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NASA NTRS2004

A Performance Evaluation of the Cray X1 for Scientific Applications

The last decade has witnessed a rapid proliferation of superscalar cache-based microprocessors to build high-end capability and cost effectiveness. However, the recent development of massively parallel vector systems is having a significant effect on the supercomputing landscape. In this paper, we compare the performance of the recently released Cray X1 vector system with that of the cacheless NEC SX-6 vector machine, and the superscalar cache-based IBM Power3 and Power4 architectures for scientific applications. Overall results demonstrate that the X1 is quite promising, but performance improvements are expected as the hardware, systems software, and numerical libraries mature. Code reengineering to effectively utilize the complex architecture may also lead to significant efficiency enhancements.

Oliker, Leonid↗

NASA NTRS1998

Geophysics of Small Planetary Bodies

As a SETI Institute PI from 1996-1998, Erik Asphaug studied impact and tidal physics and other geophysical processes associated with small (low-gravity) planetary bodies. This work included: a numerical impact simulation linking basaltic achondrite meteorites to asteroid 4 Vesta (Asphaug 1997), which laid the groundwork for an ongoing study of Martian meteorite ejection; cratering and catastrophic evolution of small bodies (with implications for their internal structure; Asphaug et al. 1996); genesis of grooved and degraded terrains in response to impact; maturation of regolith (Asphaug et al. 1997a); and the variation of crater outcome with impact angle, speed, and target structure. Research of impacts into porous, layered and prefractured targets (Asphaug et al. 1997b, 1998a) showed how shape, rheology and structure dramatically affects sizes and velocities of ejecta, and the survivability and impact-modification of comets and asteroids (Asphaug et al. 1998a). As an affiliate of the Galileo SSI Team, the PI studied problems related to cratering, tectonics, and regolith evolution, including an estimate of the impactor flux around Jupiter and the effect of impact on local and regional tectonics (Asphaug et al. 1998b). Other research included tidal breakup modeling (Asphaug and Benz 1996; Schenk et al. 1996), which is leading to a general understanding of the role of tides in planetesimal evolution. As a Guest Computational Investigator for NASA's BPCC/ESS supercomputer testbed, helped graft SPH3D onto an existing tree code tuned for the massively parallel Cray T3E (Olson and Asphaug, in preparation), obtaining a factor xIO00 speedup in code execution time (on 512 cpus). Runs which once took months are now completed in hours.

Asphaug, Erik I.↗

NASA NTRS1988

Explicit and implicit solution of the Navier-Stokes equations on a massively parallel computer

The design, implementation, and performance of a two-dimensional time-accurate Navier-Stokes solver for the CM2 supercomputer are described. The program uses a single processor for each grid point. Two different time-stepping methods have so far been implemented: an explicit third-order Runge-Kutta method and an implicit approximation-factorization method. The CM2 results are checked against those of a mature well-vectorized Cray 2 program, both for correctness and performance. The code is found to be correct, and the performance in some cases is up to several times that of the Cray 2.

Levit, Creon↗

NASA NTRS1994

Performance analysis of three dimensional integral equation computations on a massively parallel computer

The purpose of this study is to investigate the performance of the integral equation computations using numerical source field-panel method in a massively parallel processing (MPP) environment. A comparative study of computational performance of the MPP CM-5 computer and conventional Cray-YMP supercomputer for a three-dimensional flow problem is made. A serial FORTRAN code is converted into a parallel CM-FORTRAN code. Some performance results are obtained on CM-5 with 32, 62, 128 nodes along with those on Cray-YMP with a single processor. The comparison of the performance indicates that the parallel CM-FORTRAN code near or out-performs the equivalent serial FORTRAN code for some cases.

