Search NASA⌕ Search

SEARCH · Search NASA

Results for “Parallel applications”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,153 records · Page 64
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Are highly parallel systems ready for prime time?

This is the edited and abbreviated transcript of a panel discussion held on May 9, 1989, in Los Angeles, during the conference "Parallel Computational Fluid Dynamics-Implementations and Results Using MIMD Computers." The purpose of the conference was to discuss recent developments in the use of MIMD parallel computers in high-performance computational fluid dynamics. The intent of the panel discussion was to summarize the findings of the meeting, and to give a perspective on the state of the art in using parallel computers for solving large-scale engineering and scientific application problems. The panelists were Creon Levit (NAS Systems Division, NASA-Ames Research Center, Moffett Field, California), Kent Misegades (Cray Research, Inc., Mendota Heights, Minnesota), Gary Montry (Myrias Computer Corporation, Albuquerque, New Mexico), Ken Neves (Boeing Computer Services, Seattle, Washington), Anthony Patera (Massachusetts Institute of Technology, Cambridge, Massachusetts), and Justin Rattner (Intel Scientific Computers, Beaverton, Oregon).

Simon, Horst D.↗
NASA NTRS1994

Parallel Preconditioning for CFD Problems on the CM-5

Up to today, preconditioning methods on massively parallel systems have faced a major difficulty. The most successful preconditioning methods in terms of accelerating the convergence of the iterative solver such as incomplete LU factorizations are notoriously difficult to implement on parallel machines for two reasons: (1) the actual computation of the preconditioner is not very floating-point intensive, but requires a large amount of unstructured communication, and (2) the application of the preconditioning matrix in the iteration phase (i.e. triangular solves) are difficult to parallelize because of the recursive nature of the computation. Here we present a new approach to preconditioning for very large, sparse, unsymmetric, linear systems, which avoids both difficulties. We explicitly compute an approximate inverse to our original matrix. This new preconditioning matrix can be applied most efficiently for iterative methods on massively parallel machines, since the preconditioning phase involves only a matrix-vector multiplication, with possibly a dense matrix. Furthermore the actual computation of the preconditioning matrix has natural parallelism. For a problem of size n, the preconditioning matrix can be computed by solving n independent small least squares problems. The algorithm and its implementation on the Connection Machine CM-5 are discussed in detail and supported by extensive timings obtained from real problem data.

Simon, Horst D.↗
NASA NTRS2007

Modeling Magnetic Properties in EZTB

A software module that calculates magnetic properties of a semiconducting material has been written for incorporation into, and execution within, the Easy (Modular) Tight-Binding (EZTB) software infrastructure. [EZTB is designed to model the electronic structures of semiconductor devices ranging from bulk semiconductors, to quantum wells, quantum wires, and quantum dots. EZTB implements an empirical tight-binding mathematical model of the underlying physics.] This module can model the effect of a magnetic field applied along any direction and does not require any adjustment of model parameters. The module has thus far been applied to study the performances of silicon-based quantum computers in the presence of magnetic fields and of miscut angles in quantum wells. The module is expected to assist experimentalists in fabricating a spin qubit in a Si/SiGe quantum dot. This software can be executed in almost any Unix operating system, utilizes parallel computing, can be run as a Web-portal application program. The module has been validated by comparison of its predictions with experimental data available in the literature.

