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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Job Scheduling in a Heterogeneous Grid Environment

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. However, a number of major technical hurdles must be overcome before this potential can be realized. One problem that is critical to effective utilization of computational grids is the efficient scheduling of jobs. This work addresses this problem by describing and evaluating a grid scheduling architecture and three job migration algorithms. The architecture is scalable and does not assume control of local site resources. The job migration policies use the availability and performance of computer systems, the network bandwidth available between systems, and the volume of input and output data associated with each job. An extensive performance comparison is presented using real workloads from leading computational centers. The results, based on several key metrics, demonstrate that the performance of our distributed migration algorithms is significantly greater than that of a local scheduling framework and comparable to a non-scalable global scheduling approach.

Shan, Hong-Zhang↗
DOE OSTI2025

I/O in Machine Learning Applications on HPC Systems: A 360-degree Survey

Growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. This demand for speed has prompted the use of high performance computing (HPC) systems that excel in managing distributed workloads. Because data is the main fuel for AI applications, the performance of the storage and I/O subsystem of HPC systems is critical. In the past, HPC applications accessed large portions of data written by simulations or experiments or ingested data for visualizations or analysis tasks. ML workloads perform small reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to modern parallel storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We define the scope of the survey, provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during offline data preparation, training, and inference, and explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature. Lastly, we seek to expose research gaps that could spawn further R&D.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Energy Performance Evaluation of Aquila's Aquarius Fixed Cold Plate Cooling System at NREL's High Performance Computing Center

In the first half of 2018, as part of a partnership with Sandia National Laboratories, Aquila installed its fixed cold plate, liquid-cooled Aquarius rack solution for high-performance computing (HPC) clustering at the National Renewable Energy Laboratory's (NREL's) Energy Systems Integration Facility (ESIF). This new fixed cold plate, warm-water cooling technology together with a manifold design provides easy access to service nodes and eliminates the need for server auxiliary fans altogether. Aquila and Sandia National Laboratories chose NREL's HPC Data Center for the initial installation and evaluation because the data center is configured for liquid cooling and has the required instrumentation to measure flow and temperature differences to facilitate testing. This paper gives an overview of the Aquarius fixed cold plate cooling technology and provides results from early energy performance evaluation testing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating quantum computing resources into scientific HPC ecosystems

Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era’s inherent external noise issues. Here, this paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Discrete Event Execution with One-Sided and Two-Sided GVT Algorithms on 216,000 Processor Cores

Global virtual time (GVT) computation is a key determinant of the efficiency and runtime dynamics of parallel discrete event simulations (PDES), especially on large-scale parallel platforms. Here, three execution modes of a generalized GVT computation algorithm are studied on high-performance parallel computing systems: (1) a synchronous GVT algorithm that affords ease of implementation, (2) an asynchronous GVT algorithm that is more complex to implement but can relieve blocking latencies, and (3) a variant of the asynchronous GVT algorithm to exploit one-sided communication in extant supercomputing platforms. Performance results are presented of implementations of these algorithms on up to 216,000 cores of a Cray XT5 system, exercised on a range of parameters: optimistic and conservative synchronization, fine- to medium-grained event computation, synthetic and non-synthetic applications, and different lookahead values. Performance of up to 54 billion events executed per second is registered. Detailed PDES-specific runtime metrics are presented to further the understanding of tightly-coupled discrete event dynamics on massively parallel platforms.

Perumalla, Kalyan S↗
DOE OSTI2020

A Quantum Analog Coprocessor for Correlated Electron Systems Simulation

Analog quantum simulation is an approach for studying physical systems that might otherwise be computationally intractable to simulate on classical high-performance computing (HPC) systems. The key idea behind analog quantum simulation is the realization of a physical system with a low-energy effective Hamiltonian that is the same as the low-energy effective Hamiltonian of some target system to be studied. Purpose-built nanoelectronic devices are a natural candidate for implementing the analog quantum simulation of strongly correlated materials that are otherwise challenging to study using classical HPC systems. However, realizing devices that are sufficiently large to study the properties of a non-trivial material system (e.g., those described by a Fermi-Hubbard model) will eventually require the fabrication, control, and measurement of at least 0(10) quantum dots, or other engineered quantum impurities. As a step toward large-scale analog or digital quantum simulation platforms based on nanoelectronic devices, we propose a new approach to analog quantum simulation that makes use of the large Hilbert space dimension of the electronic baths that are used to adjust the occupancy of one or a few engineered quantum impurities. This approach to analog quantum simulation allows us to study a wide array of quantum impurity models. We can further augment the computational power of such an approach by combining it with a classical computer to facilitate dynamical mean-field theory (DMFT) calculations. DMFT replaces the solution of a lattice impurity problem with the solution of a family of localized impurity problems with bath couplings that are adjusted to satisfy a self-consistency condition between the two models. In DMFT, the computationally challenging task is the high-accuracy solution of an instance of a quantum impurity model that is determined self-consistently in coordination with a mean-field calculation. We propose using one or a few engineered quantum impurities with adjustable couplings to baths to realize an analog quantum coprocessor that effects the solution of such a model through measurements of a physical quantum impurity, operating in coordination with a classical computer to achieve a self-consistent solution to a DMFT calculation. We focus on implementation details relevant to a number of technologies for which Sandia has design, fabrication, and measurement expertise. The primary technical advances outlined in this report concern the development of a supporting modeling capability. As with all analog quantum simulation platforms, the successful design and operation of individual devices depends critically on one's ability to predict the effective low-energy Hamiltonian governing its dynamics Our project has made this possible and lays the foundation for future experimental implementations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1994

