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NASA NTRS1998

Genetically Engineered Microelectronic Infrared Filters

A genetic algorithm is used for design of infrared filters and in the understanding of the material structure of a resonant tunneling diode. These two components are examples of microdevices and nanodevices that can be numerically simulated using fundamental mathematical and physical models. Because the number of parameters that can be used in the design of one of these devices is large, and because experimental exploration of the design space is unfeasible, reliable software models integrated with global optimization methods are examined The genetic algorithm and engineering design codes have been implemented on massively parallel computers to exploit their high performance. Design results are presented for the infrared filter showing new and optimized device design. Results for nanodevices are presented in a companion paper at this workshop.

Cwik, Tom↗

NASA NTRS2004

Role of HPC in Advancing Computational Aeroelasticity

On behalf of the High Performance Computing and Modernization Program (HPCMP) and NASA Advanced Supercomputing Division (NAS) a study is conducted to assess the role of supercomputers on computational aeroelasticity of aerospace vehicles. The study is mostly based on the responses to a web based questionnaire that was designed to capture the nuances of high performance computational aeroelasticity, particularly on parallel computers. A procedure is presented to assign a fidelity-complexity index to each application. Case studies based on major applications using HPCMP resources are presented.

Guruswamy, Guru P.↗

NASA NTRS2012

Graphics Processing Unit Assisted Thermographic Compositing

Objective Develop a software application utilizing high performance computing techniques, including general purpose graphics processing units (GPGPUs), for the analysis and visualization of large thermographic data sets. Over the past several years, an increasing effort among scientists and engineers to utilize graphics processing units (GPUs) in a more general purpose fashion is allowing for previously unobtainable levels of computation by 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 which yield significant increases in performance. 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. Image processing is one area were GPUs are being used to greatly increase the performance of certain analysis and visualization techniques.

Ragasa, Scott↗

NASA NTRS1996

Cloud identification using genetic algorithms and massively parallel computation

As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user's manual was written and distributed nationwide to scientists whose work might benefit from its availability. Several papers, including two journal articles, were produced.

Buckles, Bill P.↗

NASA NTRS1987

The use of transputers in processing telemetry data

Parallelism will be an essential ingredient of high performance systems of the future. The Inmos transputer is a high performance single-chip computer whose architecture facilitates the construction of parallel processing systems. Occam is a high level language developed for use with the Inmos transputer. This paper describes a project to evaluate the feasibility of using the transputer to implement real time processing of telemetry data.

Delgado, Hugo M., Jr.↗

NASA NTRS2017

pFlogger: The Parallel Fortran Logging Utility

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger)' similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Clune, Tom↗

NASA NTRS1991

A parallel-vector Lanczos eigen-solver for structural vibration problems

The Lanczos algorithm for the solution of generalized eigen-problem has been receiving a lot of attention in recent years due to its computational efficiency. The focus of this paper is to develop a Lanczos algorithm which can exploit both the parallel and vector capabilities provided by modern high-performance computers. A partial restoring orthogonality scheme is also developed and incorporated into the basic Lanczos algorithm. The numerical performance in terms of accuracy and efficiency of the proposed parallel-vector Lanczos algorithm is demonstrated by solving for the frequencies and mode shapes of structural problems on multiprocessor supercomputers.

Qin, Jiangning↗

NASA NTRS1989

CFD in design - A government perspective

Some of the research programs involving the use of CFD in the aerodynamic design process at government laboratories around the United States are presented. Technology transfer issues and future directions in the discipline or CFD are addressed. The major challengers in the aerosciences as well as other disciplines that will require high-performance computing resources such as massively parallel computers are examined.

Kutler, Paul↗

NASA NTRS1993

Three-Dimensional Magnetic-Bubble Memory System

Proposed magnetic-bubble memory system includes stack of two-dimensional memory modules. Design enables overall storage density of system to be greater than prior magnetic-bubble memories. Provides nonvolatile storage, with readout access times in millisecond range. Has partly parallel input/output configuration supporting high-performance computing. Also relatively invulnerable to damage by ionizing radiation. Binary data, as represented by direction of magnetization of magnetic bubbles, reads out via Faraday effect.

