Search NASA⌕ Search

SEARCH · Search NASA

Results for “computing systems”

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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Hybrid data storage system in an HPC exascale environment

A computer-executable method, system, and computer program product for managing I/O requests from a compute node in communication with a data storage system, including a first burst buffer node and a second burst buffer node, the computer-executable method, system, and computer program product comprising striping data on the first burst buffer node and the second burst buffer node, wherein a first portion of the data is communicated to the first burst buffer node and a second portion of the data is communicated to the second burst buffer node, processing the first portion of the data at the first burst buffer node, and processing the second portion of the data at the second burst buffer node.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fast inversion, preconditioned quantum linear system solvers, fast Green's-function computation, and fast evaluation of matrix functions

Preconditioning is the most widely used and effective way for treating ill-conditioned linear systems in the context of classical iterative linear system solvers. We introduce a quantum primitive called fast inversion, which can be used as a preconditioner for solving quantum linear systems. The key idea of fast inversion is to directly block encode a matrix inverse through a quantum circuit implementing the inversion of eigenvalues via classical arithmetics. We demonstrate the application of preconditioned linear system solvers for computing single-particle Green's functions of quantum many-body systems, which are widely used in quantum physics, chemistry, and materials science. We analyze the complexities in three scenarios: the Hubbard model, the quantum many-body Hamiltonian in the plane-wave-dual basis, and the Schwinger model. We also provide a method for performing Green's function calculation in second quantization within a fixed-particle manifold and note that this approach may be valuable for simulation more broadly. Aside from solving linear systems, fast inversion also allows us to develop fast algorithms for computing matrix functions, such as the efficient preparation of Gibbs states. Furthermore, we introduce two efficient approaches for such a task, based on the contour-integral formulation and the inverse transform, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

A computational model for exploring particle acceleration during reconnection in macroscale systems

A new computational model suitable for exploring the self-consistent production of energetic electrons during magnetic reconnection in macroscale systems is presented. The equations are based on the recent discovery that parallel electric fields are ineffective drivers of energetic particles during reconnection so that the kinetic scales which control the development of such fields can be ordered out of the equations. The resulting equations consist of a magnetohydrodynamic (MHD) backbone with the energetic component represented by macro-particles described by the guiding center equations. Crucially, the energetic component feeds back on the MHD equations so that the total energy of the MHD fluid and the energetic particles is conserved. The equations correctly describe the firehose instability, whose dynamics plays a key role in throttling reconnection and in controlling the spectra of energetic particles. The results of early tests of the model, including the propagation of Alfvén waves in a system with pressure anisotropy and the growth of firehose modes, establish that the basic algorithm is stable and produces reliable physics results in preparation for further benchmarking with particle-in-cell models of reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Methods, systems, and devices for identifying computational operations based on power measurements

Disclosed herein are methods, systems, and devices for identifying computational operations based on power measurements. Disclosed herein are systems that may include a phase measurement unit configured to generate a first measurement based on a power signal, the first measurement characterizing a phase angle of the power signal. The systems may also include a power measurement unit configured to generate a second measurement based on the power signal, the second measurement characterizing an amplitude of one or more aspects of the power signal. The systems may further include a processing unit configured to identify a computational operation implemented by a target processing device, the identifying of the computational operation being based on a comparison of the first measurement and the second measurement with reference measurements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.

Shen, Xuehao↗
DOE OSTI2019

Earth System Grid Federation (ESGF) Compute Challenge

This Open Geospatial Consortium (OGC) Engineering Report (ER) describes the advancement of an Execution Management System (EMS) to support Web Processing Service (WPS) climate processes deployed on the Earth System Grid Federation (ESGF). The report introduces climate data, processes and applications into Common Workflow Language (CWL) workflows with the intent of advancing: application packaging, deployment and execution in clouds; interoperability of services in federated cyberinfrastructures; and geospatial workflows towards standardization. Work presented in this report is a direct continuation of the Earth Observation & Clouds (EOC) thread of Testbed-14. This report is expected to be of relevance to Testbed-15, both to the Earth Observation Process and Application Discovery (EOPAD) task and the Machine Learning task. This engineering report will describe: relevant work conducted in OGC Testbed-14; ESGF and its compute challenge; adaptations of existing climate processes into workflows; interoperability experiments with ESGF endpoints conforming to a common API.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Defining quantum-ready primitives for hybrid HPC-QC supercomputing: a case study in Hamiltonian simulation

