when 8-12 processors were used. The main advantage of the PC clusters was related to their cost. Regardless of whether the clusters were built from new computers or whether they were created from retired computers their performance to cost ratio was superior to the legacy mainframe computers. Finally, the typical annual maintenance cost of legacy mainframe computers is several times the cost of new equipment such as multiprocessor PC workstations. The savings from eliminating the annual maintenance fee on legacy hardware can result in a yearly increase in total computational capability for an organization.

PLUM: Parallel Load Balancing for Unstructured Adaptive Meshes. Degree awarded by Colorado Univ.

NASA Technical Reports Server (NTRS)

Oliker, Leonid

1998-01-01

Dynamic mesh adaption on unstructured grids is a powerful tool for computing large-scale problems that require grid modifications to efficiently resolve solution features. By locally refining and coarsening the mesh to capture physical phenomena of interest, such procedures make standard computational methods more cost effective. Unfortunately, an efficient parallel implementation of these adaptive methods is rather difficult to achieve, primarily due to the load imbalance created by the dynamically-changing nonuniform grid. This requires significant communication at runtime, leading to idle processors and adversely affecting the total execution time. Nonetheless, it is generally thought that unstructured adaptive- grid techniques will constitute a significant fraction of future high-performance supercomputing. Various dynamic load balancing methods have been reported to date; however, most of them either lack a global view of loads across processors or do not apply their techniques to realistic large-scale applications.

Improving User Notification on Frequently Changing HPC Environments

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

Fuson, Christopher B; Renaud, William A

2016-01-01

Today s HPC centers user environments can be very complex. Centers often contain multiple large complicated computational systems each with their own user environment. Changes to a system s environment can be very impactful; however, a center s user environment is, in one-way or another, frequently changing. Because of this, it is vital for centers to notify users of change. For users, untracked changes can be costly, resulting in unnecessary debug time as well as wasting valuable compute allocations and research time. Communicating frequent change to diverse user communities is a common and ongoing task for HPC centers. This papermore » will cover the OLCF s current processes and methods used to communicate change to users of the center s large Cray systems and supporting resources. The paper will share lessons learned and goals as well as practices, tools, and methods used to continually improve and reach members of the OLCF user community.« less

CG2Real: Improving the Realism of Computer Generated Images Using a Large Collection of Photographs.

PubMed

Johnson, Micah K; Dale, Kevin; Avidan, Shai; Pfister, Hanspeter; Freeman, William T; Matusik, Wojciech

2011-09-01

Computer-generated (CG) images have achieved high levels of realism. This realism, however, comes at the cost of long and expensive manual modeling, and often humans can still distinguish between CG and real images. We introduce a new data-driven approach for rendering realistic imagery that uses a large collection of photographs gathered from online repositories. Given a CG image, we retrieve a small number of real images with similar global structure. We identify corresponding regions between the CG and real images using a mean-shift cosegmentation algorithm. The user can then automatically transfer color, tone, and texture from matching regions to the CG image. Our system only uses image processing operations and does not require a 3D model of the scene, making it fast and easy to integrate into digital content creation workflows. Results of a user study show that our hybrid images appear more realistic than the originals.

A k-Vector Approach to Sampling, Interpolation, and Approximation

NASA Astrophysics Data System (ADS)

Mortari, Daniele; Rogers, Jonathan

2013-12-01

The k-vector search technique is a method designed to perform extremely fast range searching of large databases at computational cost independent of the size of the database. k-vector search algorithms have historically found application in satellite star-tracker navigation systems which index very large star catalogues repeatedly in the process of attitude estimation. Recently, the k-vector search algorithm has been applied to numerous other problem areas including non-uniform random variate sampling, interpolation of 1-D or 2-D tables, nonlinear function inversion, and solution of systems of nonlinear equations. This paper presents algorithms in which the k-vector search technique is used to solve each of these problems in a computationally-efficient manner. In instances where these tasks must be performed repeatedly on a static (or nearly-static) data set, the proposed k-vector-based algorithms offer an extremely fast solution technique that outperforms standard methods.

Comprehensive Anti-error Study on Power Grid Dispatching Based on Regional Regulation and Integration

NASA Astrophysics Data System (ADS)

Zhang, Yunju; Chen, Zhongyi; Guo, Ming; Lin, Shunsheng; Yan, Yinyang

2018-01-01

With the large capacity of the power system, the development trend of the large unit and the high voltage, the scheduling operation is becoming more frequent and complicated, and the probability of operation error increases. This paper aims at the problem of the lack of anti-error function, single scheduling function and low working efficiency for technical support system in regional regulation and integration, the integrated construction of the error prevention of the integrated architecture of the system of dispatching anti - error of dispatching anti - error of power network based on cloud computing has been proposed. Integrated system of error prevention of Energy Management System, EMS, and Operation Management System, OMS have been constructed either. The system architecture has good scalability and adaptability, which can improve the computational efficiency, reduce the cost of system operation and maintenance, enhance the ability of regional regulation and anti-error checking with broad development prospects.