Lee, Seungwon↗
NASA NTRS2004

Centrifugal Adsorption Cartridge System

The centrifugal adsorption cartridge system (CACS) is an apparatus that recovers one or more bioproduct(s) from a dilute aqueous solution or suspension flowing from a bioreactor. The CACS can be used both on Earth in unit gravity and in space in low gravity. The CACS can be connected downstream from the bioreactor; alternatively, it can be connected into a flow loop that includes the bioreactor so that the liquid can be recycled. A centrifugal adsorption cartridge in the CACS (see figure) includes two concentric cylinders with a spiral ramp between them. The volume between the inner and outer cylinders, and between the turns of the spiral ramp is packed with an adsorbent material. The inner cylinder is a sieve tube covered with a gas-permeable, hydrophobic membrane. During operation, the liquid effluent from the bioreactor is introduced at one end of the spiral ramp, which then constrains the liquid to flow along the spiral path through the adsorbent material. The spiral ramp also makes the flow more nearly uniform than it would otherwise be, and it minimizes any channeling other than that of the spiral flow itself. The adsorbent material is formulated to selectively capture the bioproduct(s) of interest. The bioproduct(s) can then be stored in bound form in the cartridge or else eluted from the cartridge. The centrifugal effect of the spiral flow is utilized to remove gas bubbles from the liquid. The centrifugal effect forces the bubbles radially inward, toward and through the membrane of the inner cylinder. The gas-permeable, hydrophobic membrane allows the bubbles to enter the inner cylinder while keeping the liquid out. The bubbles that thus enter the cylinder are vented to the atmosphere. The spacing between the ramps determines rate of flow along the spiral, and thereby affects the air-bubble-removal efficiency. The spacing between the ramps also determines the length of the fluid path through the cartridge adsorbent, and thus affects the bioproduct-capture efficiency of the cartridge. Depending on the application, several cartridges could be connected in a serial or parallel flow arrangement. A parallel arrangement can be used to increase product-capturing and flow capacities while maintaining a low pressure drop. A serial arrangement can be used to obtain high product-capturing capacity; alternatively, series-connected cartridges can be packed with different adsorbents to capture different bioproducts simultaneously.

Gonda, Steve R.↗
NASA NTRS2013

Modeling of Particle Acceleration at Multiple Shocks via Diffusive Shock Acceleration: Preliminary Results

Successful forecasting of energetic particle events in space weather models require algorithms for correctly predicting the spectrum of ions accelerated from a background population of charged particles. We present preliminary results from a model that diffusively accelerates particles at multiple shocks. Our basic approach is related to box models in which a distribution of particles is diffusively accelerated inside the box while simultaneously experiencing decompression through adiabatic expansion and losses from the convection and diffusion of particles outside the box. We adiabatically decompress the accelerated particle distribution between each shock by either the method explored in Melrose and Pope (1993) and Pope and Melrose (1994) or by the approach set forth in Zank et al. (2000) where we solve the transport equation by a method analogous to operator splitting. The second method incorporates the additional loss terms of convection and diffusion and allows for the use of a variable time between shocks. We use a maximum injection energy (E(sub max)) appropriate for quasi-parallel and quasi-perpendicular shocks and provide a preliminary application of the diffusive acceleration of particles by multiple shocks with frequencies appropriate for solar maximum (i.e., a non-Markovian process).

Parker, L. Neergaard↗
NASA NTRS2015

Minimal Power Latch for Single-Slope ADCs

A latch circuit that uses two interoperating latches. The latch circuit has the beneficial feature that it switches only a single time during a measurement that uses a stair step or ramp function as an input signal in an analog to digital converter. This feature minimizes the amount of power that is consumed in the latch and also minimizes the amount of high frequency noise that is generated by the latch. An application using a plurality of such latch circuits in a parallel decoding ADC for use in an image sensor is given as an example.

Hancock, Bruce R.↗
NASA NTRS1994

VLSI Neuroprocessors

Electronic and optoelectronic hardware implementations of highly parallel computing architectures address several ill-defined and/or computation-intensive problems not easily solved by conventional computing techniques.

computing↗
DOE OSTI2020

Orchestrating Fault Prediction with Live Migration and Checkpointing

Checkpoint/Restart (C/R) is widely used to provide fault tolerance on High-Performance Computing (HPC) systems. However, Parallel File System (PFS) overhead and failure uncertainty cause significant application overhead. This paper develops an adaptive multi-level C/R model that incorporates a failure prediction and analysis model, which orchestrates failure prediction, checkpointing, checkpoint frequency, and proactive live migration along with the additional benefit of Burst Buffers (BB). It effectively reduces the overheads due to failures, checkpointing, and recovery. Simulation results for the Summit supercomputer yield a reduction of ~20%-86% in application overhead due to BBs, orchestrated failure prediction, and migration. We also observe a ~29% decrease in checkpoint writes to BBs, which can increase the longevity of the BB storage devices.

