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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Visualization of unsteady computational fluid dynamics

A brief summary of the computer environment used for calculating three dimensional unsteady Computational Fluid Dynamic (CFD) results is presented. This environment requires a super computer as well as massively parallel processors (MPP's) and clusters of workstations acting as a single MPP (by concurrently working on the same task) provide the required computational bandwidth for CFD calculations of transient problems. The cluster of reduced instruction set computers (RISC) is a recent advent based on the low cost and high performance that workstation vendors provide. The cluster, with the proper software can act as a multiple instruction/multiple data (MIMD) machine. A new set of software tools is being designed specifically to address visualizing 3D unsteady CFD results in these environments. Three user's manuals for the parallel version of Visual3, pV3, revision 1.00 make up the bulk of this report.

Haimes, Robert↗
NASA NTRS2016

Computational Modeling to Limit the Impact Displays and Indicator Lights Have on Habitable Volume Operational Lighting Constraints

The goal of this investigation is to determine design limitations and architectural solutions that limit the impact light from displays and indicator lamps have on the operational environment task lighting and lighting countermeasure spectrum constraints. It is concerning that this innovative architectural lighting system, could be compromised by spectrums from display systems, architectural materials, and structures that are not considered as part a full system design implementation. The introduction of many Commercial Off the Shelf (COTS) products to the spacecraft volume that contain LEDs, without consideration to the human factors and biological constraints, is another problem. Displays and indicators are a necessary part of the spacecraft and it is the goal of this research project to determine constraints and solutions that allow these systems to be integrated while minimizing how the lighting environment is modified by them. Due to the potentially broad scope of this endeavor, the project team developed constraints for the evaluation. The evaluation will be on a set of tasks that required significant exposure in the same environment while having a large chance of impacting the light spectrum the crew is expected to receive from the architectural lighting system. The team plans to use recent HRP research on "Net Habitable Volume" [1] to provide the boundary conditions for volume size. A Zemax ® lighting model was developed of a small enclosure that had high intensity overhead lighting and a standard intensity display with LED indicator arrays. The computer model demonstrated a work surface illuminated at a high level by the overhead light source compared to displays and indicators whose light is parallel to the work plane. The overhead lighting oversaturated spectral contributions from the display and indicator at the task work surface. Interestingly, when the observer looked at the displays and LEDs within the small enclosure, their spectral contribution was significant but could be reduced by reflecting overhead light from the wall(s) to the observer. Direct observation of displays and LEDs are an issue because the user's viewing area is a display, not an illuminated work surface. Since avionics command centers consume significant crew time, the tasks that seemed at higher risk for unwanted spectral contributions as an operational volume with significant quantity of displays and indicators that were either under direct observation of the crew or impacting a volume the crew may be required to sleep in.

Clark, T. E.↗
DOE OSTI2020

Layer-Parallel Training of Deep Residual Neural Networks

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Finally, using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Aeroflex Technology as Class-Y Demonstrator

Modern space field programmable gate array (FPGA) devices with increased functional density and operational frequency, such as Xilinx Virtex 4 (V4) and S (V5), are packaged in non-hermetic ceramic flip chip forms. These next generation space parts were not qualified to the MIL-PRF-38535 Qualified Manufacturer Listing (QML) class-V when they were released because class-V was only intended for hermetic parts. In order to bring Xilinx V5 type packages into the QML system, it was suggested that class-Y be set up as a new category. From 2010 through 2014, a JEDEC G12 task group developed screening and qualification requirements for Class-Y products. The Document Standardization Division of the Defense Logistics Agency (DLA) has completed an engineering practice study. In parallel with the class-Y efforts, the NASA Electronic Parts and Packaging (NEPP) program has funded JPL to study potential reliability issues of the class-Y products. The major hurdle of this task was the absence of adequate research samples. Figure 1-1 shows schematic diagrams of typical structures of class-Y type products. Typically, class-Y products are either in ceramic flip chip column grid array (CGA) or land grid array (LGA) form. In class-Y packages, underfill and heat spread adhesive materials are directly exposed to the spacecraft environment due to their non-hermeticity. One of the concerns originally raised was that the underfill material could degrade due to the spacecraft environment and negatively impact the reliability of the package. In order to study such issues, it was necessary to use ceramic daisy chain flip chip package samples so that continuity of flip chip solder bumps could be monitored during the reliability tests. However, none of the commercially available class-Y daisy chain parts had electrical connections through flip chip solder bumps; only solder columns were daisy chained, which made it impossible to test continuity of flip chip solder bumps without using extremely costly functional parts. Among space parts manufacturers who were interested in producing class-Y products, Aeroflex Microelectronic Solutions-HiRel had been developing assembly processes using their internal R&D classy type samples. In early 2012, JPL and Aeroflex initiated a collaboration to study reliability of the Aeroflex technology as a class-Y demonstrator.