Large-Eddy Simulation of Aeroacoustic Applications

NASA Technical Reports Server (NTRS)

Pruett, C. David; Sochacki, James S.

1999-01-01

This report summarizes work accomplished under a one-year NASA grant from NASA Langley Research Center (LaRC). The effort culminates three years of NASA-supported research under three consecutive one-year grants. The period of support was April 6, 1998, through April 5, 1999. By request, the grant period was extended at no-cost until October 6, 1999. Its predecessors have been directed toward adapting the numerical tool of large-eddy simulation (LES) to aeroacoustic applications, with particular focus on noise suppression in subsonic round jets. In LES, the filtered Navier-Stokes equations are solved numerically on a relatively coarse computational grid. Residual stresses, generated by scales of motion too small to be resolved on the coarse grid, are modeled. Although most LES incorporate spatial filtering, time-domain filtering affords certain conceptual and computational advantages, particularly for aeroacoustic applications. Consequently, this work has focused on the development of subgrid-scale (SGS) models that incorporate time-domain filters.

NASA/DOD Aerospace Knowledge Diffusion Research Project. Paper 19: Computer and information technology and aerospace knowledge diffusion

NASA Technical Reports Server (NTRS)

Pinelli, Thomas E.; Kennedy, John M.; Barclay, Rebecca O.; Bishop, Ann P.

1992-01-01

To remain a world leader in aerospace, the US must improve and maintain the professional competency of its engineers and scientists, increase the research and development (R&D) knowledge base, improve productivity, and maximize the integration of recent technological developments into the R&D process. How well these objectives are met, and at what cost, depends on a variety of factors, but largely on the ability of US aerospace engineers and scientists to acquire and process the results of federally funded R&D. The Federal Government's commitment to high speed computing and networking systems presupposes that computer and information technology will play a major role in the aerospace knowledge diffusion process. However, we know little about information technology needs, uses, and problems within the aerospace knowledge diffusion process. The use of computer and information technology by US aerospace engineers and scientists in academia, government, and industry is reported.

An immersed boundary method for modeling a dirty geometry data

NASA Astrophysics Data System (ADS)

Onishi, Keiji; Tsubokura, Makoto

2017-11-01

We present a robust, fast, and low preparation cost immersed boundary method (IBM) for simulating an incompressible high Re flow around highly complex geometries. The method is achieved by the dispersion of the momentum by the axial linear projection and the approximate domain assumption satisfying the mass conservation around the wall including cells. This methodology has been verified against an analytical theory and wind tunnel experiment data. Next, we simulate the problem of flow around a rotating object and demonstrate the ability of this methodology to the moving geometry problem. This methodology provides the possibility as a method for obtaining a quick solution at a next large scale supercomputer. This research was supported by MEXT as ``Priority Issue on Post-K computer'' (Development of innovative design and production processes) and used computational resources of the K computer provided by the RIKEN Advanced Institute for Computational Science.

MER-DIMES : a planetary landing application of computer vision

NASA Technical Reports Server (NTRS)

Cheng, Yang; Johnson, Andrew; Matthies, Larry

2005-01-01

During the Mars Exploration Rovers (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combines measurements from a descent camera, a radar altimeter and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm uses altitude and attitude measurements to rectify image data to level ground plane. Feature selection and tracking is employed in the rectified data to compute the horizontal motion between images. Differences of motion estimates are then compared to inertial measurements to verify correct feature tracking. DIMES combines sensor data from multiple sources in a novel way to create a low-cost, robust and computationally efficient velocity estimation solution, and DIMES is the first use of computer vision to control a spacecraft during planetary landing. In this paper, the detailed implementation of the DIMES algorithm and the results from the two landings on Mars are presented.

Fault-tolerant building-block computer study

NASA Technical Reports Server (NTRS)

Rennels, D. A.

1978-01-01

Ultra-reliable core computers are required for improving the reliability of complex military systems. Such computers can provide reliable fault diagnosis, failure circumvention, and, in some cases serve as an automated repairman for their host systems. A small set of building-block circuits which can be implemented as single very large integration devices, and which can be used with off-the-shelf microprocessors and memories to build self checking computer modules (SCCM) is described. Each SCCM is a microcomputer which is capable of detecting its own faults during normal operation and is described to communicate with other identical modules over one or more Mil Standard 1553A buses. Several SCCMs can be connected into a network with backup spares to provide fault-tolerant operation, i.e. automated recovery from faults. Alternative fault-tolerant SCCM configurations are discussed along with the cost and reliability associated with their implementation.

AI tools in computer based problem solving

NASA Technical Reports Server (NTRS)

Beane, Arthur J.