Integration of the Response Surface Methodology with the Compromise Decision Support Problem in Developing a General Robust Design Procedure

In this paper we introduce a comprehensive and rigorous robust design procedure to overcome some limitations of the current approaches. A comprehensive approach is general enough to model the two major types of robust design applications, namely, robust design associated with the minimization of the deviation of performance caused by the deviation of noise factors (uncontrollable parameters), and robust design due to the minimization of the deviation of performance caused by the deviation of control factors (design variables). We achieve mathematical rigor by using, as a foundation, principles from the design of experiments and optimization. Specifically, we integrate the Response Surface Method (RSM) with the compromise Decision Support Problem (DSP). Our approach is especially useful for design problems where there are no closed-form solutions and system performance is computationally expensive to evaluate. The design of a solar powered irrigation system is used as an example. Our focus in this paper is on illustrating our approach rather than on the results per se.

Chen, Wei↗
DOE OSTI2021

Sandia's Liquid-Cooled Data Center Boosts Efficiency and Resiliency

The Federal Energy Management Program (FEMP) encourages federal agencies and organizations to improve data center energy efficiency, which can offer tremendous opportunities for energy and cost savings. In this success story, a novel liquid cooling system provides reliable, resilient, and energy-efficient cooling for high-performance computing (HPC) systems at Sandia National Laboratories.

data center↗
DOE OSTI2018

Computing the Properties of Hadrons, Nuclei, and Nuclear Matter from Quantum Chromodynamics, UNC-CH Final Report.

This is the final technical report for the UNC-CH component of a SciDAC3 project. This project served to coordinate the software development effort which the nuclear physics lattice QCD community is developing to ensure that lattice calculations can make optimal use of forthcoming leadership-class and dedicated hardware, including those of the national laboratories, and prepares for the exploitation of future computational resources in the exascale era. The project developed and optimized simulation software for lattice QCD calculations on leadership class computers and on GPUaccelerated heterogeneous architectures. The participating team at multiple instritutions improved and extended software libraries (Chroma, CPS, MILC, QLUA, QPhiX, QDP++, QDP-jit) by providing interfaces for heterogeneous computing environments and through the optimization of gauge field evolution algorithms and sparse matrix inverters. The former includes the development of new multi-time scale integrators and the latter focused on the development of new multi-grid and domain decomposition inverters for several QCD fermion discretization schemes. The sub-project at the University of North Carolina worked towards the development a domain specific language for lattice QCD computations with the express purpose of generating code with uncompromised performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

WinnowML: Stable feature selection for maximizing prediction accuracy of time-based system modeling

Online deep learning (ODL) has become an important methodology for modeling time-based performance of computer systems. An open problem is the intelligent selection of features from raw workload traces of computer systems. The best methods are overly sensitive to noisy data, causing frequent feature changes and re-training. Using all available features inflates training time and introduces model artifacts if some features should have been dropped. We present WinnowML, a method for automatically determining the most relevant feature subset for a predictive time-series model. WinnowML combines existing feature ranking algorithms and a history of each feature's ranking to iteratively rank a feature set to lower prediction error and maximize long term relevance. From this ranked feature set, the most relevant and stable subset is selected to train a model. Experimentally, we show how WinnowML can lower a model's mean absolute relative error up to 42% on average compared to the closest performing approach. Additionally, we lower the fluctuation in feature ranking and selection up to 65%. We also demonstrate how to combine WinnowML and a model search tool to provide improvements in performance of up to 14.5% when compared to using all the feature available.

Bel, Oceane MS↗
NASA NTRS2005

Computer Simulation Performed for Columbia Project Cooling System

This demo shows a high-fidelity simulation of the air flow in the main computer room housing the Columbia (10,024 intel titanium processors) system. The simulation asseses the performance of the cooling system and identified deficiencies, and recommended modifications to eliminate them. It used two in house software packages on NAS supercomputers: Chimera Grid tools to generate a geometric model of the computer room, OVERFLOW-2 code for fluid and thermal simulation. This state-of-the-art technology can be easily extended to provide a general capability for air flow analyses on any modern computer room. Columbia_CFD_black.tiff

Ahmad, Jasim↗
DOE OSTI2023

Analyzing File Access Patterns on Large-Scale HPC Systems: Opportunities for File Prefetching

This paper explores the potential opportunities for implementing file prefetching techniques on large-scale high-performance computing (HPC) systems. Specifically, we investigate the file access patterns of various applications across multiple scientific domains using two years' worth of Darshan I/O traces obtained from the Summit supercomputer. We identify recurring trends and patterns which indicate that prefetching can be effectively leveraged to improve data access performance on HPC systems. This study serves as a valuable reference for system architects and developers in the HPC community, providing insights into the opportunities and challenges associated with enabling file prefetching on large-scale HPC systems.

