Applying Supercomputing Methods to Multicriteria R&D Planning Problems
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Supercomputing Research Development↗
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SciDAC ISEP: Integrated Simulation of Energetic Particles in Burning Plasmas
The objective of the SciDAC Center for Integrated Simulation of Energetic Particles in Burning Plasmas (ISEP) is to improve physics understanding of energetic particle (EP) confinement and EP interactions with burning thermal plasmas through large-scale simulations. The ISEP center will develop a multiscale and multiphysics ISEP framework for a predictive capability of EP physics and deliver an EP module incorporating both first-principles simulations and high fidelity reduced transport models to the fusion whole device modeling (WDM) project. The ISEP framework will enable us to perform long time, global kinetic simulations of EP physics in burning plasmas, by utilizing the full power of the next generation supercomputers. Our research and development activities will build on fruitful collaborations with computer scientists and applied mathematicians to offer enabling technologies for performance scalability, portability, solvers, coupling for integration with the fusion WDM project, and long-term preservation of data.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Status and projections of the NAS program
NASA's Numerical Aerodynamic Simulation (NAS) Program has completed development of the initial operating configuration of the NAS Processing System Network (NPSN). This is the first milestone in the continuing and pathfinding effort to provide state-of-the-art supercomputing for aeronautics research and development. The NPSN, available to a nation-wide community of remote users, provides a uniform UNIX environment over a network of host computers ranging from the Cray-2 supercomputer to advanced scientific workstations. This system, coupled with a vendor-independent base of common user interface and network software, presents a new paradigm for supercomputing environments. Background leading to the NAS program, its programmatic goals and strategies, technical goals and objectives, and the development activities leading to the current NPSN configuration are presented. Program status, near-term plans, and plans for the next major milestone, the extended operating configuration, are also discussed.
Bailey, Frank R.↗
Status and projections of the NAS Program
NASA's Numerical Aerodynamic Simulation (NAS) Program has completed development of the initial operating configuration of the NAS Processing System Network (NPSN). This is the first milestone in the continuing and pathfinding effort to provide state-of-the-art supercomputing for aeronautics research and development. The NPSN, available to a nation-wide community of remote users, provides a uniform UNIX environment over a network of host computers ranging from the new Cray-2 supercomputer to advanced scientific workstations. This system, coupled with a vendor-independent base of common user interface and network software, presents a new paradigm for supercomputing environments. Presented here is the background leading to the NAS Program, its programmatic goals and strategies, technical goals and objectives, and the development activities leading to the current NPSN configuration. Program status, near-term plans and plans for the next major milestone, the extended operating configuration, are also discussed.
Bailey, F. R.↗
A Quantitative Study of Deep Learning Training on Heterogeneous Supercomputers
Deep learning (DL) has become a key technique for solving complex problems in scientific research and discovery. DL training for science is substantially challenging because it has to deal with massive quantities of multi-dimensional data. High- performance computing (HPC) supercomputers are increasingly being employed for meeting the exponentially growing demand for DL. Multiple GPUs and high-speed interconnect network are needed for supporting DL on HPC systems. However, the excessive use of GPUs without considering effective benefits leads to inefficient resource utilization of these expensive setups. In this paper, we conduct a quantitative analysis to gauge the efficacy of DL workloads on the latest HPC system and identify viability of next-generation DL-optimized heterogeneous supercomputers for enabling researchers to develop more efficient resource management and distributed DL middleware. We evaluate well- known DL models with large-scale datasets using the popular TensorFlow framework, and provide a thorough evaluation in- cluding scalability, accuracy, variability, storage resource, GPU- GPU/GPU-CPU data transfer, and GPU utilization. Our analysis reveals that the latest heterogeneous supercomputing cluster shows varying performance trend as compared to the existing literature for single- and multi-node training. To the best of our knowledge, this is the first work to conduct such a quantitative and comprehensive study of DL training on a supercomputing system with multiple GPUs.
Han, Jingoo↗
Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale
This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.
Cyber security, Tensor decomposition, graph analyt↗
Computational fluid dynamics
An overview of computational fluid dynamics (CFD) activities at the Langley Research Center is given. The role of supercomputers in CFD research, algorithm development, multigrid approaches to computational fluid flows, aerodynamics computer programs, computational grid generation, turbulence research, and studies of rarefied gas flows are among the topics that are briefly surveyed.
Source record↗
Energy Efficient Supercomputing
Katie Anypas, Head of NERSC's Services Department discusses the Lab's research into developing increasingly powerful and energy efficient supercomputers at our '8 Big Ideas' Science at the Theater event on October 8th, 2014, in Oakland, California.
97 MATHEMATICS AND COMPUTING↗
Tera Scale Systems and Applications
This presentation discusses NASA's efforts to develop tera scale systems designed to push the envelope of supercomputing research. Topics cover include: NASA's existing supercomputing facilities and capabilities, NASA's computational challenges in developing these systems, development of production supercomputer, and potential research projects which could benefit from these types of systems.