Logan, Terry G.↗

NASA NTRS1997

Implementation of Helioseismic Data Reduction and Diagnostic Techniques on Massively Parallel Architectures

Under the direction of Dr. Rhodes, and the technical supervision of Dr. Korzennik, the data assimilation of high spatial resolution solar dopplergrams has been carried out throughout the program on the Intel Delta Touchstone supercomputer. With the help of a research assistant, partially supported by this grant, and under the supervision of Dr. Korzennik, code development was carried out at SAO, using various available resources. To ensure cross-platform portability, PVM was selected as the message passing library. A parallel implementation of power spectra computation for helioseismology data reduction, using PVM was successfully completed. It was successfully ported to SMP architectures (i.e. SUN), and to some MPP architectures (i.e. the CM5). Due to limitation of the implementation of PVM on the Cray T3D, the port to that architecture was not completed at the time.

Korzennik, Sylvain↗

NASA NTRS1994

Performance of BLAS 3, FFTs and NAS Parallel Benchmarks on Cray T3D

Recently, a Cray T3D Emulator has been made available on the Cray Y-MP and C90 computers. The Pittsburgh Supercomputer Center has acquired a CRAY T3D system and many other centers like Jet Propulsion Laboratory (JPL) will have it by the end of 1994. The Cray T3D system is the firstphase system in Cray Research, Inc.'s (CRI) three-phase massively parallel processing (MPP) program. This system features a heterogeneous architecture that closely couples DEC's ALPHA microprocessors and CRI's parallel-vector technology, i.e. the Cray Y-MP and Cray C90. The Cray T3D Emulator will give prospective users a valuable experience in developing high performance applications on the MPP system. This emulator runs programs written in CRI's MPP Fortran programming model (data sharing and work sharing) or Parallel Virtual Machine (PVM) programming model. It will help the users to study data layout, data locality, and data reference patterns thereby providing feedback which will enable one to write more efficient parallel codes. An overview of the Cray T3D hardware, software, and three of its available programming models is presented.The Cray Fortran Programming Model comprising (a) Data Sharing, (b) Worksharing and (c) Message Passing, will be discussed with examples. We have also implemented distributed BLAS 3 (matrix-matrix multiplication) in data parallel model (using only CSHIFT); worksharing model using block distribution and collapsed distribution; and message passing model using PVM. We have also implemented 2D and 3D FFTs for radix-2 using PVM. The performance of NAS Parallel 'Benchmarks (NPB) on CRAY T3D will be compared with other highly parallel systems such as CM-5, Paragon, C90 etc.

Saini, Subhash↗

NASA NTRS1993

Computational mechanics analysis tools for parallel-vector supercomputers

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigensolution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization search analysis and domain decomposition. The source code for many of these algorithms is available.

Storaasli, Olaf O.↗

NASA NTRS1993

Computational mechanics analysis tools for parallel-vector supercomputers

Computational algorithms for structural analysis on parallel-vector supercomputers are reviewed. These parallel algorithms, developed by the authors, are for the assembly of structural equations, 'out-of-core' strategies for linear equation solution, massively distributed-memory equation solution, unsymmetric equation solution, general eigen-solution, geometrically nonlinear finite element analysis, design sensitivity analysis for structural dynamics, optimization algorithm and domain decomposition. The source code for many of these algorithms is available from NASA Langley.

Storaasli, O. O.↗

NASA NTRS1987

Chemical network problems solved on NASA/Goddard's massively parallel processor computer

The single instruction stream, multiple data stream Massively Parallel Processor (MPP) unit consists of 16,384 bit serial arithmetic processors configured as a 128 x 128 array whose speed can exceed that of current supercomputers (Cyber 205). The applicability of the MPP for solving reaction network problems is presented and discussed, including the mapping of the calculation to the architecture, and CPU timing comparisons.