Katti, Romney R.↗

NASA NTRS1993

Evaluation of the Intel iWarp parallel processor for space flight applications

The potential of a DARPA-sponsored advanced processor, the Intel iWarp, for use in future SSF Data Management Systems (DMS) upgrades is evaluated through integration into the Ames DMS testbed and applications testing. The iWarp is a distributed, parallel computing system well suited for high performance computing applications such as matrix operations and image processing. The system architecture is modular, supports systolic and message-based computation, and is capable of providing massive computational power in a low-cost, low-power package. As a consequence, the iWarp offers significant potential for advanced space-based computing. This research seeks to determine the iWarp's suitability as a processing device for space missions. In particular, the project focuses on evaluating the ease of integrating the iWarp into the SSF DMS baseline architecture and the iWarp's ability to support computationally stressing applications representative of SSF tasks.

Hine, Butler P., III↗

NASA NTRS2008

Initial Performance Results on IBM POWER6

The POWER5+ processor has a faster memory bus than that of the previous generation POWER5 processor (533 MHz vs. 400 MHz), but the measured per-core memory bandwidth of the latter is better than that of the former (5.7 GB/s vs. 4.3 GB/s). The reason for this is that in the POWER5+, the two cores on the chip share the L2 cache, L3 cache and memory bus. The memory controller is also on the chip and is shared by the two cores. This serializes the path to memory. For consistently good performance on a wide range of applications, the performance of the processor, the memory subsystem, and the interconnects (both latency and bandwidth) should be balanced. Recognizing this, IBM has designed the Power6 processor so as to avoid the bottlenecks due to the L2 cache, memory controller and buffer chips of the POWER5+. Unlike the POWER5+, each core in the POWER6 has its own L2 cache (4 MB - double that of the Power5+), memory controller and buffer chips. Each core in the POWER6 runs at 4.7 GHz instead of 1.9 GHz in POWER5+. In this paper, we evaluate the performance of a dual-core Power6 based IBM p6-570 system, and we compare its performance with that of a dual-core Power5+ based IBM p575+ system. In this evaluation, we have used the High- Performance Computing Challenge (HPCC) benchmarks, NAS Parallel Benchmarks (NPB), and four real-world applications--three from computational fluid dynamics and one from climate modeling.

Saini, Subbash↗

NASA NTRS2007

A Queue Simulation Tool for a High Performance Scientific Computing Center

The NASA Center for Computational Sciences (NCCS) at the Goddard Space Flight Center provides high performance highly parallel processors, mass storage, and supporting infrastructure to a community of computational Earth and space scientists. Long running (days) and highly parallel (hundreds of CPUs) jobs are common in the workload. NCCS management structures batch queues and allocates resources to optimize system use and prioritize workloads. NCCS technical staff use a locally developed discrete event simulation tool to model the impacts of evolving workloads, potential system upgrades, alternative queue structures and resource allocation policies.

Spear, Carrie↗

NASA NTRS2006

Architecture Adaptive Computing Environment

Architecture Adaptive Computing Environment (aCe) is a software system that includes a language, compiler, and run-time library for parallel computing. aCe was developed to enable programmers to write programs, more easily than was previously possible, for a variety of parallel computing architectures. Heretofore, it has been perceived to be difficult to write parallel programs for parallel computers and more difficult to port the programs to different parallel computing architectures. In contrast, aCe is supportable on all high-performance computing architectures. Currently, it is supported on LINUX clusters. aCe uses parallel programming constructs that facilitate writing of parallel programs. Such constructs were used in single-instruction/multiple-data (SIMD) programming languages of the 1980s, including Parallel Pascal, Parallel Forth, C*, *LISP, and MasPar MPL. In aCe, these constructs are extended and implemented for both SIMD and multiple- instruction/multiple-data (MIMD) architectures. Two new constructs incorporated in aCe are those of (1) scalar and virtual variables and (2) pre-computed paths. The scalar-and-virtual-variables construct increases flexibility in optimizing memory utilization in various architectures. The pre-computed-paths construct enables the compiler to pre-compute part of a communication operation once, rather than computing it every time the communication operation is performed.