Suh, Jong-ook↗
NASA NTRS1985

Research in computer science

Synopses are given for NASA supported work in computer science at the University of Virginia. Some areas of research include: error seeding as a testing method; knowledge representation for engineering design; analysis of faults in a multi-version software experiment; implementation of a parallel programming environment; two computer graphics systems for visualization of pressure distribution and convective density particles; task decomposition for multiple robot arms; vectorized incomplete conjugate gradient; and iterative methods for solving linear equations on the Flex/32.

Ortega, J. M.↗
NASA NTRS1991

T on the Cray X/MP

This research was aimed at porting the T programming language, a dialect of LISP, to the Cray X/MP computer. The effort involved research into systems engineering and software engineering problems related not only to compiler design and implementation, but also to parallel computation, the X/MP being a shared-memory multiprocessor. Porting also involved the sub-tasks of retargeting the assembler, code-generator, and run-time system. A reasonably effective porting methodology that is relatively straight forward, although more difficult than table-driven approaches, has been developed. The run-time system was not ported, and thus the T programs cannot be run without explicit linking of the required run-time support. The run-time performance gain achieved in generated code was found to be disappointing. Recommendations for the achievement of higher performance are given.

Hudak, Paul↗
NASA NTRS1993

Linear Static Structural and Vibration Analysis on High-Performance Computers

Parallel computers offer the opportunity to significantly reduce the computation time necessary to analyze large-scale aerospace structures. This paper presents algorithms developed for and implemented on a massively-parallel computers hereafter referred to as Scalable High Performance Computers (SHPC) for the most computationally intensive tasks involved in structural analysis, namely, generation and assembly of system matrices, solution of systems of equations and calculation of the eigenvalues and eigenvectors. Results on SHPC are presented for large-scale structural problems (i.e. Models of high speed civil transport). The goal of this research is to develop new efficient technique which extend structural analysis to SHPC and make large-scale structural analyses tractable.

Baddourah, Majdi↗
NASA NTRS1995

Learning in Neural Networks: VLSI Implementation Strategies

Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

Neural Networks↗
NASA NTRS1993

A new conformal absorbing boundary condition for finite element meshes and parallelization of FEMATS

Some of the progress toward the development and parallelization of an improved version of the finite element code FEMATS is described. This is a finite element code for computing the scattering by arbitrarily shaped three dimensional surfaces composite scatterers. The following tasks were worked on during the report period: (1) new absorbing boundary conditions (ABC's) for truncating the finite element mesh; (2) mixed mesh termination schemes; (3) hierarchical elements and multigridding; (4) parallelization; and (5) various modeling enhancements (antenna feeds, anisotropy, and higher order GIBC).

