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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 19 records

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Performance Evaluation of a Two-Dimensional Flood Model on Heterogeneous High-Performance Computing Architectures

This paper describes the implementation of a two-dimensional hydrodynamic flood model with two different numerical schemes on heterogeneous high-performance computing architectures. Both schemes were able to solve the nonlinear hyperbolic shallow water equations using an explicit upwind first-order approach on finite differences and finite volumes, respectively, and were conducted using MPI and CUDA. Four different test cases were simulated on the Summit supercomputer at Oak Ridge National Laboratory. Both numerical schemes scaled up to 128 nodes (768 GPUs) with a maximum 98.2x speedup of over 1 GPU. The lowest run time for the 10 day Hurricane Harvey event simulation at 5 meter resolution (272 million grid cells) was 50 minutes. GPUDirect communication proved to be more convenient than the standard communication strategy. Both strong and weak scaling are shown.

Sharif, Md Bulbul↗

DOE OSTI2023

Signal Processing Based Method for Real-Time Anomaly Detection in High-Performance Computing

Performance anomalies can manifest as irregular execution times or abnormal execution events for many reasons, including network congestion and resource contention. Detecting such anomalies in real-time by analyzing the details of performance traces at scale is impractical due to the sheer volume of data High-Performance Computing (HPC) applications produce. In this paper, we propose formulating HPC performance anomaly detection as a signal-processing problem where anomalies can be treated as noise. We evaluate our proposed method in comparison with two other commonly used anomaly detection techniques of varying complexity based on their detection accuracy and scalability. Since real-time in-situ anomaly detection at a large scale requires lightweight methods that can handle a large volume of streaming data, we find that our proposed method provides the best trade-off. We then implement the proposed method in Chimbuko, the first online, distributed, and scalable workflow-level performance trace analysis framework. We compare our proposed signal-based anomaly detection algorithm with two other methods using a function of their accuracy, F1 score, and detection overhead. Our experiments demonstrate that our proposed approach achieves a 99% improvement for the benchmark datasets and a 93% improvement with Chimbuko traces.

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2000

Demonstration of Cost-Effective, High-Performance Computing at Performance and Reliability Levels Equivalent to a 1994 Vector Supercomputer

The Affordable High Performance Computing (AHPC) project demonstrated that high-performance computing based on a distributed network of computer workstations is a cost-effective alternative to vector supercomputers for running CPU and memory intensive design and analysis tools. The AHPC project created an integrated system called a Network Supercomputer. By connecting computer work-stations through a network and utilizing the workstations when they are idle, the resulting distributed-workstation environment has the same performance and reliability levels as the Cray C90 vector Supercomputer at less than 25 percent of the C90 cost. In fact, the cost comparison between a Cray C90 Supercomputer and Sun workstations showed that the number of distributed networked workstations equivalent to a C90 costs approximately 8 percent of the C90.

Babrauckas, Theresa↗

DOE OSTI2022

Accelerating Simulation for High-Fidelity PV Inverter System Reliability Assessment with High-Performance Computing

The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.

high-performance computing↗

DOE OSTI2023

High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

US Department of Energy, Office of Science, High-Performance Computing Facility: 2023 Operational Assessment Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through effective allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2023, the OLCF supported 1,676 users and 598 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.52 out of 5 on the annual user survey, and 94% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,619 user tickets submitted in CY 2023, OLCF staff resolved 97% within 3 business days. The facility opened Frontier to full scientific operations this year. Two projects conducted on Frontier received the Association for Computing Machinery (ACM) Gordon Bell Prize and the Gordon Bell Special Prize for Climate Modeling, and a third earned a nomination as a Gordon Bell Prize finalist. The facility’s previous flagship machine, Summit, gained new life and was extended through 2024 in part to help provide resources to the Integrated Research Infrastructure (IRI) projects and the National Artificial Intelligence Research Resource (NAIRR) pilot program. The facility instantiated an Advanced Computing Ecosystem testbed in part to support IRI workflows. OLCF made interactivity easier and more accessible to users than ever through tools like Jupyter notebooks and workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High-Performance Computing for Earth System Modeling

High-performance computing (HPC) plays an important role during the development of Earth system models. This chapter reviews HPC efforts related to Earth system models, including community Earth system models and energy exascale Earth system models. Specifically, this chapter evaluates computational and software design issues, analyzes several current HPC-related model developments, and provides an outlook for some promising areas within Earth system modeling in the era of exascale computing.

Wang, Dali↗

DOE OSTI2025

US Department of Energy, Office of Science, High-Performance Computing Facility 2024 Operational Assessment Oak Ridge Leadership Computing Facility

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Frontiers in Scientific Workflows: Pervasive Integration With High-Performance Computing

Herein we address the increasing complexity of scientific workflows in the context of high-performance computing (HPC) and their associated need for robust, adaptable, and flexible computational support systems. We explore five key trends as well as future challenges and opportunities for scientific workflows and HPC technologies.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1997

High Performance Input/Output Systems for High Performance Computing and Four-Dimensional Data Assimilation

The approach of this task was to apply leading parallel computing research to a number of existing techniques for assimilation, and extract parameters indicating where and how input/output limits computational performance. The following was used for detailed knowledge of the application problems: 1. Developing a parallel input/output system specifically for this application 2. Extracting the important input/output characteristics of data assimilation problems; and 3. Building these characteristics s parameters into our runtime library (Fortran D/High Performance Fortran) for parallel input/output support.