Niggley, Chuck↗
The impact of CFD on development test facilities - A National Research Council projection
The results of a National Research Council study on the effect that advances in computational fluid dynamics (CFD) will have on conventional aeronautical ground testing are reported. Current CFD capabilities include the depiction of linearized inviscid flows and a boundary layer, initial use of Euler coordinates using supercomputers to automatically generate a grid, research and development on Reynolds-averaged Navier-Stokes (N-S) equations, and preliminary research on solutions to the full N-S equations. Improvements in the range of CFD usage is dependent on the development of more powerful supercomputers, exceeding even the projected abilities of the NASA Numerical Aerodynamic Simulator (1 BFLOP/sec). Full representation of the Re-averaged N-S equations will require over one million grid points, a computing level predicted to be available in 15 yr. Present capabilities allow identification of data anomalies, confirmation of data accuracy, and adequateness of model design in wind tunnel trials. Account can be taken of the wall effects and the Re in any flight regime during simulation. CFD can actually be more accurate than instrumented tests, since all points in a flow can be modeled with CFD, while they cannot all be monitored with instrumentation in a wind tunnel.
Korkegi, R. H.↗
Color graphics, interactive processing, and the supercomputer
The development of a common graphics environment for the NASA Langley Research Center user community and the integration of a supercomputer into this environment is examined. The initial computer hardware, the software graphics packages, and their configurations are described. The addition of improved computer graphics capability to the supercomputer, and the utilization of the graphic software and hardware are discussed. Consideration is given to the interactive processing system which supports the computer in an interactive debugging, processing, and graphics environment.
Smith-Taylor, Rudeen↗
NAS-current status and future plans
The Numerical Aerodynamic Simulation (NAS) has met its first major milestone, the NAS Processing System Network (NPSN) Initial Operating Configuration (IOC). The program has met its goal of providing a national supercomputer facility capable of greatly enhancing the Nation's research and development efforts. Furthermore, the program is fulfilling its pathfinder role by defining and implementing a paradigm for supercomputing system environments. The IOC is only the begining and the NAS Program will aggressively continue to develop and implement emerging supercomputer, communications, storage, and software technologies to strengthen computations as a critical element in supporting the Nation's leadership role in aeronautics.
Bailey, F. R.↗
Scaling of data communications for an advanced supercomputer network
The goal of NASA's Numerical Aerodynamic Simulation (NAS) Program is to provide a powerful computational environment for advanced research and development in aeronautics and related disciplines. The present NAS system consists of a Cray 2 supercomputer connected by a data network to a large mass storage system, to sophisticated local graphics workstations and by remote communication to researchers throughout the United States. The program plan is to continue acquiring the most powerful supercomputers as they become available. The implications of a projected 20-fold increase in processing power on the data communications requirements are described.
Levin, E.↗
Application of technology developed for flight simulation at NASA. Langley Research Center
In order to meet the stringent time-critical requirements for real-time man-in-the-loop flight simulation, computer processing operations including mathematical model computation and data input/output to the simulators must be deterministic and be completed in as short a time as possible. Personnel at NASA's Langley Research Center are currently developing the use of supercomputers for simulation mathematical model computation for real-time simulation. This, coupled with the use of an open systems software architecture, will advance the state-of-the-art in real-time flight simulation.
Cleveland, Jeff I., II↗
LLNL Partners with IBM on Brain-Like Computing Chip
Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips.
97 MATHEMATICS AND COMPUTING↗
Antenna pattern control using impedance surfaces
During this research period, we have effectively transferred existing computer codes from CRAY supercomputer to work station based systems. The work station based version of our code preserved the accuracy of the numerical computations while giving a much better turn-around time than the CRAY supercomputer. Such a task relieved us of the heavy dependence of the supercomputer account budget and made codes developed in this research project more feasible for applications. The analysis of pyramidal horns with impedance surfaces was our major focus during this research period. Three different modeling algorithms in analyzing lossy impedance surfaces were investigated and compared with measured data. Through this investigation, we discovered that a hybrid Fourier transform technique, which uses the eigen mode in the stepped waveguide section and the Fourier transformed field distributions across the stepped discontinuities for lossy impedances coating, gives a better accuracy in analyzing lossy coatings. After a further refinement of the present technique, we will perform an accurate radiation pattern synthesis in the coming reporting period.
Balanis, Constantine A.↗
University of Colorado - Center for Research and Education in Wind (CREW): Cooperative Research and Development Final Report, CRADA Number CRD-11-446
Enabled by petascale supercomputing, the next generation of computer models for wind energy will simulate a vast range of scales and physics, spanning from turbine structural dynamics and blade-scale turbulence to mesoscale atmospheric flow. A single model covering all scales and physics is not feasible. Thus, these simulations will require the coupling of different models/codes, each for different physics, interacting at their domain boundaries.
17 WIND ENERGY↗
Cancer's Big Data Problem
The US Department of Energy (DOE) has partnered with the National Cancer Institute (NCI) to use DOE supercomputers to aid in the fight against cancer by building sophisticated models based on data available at the population, patient, and molecular levels. Here, through a three-year pilot project called the Joint Design of Advanced Computing Solutions for Cancer (JDACSC), four participating national laboratories--Argonne, Lawrence Livermore, Los Alamos, and Oak Ridge--will focus on three problems singled out by the NCI as the biggest bottlenecks to advancing cancer research.
60 APPLIED LIFE SCIENCES↗