Behera, Subhendu↗
DOE OSTI2023

Non-Orthogonal Configuration Interaction for Fragments

An overview is given of the non-orthogonal configuration interaction (NOCI) implementation in the open-source program GronOR. After pointing out the specifics of NOCI and its extension to ensembles of molecules NOCI-Fragments (NOCI-F), the theoretical foundations of the approaches are described. The introduction of a common molecular orbital basis, the selection of the most contributing determinants pairs, and a massively parallel and GPU-accelerated implementation have made possible the application of NOCI to medium-sized systems as illustrated in a short overview of some of the recent applications.

de Graaf, C.↗
DOE OSTI2021

Efficient exascale discretizations: High-order finite element methods

Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

On-lattice kinetic Monte Carlo approaches for modeling molecular anisotropy in resveratrol crystallization

Stilbenes are a class of organic compounds with broad-ranging pharmaceutical and agricultural applications, which are typically isolated and purified through recrystallization. We are motivated by reducing experimental waste and optimizing yield via developing predictive simulations for processing-dependent crystal morphologies. Using resveratrol as a model stilbene system, we have developed an approach for simulating crystallization with molecular resolution using on-lattice kinetic Monte Carlo. In this work, we highlight modifications to the Stochastic Parallel PARticle Kinetic Simulator (SPPARKS) software package, which were essential to this application. Key enhancements include the incorporation of non-orthogonal cell shapes and monomer anisotropy approximations using bound hard spheres. This new SPPARKS application has been applied to resveratrol with attachment energy libraries obtained from density functional theory, resulting in excellent agreement with experimental morphology prediction.

crystallization↗
DOE OSTI2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications

Scientific communities are increasingly adopting deep learning (DL) models in their applications to accelerate scientific discovery processes. However, with rapid growth in the computing capabilities of HPC supercomputers, large-scale DL applications have to spend a significant portion of training time performing I/O to a parallel storage system. Previous research works have investigated optimization techniques such as prefetching and caching. Unfortunately, there exist non-trivial challenges to adopting the existing solutions on HPC supercomputers for large-scale DL training applications, which include non-performance and/or failures at extreme scale, lack of portability and generality in design, complex deployment methodology, and being limited to a specific application or dataset. To address these challenges, we propose High-Velocity AI Cache (HVAC), a distributed read-cache layer that targets and fully exploits the node-local storage or near node-local storage technology. HVAC seamlessly accelerates read I/O by aggregating node-local or near node-local storage, avoiding metadata lookups and file locking while preserving portability in the application code. We deploy and evaluate HVAC on 1,024 nodes (with over 6000 NVIDIA V100 GPUS) of the Summit supercomputer. In particular, we evaluate the scalability, efficiency, accuracy, and load distribution of HVAC compared to GPFS and XFS-on-NVMe. With four different DL applications, we observe an average 25 % performance improvement atop GPFS and 9% drop against XFS-on-NVMe, which scale linearly and are considered the performance upper bound. We envision HVAC as an important caching library for upcoming HPC supercomputers such as Frontier.

Khan, Awais↗
DOE OSTI2021

Enabling particle applications for exascale computing platforms

The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applications (CoPA) is addressing challenges faced by particle-based applications across four “sub-motifs”: short-range particle–particle interactions (e.g., those which often dominate molecular dynamics (MD) and smoothed particle hydrodynamics (SPH) methods), long-range particle–particle interactions (e.g., electrostatic MD and gravitational N-body), particle-in-cell (PIC) methods, and linear-scaling electronic structure and quantum molecular dynamics (QMD) algorithms. Our crosscutting co-designed technologies fall into two categories: proxy applications (or “apps”) and libraries. Proxy apps are vehicles used to evaluate the viability of incorporating various types of algorithms, data structures, and architecture-specific optimizations and the associated trade-offs; examples include ExaMiniMD, CabanaMD, CabanaPIC, and ExaSP2. Libraries are modular instantiations that multiple applications can utilize or be built upon; CoPA has developed the Cabana particle library, PROGRESS/BML libraries for QMD, and the SWFFT and fftMPI parallel FFT libraries. Success is measured by identifiable “lessons learned” that are translated either directly into parent production application codes or into libraries, with demonstrated performance and/or productivity improvement. The libraries and their use in CoPA’s ECP application partner codes are also addressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Highlights of X-Stack ExM Deliverable: MosaStore