Dorband, John E.↗

NASA NTRS2003

yourSky: Custom Sky-Image Mosaics via the Internet

yourSky (http://yourSky.jpl.nasa.gov) is a computer program that supplies custom astronomical image mosaics of sky regions specified by requesters using client computers connected to the Internet. [yourSky is an upgraded version of the software reported in Software for Generating Mosaics of Astronomical Images (NPO-21121), NASA Tech Briefs, Vol. 25, No. 4 (April 2001), page 16a.] A requester no longer has to engage in the tedious process of determining what subset of images is needed, nor even to know how the images are indexed in image archives. Instead, in response to a requester s specification of the size and location of the sky area, (and optionally of the desired set and type of data, resolution, coordinate system, projection, and image format), yourSky automatically retrieves the component image data from archives totaling tens of terabytes stored on computer tape and disk drives at multiple sites and assembles the component images into a mosaic image by use of a high-performance parallel code. yourSky runs on the server computer where the mosaics are assembled. Because yourSky includes a Web-interface component, no special client software is needed: ordinary Web browser software is sufficient.

Jacob, Joseph↗

NASA NTRS1994

High performance computing applications in neurobiological research

The human nervous system is a massively parallel processor of information. The vast numbers of neurons, synapses and circuits is daunting to those seeking to understand the neural basis of consciousness and intellect. Pervading obstacles are lack of knowledge of the detailed, three-dimensional (3-D) organization of even a simple neural system and the paucity of large scale, biologically relevant computer simulations. We use high performance graphics workstations and supercomputers to study the 3-D organization of gravity sensors as a prototype architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scale-up, three-dimensional versions run on the Cray Y-MP and CM5 supercomputers.

Ross, Muriel D.↗

NASA NTRS1998

HPCC Methodologies for Structural Design and Analysis on Parallel and Distributed Computing Platforms

In this grant, we have proposed a three-year research effort focused on developing High Performance Computation and Communication (HPCC) methodologies for structural analysis on parallel processors and clusters of workstations, with emphasis on reducing the structural design cycle time. Besides consolidating and further improving the FETI solver technology to address plate and shell structures, we have proposed to tackle the following design related issues: (a) parallel coupling and assembly of independently designed and analyzed three-dimensional substructures with non-matching interfaces, (b) fast and smart parallel re-analysis of a given structure after it has undergone design modifications, (c) parallel evaluation of sensitivity operators (derivatives) for design optimization, and (d) fast parallel analysis of mildly nonlinear structures. While our proposal was accepted, support was provided only for one year.

Farhat, Charbel↗

NASA NTRS2017

Position Paper - pFLogger: The Parallel Fortran Logging framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or logger) similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Fortran↗

NASA NTRS2017

POSITION PAPER - pFLogger: The Parallel Fortran Logging Framework for HPC Applications

In the context of high performance computing (HPC), software investments in support of text-based diagnostics, which monitor a running application, are typically limited compared to those for other types of IO. Examples of such diagnostics include reiteration of configuration parameters, progress indicators, simple metrics (e.g., mass conservation, convergence of solvers, etc.), and timers. To some degree, this difference in priority is justifiable as other forms of output are the primary products of a scientific model and, due to their large data volume, much more likely to be a significant performance concern. In contrast, text-based diagnostic content is generally not shared beyond the individual or group running an application and is most often used to troubleshoot when something goes wrong. We suggest that a more systematic approach enabled by a logging facility (or 'logger') similar to those routinely used by many communities would provide significant value to complex scientific applications. In the context of high-performance computing, an appropriate logger would provide specialized support for distributed and shared-memory parallelism and have low performance overhead. In this paper, we present our prototype implementation of pFlogger - a parallel Fortran-based logging framework, and assess its suitability for use in a complex scientific application.

Clune, Thomas L.↗