Karimi, Ahmad Maroof↗
DOE OSTI2014

Energy Performance Testing of Asetek's RackCDU System at NREL's High Performance Computing Data Center

In this study, we report on the first tests of Asetek's RackCDU direct-to-chip liquid cooling system for servers at NREL's ESIF data center. The system was simple to install on the existing servers and integrated directly into the data center's existing hydronics system. The focus of this study was to explore the total cooling energy savings and potential for waste-heat recovery of this warm-water liquid cooling system. RackCDU captured up to 64% of server heat into the liquid stream at an outlet temperature of 89 degrees F, and 48% at outlet temperatures approaching 100 degrees F. This system was designed to capture heat from the CPUs only, indicating a potential for increased heat capture if memory cooling was included. Reduced temperatures inside the servers caused all fans to reduce power to the lowest possible BIOS setting, indicating further energy savings potential if additional fan control is included. Preliminary studies manually reducing fan speed (and even removing fans) validated this potential savings but could not be optimized for these working servers. The Asetek direct-to-chip liquid cooling system has been in operation with users for 16 months with no necessary maintenance and no leaks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications

Large-scale simulations require efficient computation across the entire computing hierarchy. A challenge of the Exascale Computing Project (ECP) was to reconcile highly heterogeneous hardware with the myriad of applications that were required to run on these supercomputers. Mathematical software forms the backbone of almost all scientific applications, providing efficient abstractions and operations that are crucial to harness the performance of computing systems. Ginkgo is one such mathematical software library, nurtured by ECP, providing high-performance, user-friendly, and performance portable interfaces for applications in ECP and beyond. In this paper, we elaborate on Ginkgo’s philosophy of high-performance software that is sustainable, reproducible, and easy to use. We showcase the wide feature set of solvers and preconditioners available in Ginkgo and the central concepts involved in their design. We elaborate on four different ECP software integrations: MFEM, PeleLM + SUNDIALS, XGC, and ExaSGD that use Ginkgo to accelerate their science runs. Performance studies of different problems from these applications highlight the effectiveness of Ginkgo and the benefits incurred by these ECP applications.

Cojean, Terry↗
DOE OSTI2018

Opal: A Centralized Memory Manager for Investigating Disaggregated Memory Systems

Many modern applications have memory footprints that are increasingly large, driving system memory capacities higher and higher. Moreover, these systems are often organized where the bulk of the memory is collocated with the compute capability, which necessitates the need for message passing APIs to facilitate information sharing between compute nodes. Due to the diversity of applications that must run on High-Performance Computing (HPC) systems, the memory utilization can fluctuate wildly from one application to another. And, because memory is located in the node, maintenance can become problematic because each node must be taken offline and upgraded individually. To address these issues, vendors are exploring disaggregated, memory-centric, systems. In this type of organization, there are discrete nodes, reserved solely for memory, which are shared across many compute nodes. Due to their capacity, low-power, and non-volatility, Non-Volatile Memories (NVMs) are ideal candidates for these memory nodes. This report discusses a new component for the Structural Simulation Toolkit (SST), Opal, that can be used to study the impact of using NVMs in a disaggregated system in terms of performance, security, and memory management. This page intentionally left blank.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1996

Research on computer systems benchmarking

This grant addresses the topic of research on computer systems benchmarking and is more generally concerned with performance issues in computer systems. This report reviews work in those areas during the period of NASA support under this grant. The bulk of the work performed concerned benchmarking and analysis of CPUs, compilers, caches, and benchmark programs. The first part of this work concerned the issue of benchmark performance prediction. A new approach to benchmarking and machine characterization was reported, using a machine characterizer that measures the performance of a given system in terms of a Fortran abstract machine. Another report focused on analyzing compiler performance. The performance impact of optimization in the context of our methodology for CPU performance characterization was based on the abstract machine model. Benchmark programs are analyzed in another paper. A machine-independent model of program execution was developed to characterize both machine performance and program execution. By merging these machine and program characterizations, execution time can be estimated for arbitrary machine/program combinations. The work was continued into the domain of parallel and vector machines, including the issue of caches in vector processors and multiprocessors. All of the afore-mentioned accomplishments are more specifically summarized in this report, as well as those smaller in magnitude supported by this grant.

Smith, Alan Jay↗