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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

On evaluating the performability of degradable computing systems

If the performance of a computing system is 'degradable', performance and reliability issues must be dealt with simultaneously in the process of evaluating system effectiveness. For this purpose, a unified measure, called 'performability', is introduced and the foundations of performability modeling and evaluation are established. A critical step in the modeling process is the introduction of a 'capability function' which relates low-level system behavior to user-oriented performance levels. A hierarchical modeling scheme is used to formulate the capability function and capability is used, in turn, to evaluate performability. These techniques are then illustrated for a specific application: the performability evaluation of an aircraft computer in the environment of an air transport mission.

Meyer, J. F.↗
DOE OSTI2013

A survey on resource allocation in high performance distributed computing systems

An efficient resource allocation is a fundamental requirement in high performance computing (HPC) systems. Many projects are dedicated to large-scale distributed computing systems that have designed and developed resource allocation mechanisms with a variety of architectures and services. In our study, through analysis, a comprehensive survey for describing resource allocation in various HPCs is reported. The aim of the work is to aggregate under a joint framework, the existing solutions for HPC to provide a thorough analysis and characteristics of the resource management and allocation strategies. Resource allocation mechanisms and strategies play a vital role towards the performance improvement of all the HPCs classifications. Therefore, a comprehensive discussion of widely used resource allocation strategies deployed in HPC environment is required, which is one of the motivations of this survey. Moreover, we have classified the HPC systems into three broad categories, namely: (a) cluster, (b) grid, and (c) cloud systems and define the characteristics of each class by extracting sets of common attributes. All of the aforementioned systems are cataloged into pure software and hybrid/hardware solutions. The system classification is used to identify approaches followed by the implementation of existing resource allocation strategies that are widely presented in the literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Utility maximizing dynamic resource management in an oversubscribed energy-constrained heterogeneous computing system

The need for greater performance in high performance computing systems combined with rising costs of electricity to power these systems motivates the need for energy-efficient resource management. Driven by the requirements of the Extreme Scale Systems Center at Oak Ridge National Laboratory, our study addresses the problem of scheduling dynamically-arriving tasks to machines in an oversubscribed and energy-constrained heterogeneous distributed computing environment. Our goal is to maximize total “utility” earned by the system, where the utility of a task is defined by a monotonically-decreasing function that represents the value of completing that task at different times. To address this problem, we design four energy-aware resource allocation heuristics and compare their performance to heuristics from the literature. For our given energy-constrained environment, we also design an energy filtering technique that helps some heuristics regulate their energy consumption by allowing tasks to only consume up to an estimated fair-share of energy. Extensive sensitivity analyses of the heuristics in environments with different levels of heterogeneity show that heuristics with the ability to balance both energy consumption and utility exhibit the best performance because they save energy for use by future tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Software Systems for High-performance Quantum Computing

Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.

high-performance computing↗
NASA NTRSDate not supplied

Signal and Power Integrity Design Methodology for High-Performance Flight Computing Systems

Computing capabilities of space systems have in-creased onboard performance by orders of magnitude with the use of radiation-tolerant field-programmable gate arrays (FPGA)and processors. The incorporation of signal and power integrity analysis with printed circuit board (PCB) design in reliable computing architectures for space systems has become critical to enable future mission capabilities. Developers launch high-performance processors into a breadth of orbits and missions, running varying applications that create challenges for designing reliable computing hardware. Specifically, for these designs, academic and industry research has focused on component radiation performance, fault mitigation, and reliable architectures. How-ever, other design parameters including electromagnetic interference (EMI), PCB stackup, signal integrity (SI), voltage regulator module (VRM) design, and power distribution network (PDN)are often deprioritized or disregarded as the design matures. Since these characteristics are becoming more significant in high-performance processor designs, this research presents a hardware design and analysis methodology for high-performance, space-computing systems that focuses on a holistic design approach and PDN reliability. While these challenges exist across all space hardware, the reduced PCB dimensions imposed by SmallSats and CubeSats introduce additional hurdles, specifically to VRM and decoupling design. By examining the relationship between the PDN and radiation performance, an analytical relationship is developed that incorporates Total Ionizing Dose and Single-Event Transients to ensure reliability throughout the mission duration. The presented design methodology is applied to the SpaceCube v3.0 Mini, an FPGA-based on-board science data processing system developed at NASA Goddard Space Flight Center.