As computational demands in scientific applications continue to rise, hybrid high-performance computing (HPC) systems integrating classical and quantum computers (HPC-QC) are emerging as a promising approach to tackling complex computational challenges. One critical area of application is Hamiltonian simulation, a fundamental task in quantum physics and other large-scale scientific domains. This paper investigates strategies for quantum-classical integration to enhance Hamiltonian simulation within hybrid supercomputing environments. By analyzing computational primitives in HPC allocations dedicated to these tasks, we identify key components in Hamiltonian simulation workflows that stand to benefit from quantum acceleration. To this end, we systematically break down the Hamiltonian simulation process into discrete computational phases, highlighting specific primitives that could be effectively offloaded to quantum processors for improved efficiency. Our empirical findings provide insights into system integration, potential offloading techniques, and the challenges of achieving seamless quantum-classical interoperability. We assess the feasibility of quantum-ready primitives within HPC workflows and discuss key barriers such as synchronization, data transfer latency, and algorithmic adaptability. These results contribute to the ongoing development of optimized hybrid solutions, advancing the role of quantum-enhanced computing in scientific research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

In-situ trainable intrusion detection system

A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Design and Construction of Detector and Data Acquisition Elements for Proton Computed Tomography

Proton computed tomography (pCT) offers an alternative to x-ray imaging with potential for three-dimensional imaging, reduced radiation exposure, and in-situ imaging. Northern Illinois University (NIU) is developing a second-generation proton computed tomography system with a goal of demonstrating the feasibility of three-dimensional imaging within clinically realistic imaging times. The second-generation pCT system is comprised of a tracking system, a calorimeter, data acquisition, a computing farm, and software algorithms. The proton beam encounters the upstream tracking detectors, the patient or phantom, the downstream tracking detectors, and a calorimeter. The schematic layout of the PCT system is shown. The data acquisition sends the proton scattering information to an offline computing farm. Major innovations of the second generation pCT project involve an increased data acquisition rate ( MHz range) and development of three-dimensional imaging algorithms. The Fermilab Particle Physics Division and Northern Illinois Center for Accelerator and Detector Development at Northern Illinois University worked together to design and construct the tracking detectors, calorimeter, readout electronics and detector mounting system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

System and method for computed axial lithography (CAL) for 3D additive manufacturing

In one aspect the present disclosure relates to a system for forming a three dimensional (3D) object from a volume of photo-responsive material contained within a material container. A plurality of optical projection subsystems are adapted to be arranged circumferentially around a material container containing the volume of photo-responsive material, and are controlled by a controller. The optical projection subsystems direct optical projections at a plurality of angles θ through the volume of photo-responsive material. The optical projections are further directed about a z axis extending through the volume of photo-responsive material. The plurality of optical subsystems are further controlled by the controller to provide each of the optical projections with a calculated three-dimensional intensity distribution acting over a fixed temporal exposure period, which is sufficient to at least one of cure or remove selected portions of the volume of photo-responsive material, and leave other portions unmodified, to form a desired 3D part.

Kelly, Brett↗
DOE OSTI2024

System and method for computed axial lithography (CAL) for 3D additive manufacturing

In one aspect the present disclosure relates to a system for forming a three dimensional (3D) object from a volume of photo-responsive material contained within a material container. A plurality of optical projection subsystems are adapted to be arranged at least partially circumferentially around a material container containing the volume of photo-responsive material, and are controlled by a controller. The optical projection subsystems direct optical projections at a plurality of angles θ through the volume of photo-responsive material. The optical projections are further directed about a z axis extending through the volume of photo-responsive material. The plurality of optical subsystems are further controlled by the controller to provide each of the optical projections with a calculated three-dimensional intensity distribution acting over a fixed temporal exposure period, which is sufficient to at least one of cure or remove selected portions of the volume of photo-responsive material, and leave other portions unmodified, to form a desired 3D part.

Kelly, Brett↗
DOE OSTI2022

Scalable Energy System Expansion Under Uncertainty Using Multi-Stage Stochastic Optimization

The intermittent nature of power from renewable energy sources poses new challenges for electrical grids. This is due to the variable and uncertain nature of the power output from these resources. These features of renewable generation are becoming more relevant to energy system planning as grids reach higher penetration levels of renewable energy. In this presentation we present approaches for energy system planning based on scalable computational approaches which enable the explicit consideration of operational uncertainties in the planning process. Using multi-stage stochastic programming and the progressive hedging algorithm, we compute energy system expansion decisions on modified versions of the RTS-GMLC test system augmented with large amounts of renewable generation.

MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Enabling Scalable VQE Simulation on Leading HPC Systems

Large-scale simulations of quantum circuits pose significant challenges, especially in the context of quantum chemistry, due to the number of qubits, circuit depth, and the number of circuits needed per problem. High-performance computing (HPC) systems offer massive computational capabilities that could help overcome these obstacles. We developed a high-performance quantum circuit simulator, called NWQ-Sim, and demonstrate its capability to simulate large quantum chemistry problems on NERSC's Perlmutter supercomputer. Integrating NWQ-Sim with XACC, we have executed QPE and VQE algorithms for downfolded quantum chemistry systems at unprecedented scales. Our work demonstrates the potential of leveraging HPC resources to advance quantum chemistry and other applications of near-term quantum devices.

Wang, Meng↗
DOE OSTI2019

Failure prediction using machine learning in a virtualised HPC system and application

Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), classification and regression trees (CART) and linear discriminant analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This finding implies that our method can effectively predict all possible future system and application failures within the system.

Mohammed, Bashir↗
DOE OSTI2022

Scalable Energy System Expansion Under Uncertainty Using Multi-Stage Stochastic Optimization

The intermittent nature of renewable energy sources poses challenges for electrical grids. This is due to the variable and uncertain nature of the power output from these resources. These features of renewable generation are more relevant to energy system planning as grids reach higher penetration levels of renewable energy. We present approaches for energy system planning based on scalable computational approaches which enable explicit consideration of operational uncertainties in the planning process. Using multi-stage stochastic programming and the progressive hedging algorithm, we compute energy system expansion decisions on modified versions of the RTS-GMLC test system.

electrical↗
DOE OSTI2021

Computational approaches for three-nucleon systems

Highlights: • Pedagogical review on the computational aspects of the three-nucleon system. • Detailed numerical steps for solving the three-body problem. • Three-nucleon binding and scattering for two different separable potentials. • Triton binding with partial-wave projected chiral potentials. We revisit the three-nucleon system with two kinds of nucleon–nucleon interactions: separable potentials and chiral forces, showing the computational aspects in detail. We start with S-wave separable potentials for which there are simplified forms for the Faddeev equations describing the scattering and binding of three nucleons. We then discuss the partial-wave projected case with a chiral potential considering only one channel so that the computational details can be clearly shown.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Flexible silicon photonic architecture for accelerating distributed deep learning

The increasing size and complexity of deep learning (DL) models have led to the wide adoption of distributed training methods in datacenters (DCs) and high-performance computing (HPC) systems. However, communication among distributed computing units (CUs) has emerged as a major bottleneck in the training process. In this study, we propose Flex-SiPAC, a flexible silicon photonic accelerated compute cluster designed to accelerate multi-tenant distributed DL training workloads. Flex-SiPAC takes a co-design approach that combines a silicon photonic hardware platform with a tailored collective algorithm, optimized to leverage the unique physical properties of the architecture. The hardware platform integrates a novel wavelength-reconfigurable transceiver design and a micro-resonator-based wavelength-reconfigurable switch, enabling the system to achieve flexible bandwidth steering in the wavelength domain. The collective algorithm is designed to support reconfigurable topologies, enabling efficient all-reduce communications that are commonly used in DL training. The feasibility of the Flex-SiPAC architecture is demonstrated through two testbed experiments. First, an optical testbed experiment demonstrates the flexible routing of wavelengths by shuffling an array of input wavelengths using a custom-designed spatial-wavelength selective switch. Second, a four-GPU testbed running two DL workloads shows a 23% improvement in job completion time compared to a similarly sized leaf-spine topology. We further evaluate Flex-SiPAC using large-scale simulations, which show that Flex-SiPAC is able to reduce the communication time by 26% to 29% compared to state-of-the-art compute clusters under representative collective operations.

Wu, Zhenguo (ORCID:0000000322847985)↗
DOE OSTI2019

Harmony: A Harness Monitoring System for the Oak Ridge Leadership Computing Facility

Acceptance of a new system requires extensive testing and is often comprised of hundreds of tests. Summit, the latest flagship super-computer at the Oak Ridge Leadership Computing Facility (OLCF), and the number one system in the November 2018 Top500 list [2], completed its acceptance testing in 2018. To execute acceptance, the acceptance test (AT) team utilizes the OLCF test harness, a tool developed at the OLCF that automates the launch and verification of all acceptance tests. Acceptance requires analysis of test results and classification of all test failures. The sheer number of tests involved makes performing these tasks challenging. To complete these tasks more efficiently, in addition to lessen the personnel burden during acceptance testing, we developed a monitoring system for the OLCF test harness called Harmony.

Kuchta, Cameron↗