Cho, Seog Y.↗

NASA NTRS2006

Implementing Scientific Simulation Codes Highly Tailored for Vector Architectures Using Custom Configurable Computing Machines

The motivation for this work comes from an observation that amidst the push for Massively Parallel (MP) solutions to high-end computing problems such as numerical physical simulations, large amounts of legacy code exist that are highly optimized for vector supercomputers. Because re-hosting legacy code often requires a complete re-write of the original code, which can be a very long and expensive effort, this work examines the potential to exploit reconfigurable computing machines in place of a vector supercomputer to implement an essentially unmodified legacy source code. Custom and reconfigurable computing resources could be used to emulate an original application's target platform to the extent required to achieve high performance. To arrive at an architecture that delivers the desired performance subject to limited resources involves solving a multi-variable optimization problem with constraints. Prior research in the area of reconfigurable computing has demonstrated that designing an optimum hardware implementation of a given application under hardware resource constraints is an NP-complete problem. The premise of the approach is that the general issue of applying reconfigurable computing resources to the implementation of an application, maximizing the performance of the computation subject to physical resource constraints, can be made a tractable problem by assuming a computational paradigm, such as vector processing. This research contributes a formulation of the problem and a methodology to design a reconfigurable vector processing implementation of a given application that satisfies a performance metric. A generic, parametric, architectural framework for vector processing implemented in reconfigurable logic is developed as a target for a scheduling/mapping algorithm that maps an input computation to a given instance of the architecture. This algorithm is integrated with an optimization framework to arrive at a specification of the architecture parameters that attempts to minimize execution time, while staying within resource constraints. The flexibility of using a custom reconfigurable implementation is exploited in a unique manner to leverage the lessons learned in vector supercomputer development. The vector processing framework is tailored to the application, with variable parameters that are fixed in traditional vector processing. Benchmark data that demonstrates the functionality and utility of the approach is presented. The benchmark data includes an identified bottleneck in a real case study example vector code, the NASA Langley Terminal Area Simulation System (TASS) application.

Rutishauser, David↗

NASA NTRS2012

Graphics Processing Unit Assisted Thermographic Compositing

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often great, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques. Technical Methodology/Approach: Apply massively parallel algorithms and data structures to the specific analysis requirements presented when working with thermographic data sets.

Ragasa, Scott↗

NASA NTRS2013

Graphics Processing Unit Assisted Thermographic Compositing

Objective: To develop a software application utilizing general purpose graphics processing units (GPUs) for the analysis of large sets of thermographic data. Background: Over the past few years, an increasing effort among scientists and engineers to utilize the GPU in a more general purpose fashion is allowing for supercomputer level results at individual workstations. As data sets grow, the methods to work them grow at an equal, and often greater, pace. Certain common computations can take advantage of the massively parallel and optimized hardware constructs of the GPU to allow for throughput that was previously reserved for compute clusters. These common computations have high degrees of data parallelism, that is, they are the same computation applied to a large set of data where the result does not depend on other data elements. Signal (image) processing is one area were GPUs are being used to greatly increase the performance of certain algorithms and analysis techniques.

Ragasa, Scott↗

NASA NTRS1999

An Integrated Architecture for Onboard Spacecraft

As increasingly complex scientific and environmental observation spacecraft are deployed, the burden on the downlink assets, and ground-based systems complexity and cost is becoming a major problem. Already, the limitations of communications bandwidth and processing throughput limit the science data gathering, both in volume and in rate. This poses a dilemma to the scientist experimenter forcing choices between data collection and bandwidth/processing/archiving. Advances in ground based processing and space-to-Earth links have fallen behind the requirements for observation data, at increasing rates, over the last few decades. As NASA achieves its 40th anniversary, the ability to observe and capture phenomena of theoretical and practical interest to life on Earth far outstrips the ability to transfer, process, or store these data. NASA recognizes the need to invest on technological advancements that will enable both the space and ground systems to address the limitations. Spacecraft onboard computing power is a clear one. The capability of creating data products onboard the spacecraft adds a new level of flexibility to address the more demanding observation needs. Current spacecraft computing power is limited and incapable of addressing the needs of the new generation of observation satellites because extensive onboard data processing is required. Traditional spacecraft architectures only collect, package, and transmit to Earth the data acquired by multiple instruments. Conversely, the experience on developing ground data systems shows the need for high performance computing systems to process and create information from the instrumentation data. The expectation is that supercomputing technology is required to enable spacecraft to create information onboard. Moving supercomputing capability onboard spacecraft requires an approach that considers an integrated data architecture. Otherwise, it may simply convert a compute-bound problem into a communications bound problem, as has been shown numerous times in the context of massively parallel architectures. What is left to determine are the technologies that will enable spacecraft high performance computing.