Advanced avionics↗
DOE OSTI2016

Methods, apparatus and system for selective duplication of subtasks

A method for selective duplication of subtasks in a high-performance computing system includes: monitoring a health status of one or more nodes in a high-performance computing system, where one or more subtasks of a parallel task execute on the one or more nodes; identifying one or more nodes as having a likelihood of failure which exceeds a first prescribed threshold; selectively duplicating the one or more subtasks that execute on the one or more nodes having a likelihood of failure which exceeds the first prescribed threshold; and notifying a messaging library that one or more subtasks were duplicated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Methods, apparatus and system for selective duplication of subtasks

A method for selective duplication of subtasks in a high-performance computing system includes: monitoring a health status of one or more nodes in a high-performance computing system, where one or more subtasks of a parallel task execute on the one or more nodes; identifying one or more nodes as having a likelihood of failure which exceeds a first prescribed threshold; selectively duplicating the one or more subtasks that execute on the one or more nodes having a likelihood of failure which exceeds the first prescribed threshold; and notifying a messaging library that one or more subtasks were duplicated.

Andrade Costa, Carlos H.↗
DOE OSTI2025

ChemComp: Compiling and Computing with Chemical Reaction Networks

The exponential growth in computing demands driven by scientific computing, data analytics, and artificial intelligence is pushing conventional CMOS-based high-performance computing systems to their physical and energy efficiency limits. As we approach the era of post-exascale computing, disruptive approaches are necessary to overcome these barriers and achieve substantial gains in energy efficiency. Analog and hybrid digital-analog computing systems have emerged as promising alternatives, offering the potential for orders-of-magnitude improvements in efficiency. Among these, biochemical computing stands out as a novel paradigm capable of leveraging the natural efficiency of chemical reactions, which have shown promise in solving optimization problems by converging to steady states. By scaling up reaction networks or reaction vessel sizes, biochemical systems present an opportunity to meet the high-performance demands of modern computing tasks. Despite their promise, significant theoretical and practical challenges remain, particularly in formulating and mapping computational problems to chemical reaction networks (CRNs) and designing viable biochemical computing devices. This paper addresses these challenges by introducing new ideas to ChemComp, a compilation and emulation framework for chemical computation. This work describes the mechanisms through which solutions to ordinary differential equations (ODEs) that can be represented as CRN systems can be achieved. Furthermore, we explain the design principles of an ODE dialect implemented as a multi-level intermediate representation (MLIR) compiler extension that will be coupled with existing infrastructure. We demonstrate the potential of our framework through a case study emulating a simplified chemical reservoir computing device. This work establishes foundational tools and methodologies necessary to harness the computational power of chemistry, paving the way for the development of energy-efficient, high-performance computing systems tailored to contemporary and future computational needs.

Bohm Agostini, Nicolas↗
DOE OSTI2015

Opportunities for nonvolatile memory systems in extreme-scale high-performance computing

For extreme-scale high-performance computing systems, system-wide power consumption has been identified as one of the key constraints moving forward, where DRAM main memory systems account for about 30 to 50 percent of a node's overall power consumption. As the benefits of device scaling for DRAM memory slow, it will become increasingly difficult to keep memory capacities balanced with increasing computational rates offered by next-generation processors. However, several emerging memory technologies related to nonvolatile memory (NVM) devices are being investigated as an alternative for DRAM. Moving forward, NVM devices could offer solutions for HPC architectures. Researchers are investigating how to integrate these emerging technologies into future extreme-scale HPC systems and how to expose these capabilities in the software stack and applications. In addition, current results show several of these strategies could offer high-bandwidth I/O, larger main memory capacities, persistent data structures, and new approaches for application resilience and output postprocessing, such as transaction-based incremental checkpointing and in situ visualization, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

FY 2021 High Performance Computing Overall Systems Availability

INL HPC capabilities were utilized by individuals at universities, industry, and government laboratories for a wide range of research and engineering activities between October 1, 2020 and September 30, 2021. The HPC systems themselves experienced minimal downtime over the course of the fiscal year. Most downtime was planned to apply critical security patches to system software. The most serious of the unplanned downtime events occurred due to maintenance on the Kohler backup generator in July. Users were kept notified of all events, both planned and unplanned, via 36 total email notifications in FY-21. The INL HPC systems continue to provide high availability for collaboration and innovation in nuclear energy systems research.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2015

Memory Errors in Modern Systems: The Good, The Bad, and The Ugly

Several recent publications have shown that hardware faults in the memory subsystem are commonplace. These faults are predicted to become more frequent in future systems that contain orders of magnitude more DRAM and SRAM than found in current memory subsystems. These memory subsystems will need to provide resilience techniques to tolerate these faults when deployed in high-performance computing systems and data centers containing tens of thousands of nodes. Therefore, it is critical to understand the efficacy of current hardware resilience techniques to determine whether they will be suitable for future systems. In this paper, we present a study of DRAM and SRAM faults and errors from the field. We use data from two leadership-class high-performance computer systems to analyze the reliability impact of hardware resilience schemes that are deployed in current systems. Our study has several key findings about the efficacy of many currently deployed reliability techniques such as DRAM ECC, DDR address/command parity, and SRAM ECC and parity. We also perform a methodological study, and find that counting errors instead of faults, a common practice among researchers and data center operators, can lead to incorrect conclusions about system reliability. Lastly, we use our data to project the needs of future large-scale systems. We find that SRAM faults are unlikely to pose a significantly larger reliability threat in the future, while DRAM faults will be a major concern and stronger DRAM resilience schemes will be needed to maintain acceptable failure rates similar to those found on today's systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