Fox, Geoffrey C.↗

DOE OSTI2020

Heterogeneous Machine Learning on High Performance Computing for End to End Driving of Autonomous Vehicles

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving. For machines though, this gap is guarded by at least two challenges: 1) machine learning techniques remain brittle and unable to generalize to a wide range of scenarios, and 2) effective training data that enhances generalization and generates the desired driving behavior. Further, each challenge can be computationally intensive on its own thereby exasperating the gap. Moreover, is has not yet been shown that a single form of learning or training is capable of addressing a large range of scenarios. As a result, solving the first challenge does not inherently solve the second and vice versa. The work described here discusses an approach to address the first challenge that would also provide a foundation for solving the second. Our approach utilizes a combination of conditional imitation learning with a static dataset, reinforcement learning with a simulation environment, and high-performance computing to train a neural network. As a result, this reduces the “time to solution” from to the existing techniques for autonomous driving and provides an extensible framework to address the second key challenge.

autonomous vehicles↗

DOE OSTI2022

Transforming Energy Through Computational Excellence: High-Performance Computing for Energy Innovation

The challenges associated with energy efficiency of manufacturing and advanced materials often cannot be addressed through experimentation alone, whether because of scale, complexity, or practicality. High-performance computing (HPC) enables fast tackling of these challenges in the manufacturing sector - vital to achieving net-zero carbon emissions by 2050. The National Renewable Energy Laboratory (NREL) and industry partners leverage HPC to apply advanced modeling, simulation, and data analysis to improve manufacturing efficiency, explore new materials for energy applications, and develop technologies to manage carbon across the life cycle. From improving additive manufacturing processes to increasing the energy efficiency of jet-engine components, advanced computing can help manage emissions produced by manufacturing in a wide variety of ways.

advanced materials↗

DOE OSTI2024

Integrating Quantum Computing with High-Performance Computing: A Streamlined Approach

In recent years, quantum computing has demon-strated the potential to revolutionize specific algorithms and applications by solving problems exponentially faster than classical computers. However, its widespread adoption for general computing remains a future prospect. This paper discusses the integration of quantum computing within High-Performance Computing (HPC) environments, focusing on a resource management framework designed to streamline quantum simulators' use and enhance runtime performance and efficiency. The proposed framework facilitates hybrid applications' transition from simulation backends to real quantum hardware, optimizing resource utilization and providing a flexible infrastructure for developing and testing quantum algorithms.

Shehata, Amir↗

NASA NTRS2024

A High-Performance Computing Predictive GNSS Performance Monitor for Autonomous Air Vehicles in Urban Environments

This report offers analysis and design insights for leveraging High-Performance Computing (HPC) to predict line-of-sight (LOS) Global Navigation Satellite System (GNSS) availability in a city. This work is motivated by the emerging fields of Advanced and Urban Air Mobility (AAM/UAM), where regulatory authorities are seeking city-scale, meter-resolution risk forecasting in order to safely integrate new flight missions with existing urban life and infrastructure. This work addresses the technical challenge of efficiently computing urban GNSS satellite visibility to predict GNSS performance metrics under these requirements. We present a new HPC-optimized shadow casting algorithm variant as a ray-based approach to forecasting satellite visibility. We apply this algorithm variant in a software-defined prognostic service which generates a GNSS navigation risk-correlated map as a path planning-style potential field. We detail dominant computational burdens, viable simplifying assumptions, and different algorithmic implementations, intending to demonstrate a baseline of computation time needed by each stage in such a service. We conclude by analyzing the prototype service’s prediction accuracy compared to receiver data from Corpus Christi, Texas. This informs design trade-offs along the dimensions of hardware, computation time, and tolerable forecasting error (including proportions of false positives and false negatives).

GNSS↗

NASA NTRS1984

Ku-Band rendezvous radar performance computer simulation model

All work performed on the Ku-band rendezvous radar performance computer simulation model program since the release of the preliminary final report is summarized. Developments on the program fall into three distinct categories: (1) modifications to the existing Ku-band radar tracking performance computer model; (2) the addition of a highly accurate, nonrealtime search and acquisition performance computer model to the total software package developed on this program; and (3) development of radar cross section (RCS) computation models for three additional satellites. All changes in the tracking model involved improvements in the automatic gain control (AGC) and the radar signal strength (RSS) computer models. Although the search and acquisition computer models were developed under the auspices of the Hughes Aircraft Company Ku-Band Integrated Radar and Communications Subsystem program office, they have been supplied to NASA as part of the Ku-band radar performance comuter model package. Their purpose is to predict Ku-band acquisition performance for specific satellite targets on specific missions. The RCS models were developed for three satellites: the Long Duration Exposure Facility (LDEF) spacecraft, the Solar Maximum Mission (SMM) spacecraft, and the Space Telescopes.

Magnusson, H. G.↗

NASA NTRS1970

A report on computer performance evaluation techniques

Computer performance evaluation techniques

Yeh, J.↗

NASA NTRS1991

The High Performance Computing Initiative

The paper discusses NASA High Performance Computing Initiative (HPCI), an essential component of the Federal High Performance Computing Program. The HPCI program is designed to provide a thousandfold increase in computing performance, and apply the technologies to NASA 'Grand Challenges'. The Grand Challenges chosen include integrated multidisciplinary simulations and design optimizations of aerospace vehicles throughout the mission profiles; the multidisciplinary modeling and data analysis of the earth and space science physical phenomena; and the spaceborne control of automated systems, handling, and analysis of sensor data and real-time response to sensor stimuli.

Holcomb, Lee B.↗

DOE OSTI2020

INL High Performance Computing Overview

INL High Performance Computing Overview presentation by Eric Whiting for the National Science Users Facility 2020 Annual Program Review on November 10, 2020; data covering our Collaborative Computing Center (C3), the supercomputers and their data.

99 GENERAL AND MISCELLANEOUS↗