1988-01-01

The use of computers to solve value oriented, deterministic, algorithmic problems, has evolved a structured life cycle model of the software process. The symbolic processing techniques used, primarily in research, for solving nondeterministic problems, and those for which an algorithmic solution is unknown, have evolved a different model, much less structured. Traditionally, the two approaches have been used completely independently. With the advent of low cost, high performance 32 bit workstations executing identical software with large minicomputers and mainframes, it became possible to begin to merge both models into a single extended model of computer problem solving. The implementation of such an extended model on a VAX family of micro/mini/mainframe systems is described. Examples in both development and deployment of applications involving a blending of AI and traditional techniques are given.

Mendel-GPU: haplotyping and genotype imputation on graphics processing units

PubMed Central

Chen, Gary K.; Wang, Kai; Stram, Alex H.; Sobel, Eric M.; Lange, Kenneth

2012-01-01

Motivation: In modern sequencing studies, one can improve the confidence of genotype calls by phasing haplotypes using information from an external reference panel of fully typed unrelated individuals. However, the computational demands are so high that they prohibit researchers with limited computational resources from haplotyping large-scale sequence data. Results: Our graphics processing unit based software delivers haplotyping and imputation accuracies comparable to competing programs at a fraction of the computational cost and peak memory demand. Availability: Mendel-GPU, our OpenCL software, runs on Linux platforms and is portable across AMD and nVidia GPUs. Users can download both code and documentation at http://code.google.com/p/mendel-gpu/. Contact: gary.k.chen@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22954633

Computational modelling of large deformations in layered-silicate/PET nanocomposites near the glass transition

NASA Astrophysics Data System (ADS)

Figiel, Łukasz; Dunne, Fionn P. E.; Buckley, C. Paul

2010-01-01

Layered-silicate nanoparticles offer a cost-effective reinforcement for thermoplastics. Computational modelling has been employed to study large deformations in layered-silicate/poly(ethylene terephthalate) (PET) nanocomposites near the glass transition, as would be experienced during industrial forming processes such as thermoforming or injection stretch blow moulding. Non-linear numerical modelling was applied, to predict the macroscopic large deformation behaviour, with morphology evolution and deformation occurring at the microscopic level, using the representative volume element (RVE) approach. A physically based elasto-viscoplastic constitutive model, describing the behaviour of the PET matrix within the RVE, was numerically implemented into a finite element solver (ABAQUS) using an UMAT subroutine. The implementation was designed to be robust, for accommodating large rotations and stretches of the matrix local to, and between, the nanoparticles. The nanocomposite morphology was reconstructed at the RVE level using a Monte-Carlo-based algorithm that placed straight, high-aspect ratio particles according to the specified orientation and volume fraction, with the assumption of periodicity. Computational experiments using this methodology enabled prediction of the strain-stiffening behaviour of the nanocomposite, observed experimentally, as functions of strain, strain rate, temperature and particle volume fraction. These results revealed the probable origins of the enhanced strain stiffening observed: (a) evolution of the morphology (through particle re-orientation) and (b) early onset of stress-induced pre-crystallization (and hence lock-up of viscous flow), triggered by the presence of particles. The computational model enabled prediction of the effects of process parameters (strain rate, temperature) on evolution of the morphology, and hence on the end-use properties.

HBLAST: Parallelised sequence similarity--A Hadoop MapReducable basic local alignment search tool.

PubMed

O'Driscoll, Aisling; Belogrudov, Vladislav; Carroll, John; Kropp, Kai; Walsh, Paul; Ghazal, Peter; Sleator, Roy D

2015-04-01

The recent exponential growth of genomic databases has resulted in the common task of sequence alignment becoming one of the major bottlenecks in the field of computational biology. It is typical for these large datasets and complex computations to require cost prohibitive High Performance Computing (HPC) to function. As such, parallelised solutions have been proposed but many exhibit scalability limitations and are incapable of effectively processing "Big Data" - the name attributed to datasets that are extremely large, complex and require rapid processing. The Hadoop framework, comprised of distributed storage and a parallelised programming framework known as MapReduce, is specifically designed to work with such datasets but it is not trivial to efficiently redesign and implement bioinformatics algorithms according to this paradigm. The parallelisation strategy of "divide and conquer" for alignment algorithms can be applied to both data sets and input query sequences. However, scalability is still an issue due to memory constraints or large databases, with very large database segmentation leading to additional performance decline. Herein, we present Hadoop Blast (HBlast), a parallelised BLAST algorithm that proposes a flexible method to partition both databases and input query sequences using "virtual partitioning". HBlast presents improved scalability over existing solutions and well balanced computational work load while keeping database segmentation and recompilation to a minimum. Enhanced BLAST search performance on cheap memory constrained hardware has significant implications for in field clinical diagnostic testing; enabling faster and more accurate identification of pathogenic DNA in human blood or tissue samples. Copyright © 2015 Elsevier Inc. All rights reserved.