Chatterjee, A.↗
NASA NTRS2002

Gilgamesh: A Multithreaded Processor-In-Memory Architecture for Petaflops Computing

Processor-in-Memory (PIM) architectures avoid the von Neumann bottleneck in conventional machines by integrating high-density DRAM and CMOS logic on the same chip. Parallel systems based on this new technology are expected to provide higher scalability, adaptability, robustness, fault tolerance and lower power consumption than current MPPs or commodity clusters. In this paper we describe the design of Gilgamesh, a PIM-based massively parallel architecture, and elements of its execution model. Gilgamesh extends existing PIM capabilities by incorporating advanced mechanisms for virtualizing tasks and data and providing adaptive resource management for load balancing and latency tolerance. The Gilgamesh execution model is based on macroservers, a middleware layer which supports object-based runtime management of data and threads allowing explicit and dynamic control of locality and load balancing. The paper concludes with a discussion of related research activities and an outlook to future work.

management locality load balance↗
NASA NTRS1995

The Application of a Massively Parallel Computer to the Simulation of Electrical Wave Propagation Phenomena in the Heart Muscle Using Simplified Models

The simulation of heart arrhythmia and fibrillation are very important and challenging tasks. The solution of these problems using sophisticated mathematical models is beyond the capabilities of modern super computers. To overcome these difficulties it is proposed to break the whole simulation problem into two tightly coupled stages: generation of the action potential using sophisticated models. and propagation of the action potential using simplified models. The well known simplified models are compared and modified to bring the rate of depolarization and action potential duration restitution closer to reality. The modified method of lines is used to parallelize the computational process. The conditions for the appearance of 2D spiral waves after the application of a premature beat and the subsequent traveling of the spiral wave inside the simulated tissue are studied.

Karpoukhin, Mikhii G.↗
NASA NTRS1999

Computing with Beowulf

Parallel computers built out of mass-market parts are cost-effectively performing data processing and simulation tasks. The Supercomputing (now known as "SC") series of conferences celebrated its 10th anniversary last November. While vendors have come and gone, the dominant paradigm for tackling big problems still is a shared-resource, commercial supercomputer. Growing numbers of users needing a cheaper or dedicated-access alternative are building their own supercomputers out of mass-market parts. Such machines are generally called Beowulf-class systems after the 11th century epic. This modern-day Beowulf story began in 1994 at NASA's Goddard Space Flight Center. A laboratory for the Earth and space sciences, computing managers there threw down a gauntlet to develop a $50,000 gigaFLOPS workstation for processing satellite data sets. Soon, Thomas Sterling and Don Becker were working on the Beowulf concept at the University Space Research Association (USRA)-run Center of Excellence in Space Data and Information Sciences (CESDIS). Beowulf clusters mix three primary ingredients: commodity personal computers or workstations, low-cost Ethernet networks, and the open-source Linux operating system. One of the larger Beowulfs is Goddard's Highly-parallel Integrated Virtual Environment, or HIVE for short.

Cohen, Jarrett↗
NASA NTRS2001

Parallel Programming Strategies for Irregular Adaptive Applications

Achieving scalable performance for dynamic irregular applications is eminently challenging. Traditional message-passing approaches have been making steady progress towards this goal; however, they suffer from complex implementation requirements. The use of a global address space greatly simplifies the programming task, but can degrade the performance for such computations. In this work, we examine two typical irregular adaptive applications, Dynamic Remeshing and N-Body, under competing programming methodologies and across various parallel architectures. The Dynamic Remeshing application simulates flow over an airfoil, and refines localized regions of the underlying unstructured mesh. The N-Body experiment models two neighboring Plummer galaxies that are about to undergo a merger. Both problems demonstrate dramatic changes in processor workloads and interprocessor communication with time; thus, dynamic load balancing is a required component.