This brief report highlights the experience gained with MosaStore, an exploratory part of the X-Stack project “ExM: System support for extreme-scale, many-task applications”. The ExM project proposed to use concurrent workflows supported by the Swift language and runtime as an innovative programming model to exploit parallelism in exascale computers. MosaStore aims to support this endeavor by improving storage support for workflow-based applications, more precisely by exploring the gains that can be obtained from co-designing the storage system and the workflow runtime engine. MosaStore has been developed primarily at the University of British Columbia.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Addressing Two-Phase Flow Maldistribution in Microchannel Heat and Mass Exchangers

Multiphase flow phenomena in single micro and minichannels have been widely studied. Characteristics of two-phase flow through a large array of microchannels are investigated here. An air–water mixture is used to represent the two phases flowing through a microchannel array representative of those employed in practical applications. Flow distribution of the air and water flow across 52 parallel microchannels of 0.4 mm hydraulic diameter is visually investigated using high-speed photography. Two microchannel configurations are studied and compared, with mixing features incorporated into the second configuration. Slug and annular flow regimes are observed in the channels. Void fractions and interfacial areas are calculated for each channel from these observations. The flow distribution is tracked at various lengths along the microchannel array sheets. Statistical distributions of void fraction and interfacial area along the microchannel array are measured. The design with mixing features yields improved flow distribution. Void fraction and interfacial area change along the length of the second configuration, indicating a change in fluid distribution among the channels. The void fraction and interfacial area results are used to predict the performance of different microchannel array configurations for heat and mass transfer applications. Results from this study can help inform the design of compact thermal-fluid energy systems.

Engineering↗
DOE OSTI2019

Towards Native Execution of Deep Learning on a Leadership-Class HPC System

Large parallel machines generally offer the best parallel performance with "native execution" that is achieved using codes developed with the optimized compilers, communication libraries, and runtimes offered on the machines. In this paper, we report and analyze performance results from native execution of deep learning on a leadership-class high-performance computing (HPC) system. Using our new code called DeepEx, we present a study of the parallel speed up and convergence rates of learning achieved with native parallel execution. In the trade-off between computational parallelism and synchronized convergence, we first focus on maximizing parallelism while still obtaining convergence. Scaling results are reported from execution on up to 15,000 GPUs using two scientific data sets from atom microscopy and protein folding applications, and also using the popular ImageNet data set. In terms of the traditional measure of parallel speed up, excellent scaling is observed up to 12,000 GPUs. Additionally, accounting for convergence rates of deep learning accuracy or error, a deep learning-specific metric called "learning speed up" is also tracked. The performance results indicate the need to evaluate parallel deep learning execution in terms of learning speed up, and point to additional directions for improved exploitation of high-end HPC systems.

Yoginath, Srikanth↗
DOE OSTI2021

Axom

Axom is an open-source library of "building block" software components that provide core infrastructure capabilities for HPC applications. Such capabilities include: input file parsing and verification for various file formats, format specification for material shape input to multi-material simulations and tools for placing those shapes on meshes, parallel distributed coordination of diagnostic messages, geometric primitives, spatial queries and spatial search acceleration data structures, in memory key-value data store for managing simulation data and parallel file I/O. Axom components are used widely across LLNL Advanced Simulation and Computing program applications. It is also part of the LLNL institutionally-supported RADIUSS project, which promotes and funds its adoption by projects across LLNL.

Zagaris, George↗