Figueiredo, Marco A.↗

NASA NTRS1994

Performance Evaluation Methodologies and Tools for Massively Parallel Programs

The need for computing power has forced a migration from serial computation on a single processor to parallel processing on multiprocessors. However, without effective means to monitor (and analyze) program execution, tuning the performance of parallel programs becomes exponentially difficult as program complexity and machine size increase. The recent introduction of performance tuning tools from various supercomputer vendors (Intel's ParAide, TMC's PRISM, CSI'S Apprentice, and Convex's CXtrace) seems to indicate the maturity of performance tool technologies and vendors'/customers' recognition of their importance. However, a few important questions remain: What kind of performance bottlenecks can these tools detect (or correct)? How time consuming is the performance tuning process? What are some important technical issues that remain to be tackled in this area? This workshop reviews the fundamental concepts involved in analyzing and improving the performance of parallel and heterogeneous message-passing programs. Several alternative strategies will be contrasted, and for each we will describe how currently available tuning tools (e.g., AIMS, ParAide, PRISM, Apprentice, CXtrace, ATExpert, Pablo, IPS-2)) can be used to facilitate the process. We will characterize the effectiveness of the tools and methodologies based on actual user experiences at NASA Ames Research Center. Finally, we will discuss their limitations and outline recent approaches taken by vendors and the research community to address them.

Yan, Jerry C.↗

NASA NTRS1996

Fast I/O for Massively Parallel Applications

The two primary goals for this report were the design, contruction and modeling of parallel disk arrays for scientific visualization and animation, and a study of the IO requirements of highly parallel applications. In addition, further work in parallel display systems required to project and animate the very high-resolution frames resulting from our supercomputing simulations in ocean circulation and compressible gas dynamics.

OKeefe, Matthew T.↗

NASA NTRS1987

A study of the XY model by the Monte Carlo method

The massively parallel processor is used to perform Monte Carlo simulations for the two dimensional XY model on lattices of sizes up to 128 x 128. A parallel random number generator was constructed, finite size effects were studied, and run times were compared with those on a CRAY X-MP supercomputer.

Suranyi, Peter↗

NASA NTRS1987

The Massively Parallel Processor: Architecture and application

The massively parallel processor (MPP) has a simple, yet elegant architecture. Some have granted that it is novel, and other have claimed it to be fundamental. Whichever is the case, it has shown itself to be both useful and practical. Its hardware embodiment consumes relatively low power compared to other supercomputers, does not require special cooling, has an instruction cycle time that is relatively long, thus allowing it to avoid high speed technologies, and yet it competes respectably with any currently available supercomputer in speed of execution of applications. Due to the efficiency of the architecture, ease of construction and variety of applications, it is conceivable that within a few years computational accelerators more powerful than the MPP will be available at reasonable cost to be placed inside desktop workstations.

Dorband, John E.↗

NASA NTRS2001

Development and Applications of a Modular Parallel Process for Large Scale Fluid/Structures Problems

A modular process that can efficiently solve large scale multidisciplinary problems using massively parallel super computers is presented. The process integrates disciplines with diverse physical characteristics by retaining the efficiency of individual disciplines. Computational domain independence of individual disciplines is maintained using a meta programming approach. The process integrates disciplines without affecting the combined performance. Results are demonstrated for large scale aerospace problems on several supercomputers. The super scalability and portability of the approach is demonstrated on several parallel computers.

Guruswamy, Guru P.↗