Biswas, Rupak↗
NASA NTRS2005

A Parallel Processing Algorithm for Remote Sensing Classification

A current thread in parallel computation is the use of cluster computers created by networking a few to thousands of commodity general-purpose workstation-level commuters using the Linux operating system. For example on the Medusa cluster at NASA/GSFC, this provides for super computing performance, 130 G(sub flops) (Linpack Benchmark) at moderate cost, $370K. However, to be useful for scientific computing in the area of Earth science, issues of ease of programming, access to existing scientific libraries, and portability of existing code need to be considered. In this paper, I address these issues in the context of tools for rendering earth science remote sensing data into useful products. In particular, I focus on a problem that can be decomposed into a set of independent tasks, which on a serial computer would be performed sequentially, but with a cluster computer can be performed in parallel, giving an obvious speedup. To make the ideas concrete, I consider the problem of classifying hyperspectral imagery where some ground truth is available to train the classifier. In particular I will use the Support Vector Machine (SVM) approach as applied to hyperspectral imagery. The approach will be to introduce notions about parallel computation and then to restrict the development to the SVM problem. Pseudocode (an outline of the computation) will be described and then details specific to the implementation will be given. Then timing results will be reported to show what speedups are possible using parallel computation. The paper will close with a discussion of the results.

Gualtieri, J. Anthony↗
DOE OSTI2023

Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific Computing and Machine Learning

We present Adrastea, an efficient FPGA design environment for developing scientific machine learning applications. FPGA development is challenging, from deployment, proper toolchain setup, programming methods, interfacing FPGA kernels, and more importantly, the need to explore design space choices to get the best performance and area usage from the FPGA kernel design. Adrastea provides an automated and scalable design flow to parameterize, implement, and optimize complex FPGA kernels and associated interfaces. We show how virtualization of the development environment via virtual machines is leveraged to simplify the setup of the FPGA toolchain while deploying the FPGA boards and while scaling up the automated design space exploration to leverage multiple machines concurrently. Adrastea provides an automated build and test environment of FPGA kernels. By exposing design space hyper-parameters, Adrastea can automatically search the design space in parallel to optimize the FPGA design for a given metric, usually performance or area. Adrastea simplifies the task of interfacing with the FPGA kernels with a simplified interface API. To demonstrate the capabilities of Adrastea, we implement a complex random forest machine learning kernel with 10,000 input features while achieving extremely low computing latency without loss of prediction accuracy, which is required by a scientific edge application at SNS. We also demonstrate Adrastea using an FFT kernel and show that for both applications Adrastea is able to systematically and efficiently evaluate different design options, which reduced the time and effort required to develop the kernel from months of manual work to days of automatic builds.

Young, Aaron↗
DOE OSTI2025

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗
DOE OSTI2020

GridOPTICS/GridPACK

GridPACK is a software framework consisting of a set of modules designed to simplify the development of programs that model the power grid and run on parallel, high performance computing platforms. It also contains several fully developed applications, including powerflow, dynamic simulation, state estimation, Kalman filter analysis (dynamic state estimation), contingency analysis and real time path rating. These applications can be used either standalone or as components in more complicated workflows that combine several different types of application together. The framework modules are available as a combination of libraries and software templates and consist of components for setting up and distributing power grid networks, support for modeling the behavior of individual buses and branches in the network, converting the network models to the corresponding algebraic equations, and parallel routines for manipulating and solving large algebraic systems. The framework also contains a module for distributing tasks evenly amongst computing resources, even if individual tasks vary widely in their execution times. Additional modules support input and output, basic statistical analysis of contingency based calculations, distributed data structures, as well as basic profiling and error management.

Palmer, Bruce↗
NASA NTRS1993

Linear static structural and vibration analysis on high-performance computers

Parallel computers offer the oppurtunity to significantly reduce the computation time necessary to analyze large-scale aerospace structures. This paper presents algorithms developed for and implemented on massively-parallel computers hereafter referred to as Scalable High-Performance Computers (SHPC), for the most computationally intensive tasks involved in structural analysis, namely, generation and assembly of system matrices, solution of systems of equations and calculation of the eigenvalues and eigenvectors. Results on SHPC are presented for large-scale structural problems (i.e. models for High-Speed Civil Transport). The goal of this research is to develop a new, efficient technique which extends structural analysis to SHPC and makes large-scale structural analyses tractable.

Baddourah, M. A.↗