GPU Acceleration of Large-Scale Full-Frequency GW Calculations

Many-body perturbation theory is a powerful method to simulate electronic excitations in molecules and materials starting from the output of density functional theory calculations. By implementing the theory efficiently so as to run at scale on the latest leadership high-performance computing systems it is possible to extend the scope of GW calculations. Here, we present a GPU acceleration study of the full-frequency GW method as implemented in the WEST code. Excellent performance is achieved through the use of (i) optimized GPU libraries, e.g., cuFFT and cuBLAS, (ii) a hierarchical parallelization strategy that minimizes CPU-CPU, CPU-GPU, and GPU-GPU data transfer operations, (iii) nonblocking MPI communications that overlap with GPU computations, and (iv) mixed precision in selected portions of the code. A series of performance benchmarks has been carried out on leadership high-performance computing systems, showing a substantial speedup of the GPU-accelerated version of WEST with respect to its CPU version. Good strong and weak scaling is demonstrated using up to 25 920 GPUs. Finally, we showcase the capability of the GPU version of WEST for large-scale, full-frequency GW calculations of realistic systems, e.g., a nanostructure, an interface, and a defect, comprising up to 10 368 valence electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver

Abstract Solving large-scale inverse problems using deep-learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem may require the utilization of high performance computing systems which poses a challenge on the algorithmic design of the inverse problem solver. Most deep learning algorithms require, due to their design, custom parallelization techniques in order to be resource efficient while showing a reasonable convergence. In this paper we introduce a S calable A synchronous G enerative I nverse P roblem S olver (SAGIPS) on high-performance computing systems. We present a workflow that utilizes an asynchronous ring-allreduce algorithm to transfer the gradients of the generator network across multiple GPUs. Experiments with a scientific proxy application demonstrate that SAGIPS shows near linear weak scaling, together with a convergence quality that is comparable to traditional methods. The approach presented here allows leveraging Generative Adverserial Network across multiple GPUs, promising advancements in solving complex inverse problems at scale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

A fully non-linear multi-species Fokker–Planck–Landau collision operator for simulation of fusion plasma

Fusion edge plasmas can be far from thermal equilibrium and require the use of a non-linear collision operator for accurate numerical simulations. In this article, the non-linear single-species Fokker–Planck–Landau collision operator developed by Yoon and Chang (2014) [9] is generalized to include multiple particle species. The finite volume discretization used in this work naturally yields exact conservation of mass, momentum, and energy. The implementation of this new non-linear Fokker–Planck–Landau operator in the gyrokinetic particle-in-cell codes XGC1 and XGCa is described and results of a verification study are discussed. Finally, the numerical techniques that make our non-linear collision operator viable on high-performance computing systems are described, including specialized load balancing algorithms and nested OpenMP parallelization. The collision operator's good weak and strong scaling behavior are shown.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1979

Phased models for evaluating the performability of computing systems

A phase-by-phase modelling technique is introduced to evaluate a fault tolerant system's ability to execute different sets of computational tasks during different phases of the control process. Intraphase processes are allowed to differ from phase to phase. The probabilities of interphase state transitions are specified by interphase transition matrices. Based on constraints imposed on the intraphase and interphase transition probabilities, various iterative solution methods are developed for calculating system performability.

Wu, L. T.↗
DOE OSTI2018

Iterative Importance Sampling Algorithms for Parameter Estimation

In parameter estimation problems one computes a posterior distribution over uncertain parameters defined jointly by a prior distribution, a model, and noisy data. Markov chain Monte Carlo (MCMC) is often used for the numerical solution of such problems. An alternative to MCMC is importance sampling, which can exhibit near perfect scaling with the number of cores on high performance computing systems because samples are drawn independently. However, finding a suitable proposal distribution is a challenging task. Several sampling algorithms have been proposed over the past years that take an iterative approach to constructing a proposal distribution. We investigate the applicability of such algorithms by applying them to two realistic and challenging test problems, one in subsurface flow, and one in combustion modeling. More specifically, we implement importance sampling algorithms that iterate over the mean and covariance matrix of Gaussian or multivariate t-proposal distributions. Our implementation leverages massively parallel computers, and we present strategies to initialize the iterations using “coarse” MCMC runs or Gaussian mixture models.

97 MATHEMATICS AND COMPUTING↗