II-New: Infrastructure for Energy-Aware High Performance Computing (HPC) and Data Analytics on Heterogeneous Systems

II-新:异构系统上的能源感知高性能计算 (HPC) 和数据分析基础设施

基本信息

  • 批准号:
    1513120
  • 负责人:
  • 金额:
    $ 89.87万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2015
  • 资助国家:
    美国
  • 起止时间:
    2015-07-01 至 2019-06-30
  • 项目状态:
    已结题

项目摘要

The project builds a comprehensive research infrastructure to meet the needs of a large research team at the Ohio State University allowing experimental research in a number of computer science areas including high performance computing, data analytics, storage, and virtualization. The project will lead to significant advances in many computer science areas, and its impact will be enhanced through active dissemination of software from the investigators. The project will contribute substantially to human resource development, education in computer science increasing diversity in related areas. More specifically, researchers will acquire and deploy an infrastructure that includes three types of accelerators, conventional as well as energy-efficient nodes, large main memory including Solid State Drive (SSD) on a subset of nodes, and hardware for fine-grained power measurements. The requested resources will allow experimentation with a number of popular or emerging cluster configurations that address needs of a variety of compute-intensive and/or data-intensive workloads. Such an internally hosted and reconfigurable cluster will also allow power measurements, voltage margin reduction experiments, and failure detection and recovery studies in the presence of physical failures - none of which is typically feasible at national supercomputing and cloud infrastructures. The infrastructure is motivated by the multiple paradigm shifts that high performance computing is presently undergoing. They include increasing use of accelerators or coprocessors, increased criticality of energy and resilience beyond performance, synergy with data analytic applications, and popularity of massive main memory or SSD technologies. These trends provide opportunities and challenges for a variety of scientific, medical, and enterprise applications to be explored in this project.
该项目建立了一个全面的研究基础设施,以满足俄亥俄州州立大学的大型研究团队的需求,允许在许多计算机科学领域进行实验研究,包括高性能计算,数据分析,存储和虚拟化。该项目将导致在许多计算机科学领域取得重大进展,并将通过积极传播调查人员的软件来加强其影响。该项目将大大有助于人力资源开发、计算机科学教育和相关领域的多样化。 更具体地说,研究人员将获得和部署一个基础设施,其中包括三种类型的加速器,传统和节能节点,大型主内存,包括节点子集上的固态驱动器(SSD),以及用于细粒度功率测量的硬件。所请求的资源将允许对一些流行的或新兴的群集配置进行试验,以满足各种计算密集型和/或数据密集型工作负载的需求。 这种内部托管和可重新配置的集群还将允许功率测量、电压裕度降低实验以及在存在物理故障的情况下的故障检测和恢复研究-这些在国家超级计算和云基础设施中通常都不可行。高性能计算目前正在经历的多重范式转变推动了基础设施的发展。其中包括加速器或协处理器的使用越来越多,能源和性能以外的弹性的关键性增加,与数据分析应用程序的协同作用,以及大容量主内存或SSD技术的普及。这些趋势为本项目中要探索的各种科学、医疗和企业应用提供了机遇和挑战。

项目成果

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Gagan Agrawal其他文献

MMIS-07, 08: Mining Multiple Information Sources Workshop Report
MMIS-07, 08:挖掘多信息源研讨会报告
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    朱兴全;Gagan Agrawal;Yuri Breitbart;Ruoming Jin
  • 通讯作者:
    Ruoming Jin
Middleware for data mining applications on clusters and grids
  • DOI:
    10.1016/j.jpdc.2007.06.007
  • 发表时间:
    2008-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Leonid Glimcher;Ruoming Jin;Gagan Agrawal
  • 通讯作者:
    Gagan Agrawal
<strong>POSTER:</strong> MDS-044 Cancer Disparities in Survival of Patients With Hematologic Malignancies in the Context of Social Determinants of Health: A Systematic Review
  • DOI:
    10.1016/s2152-2650(23)00577-3
  • 发表时间:
    2023-09-01
  • 期刊:
  • 影响因子:
  • 作者:
    Marisol Miranda-Galvis;Kellen Tjioe;Andrew Balas;Gagan Agrawal;Jorge Cortes
  • 通讯作者:
    Jorge Cortes
Organizing Records for Retrieval in Multi-Dimensional Range Searchable Encryption
多维范围可搜索加密中组织检索记录
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Mahdieh Heidaripour;Ladan Kian;Maryam Rezapour;Mark Holcomb;Benjamin Fuller;Gagan Agrawal;Hoda Maleki
  • 通讯作者:
    Hoda Maleki
The interaction between social determinants of health and cervical cancer survival: A systematic review
健康的社会决定因素与宫颈癌生存之间的相互作用:系统评价
  • DOI:
    10.1016/j.ygyno.2023.12.020
  • 发表时间:
    2024-02-01
  • 期刊:
  • 影响因子:
    4.100
  • 作者:
    Kellen Cristine Tjioe;Marisol Miranda-Galvis;Marian Symmes Johnson;Gagan Agrawal;E. Andrew Balas;Jorge E. Cortes
  • 通讯作者:
    Jorge E. Cortes

Gagan Agrawal的其他文献

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{{ truncateString('Gagan Agrawal', 18)}}的其他基金

Collaborative Research: CNS Core: Small: A Compilation System for Mapping Deep Learning Models to Tensorized Instructions (DELITE)
合作研究:CNS Core:Small:将深度学习模型映射到张量化指令的编译系统(DELITE)
  • 批准号:
    2230945
  • 财政年份:
    2023
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
Collaborative Research: CNS Core: Small: A Compilation System for Mapping Deep Learning Models to Tensorized Instructions (DELITE)
合作研究:CNS Core:Small:将深度学习模型映射到张量化指令的编译系统(DELITE)
  • 批准号:
    2341378
  • 财政年份:
    2023
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
OAC Core: SHF: SMALL: ICURE -- In-situ Analytics with Compressed or Summary Representations for Extreme-Scale Architectures
OAC 核心:SHF:SMALL:ICURE——针对超大规模架构的压缩或摘要表示的原位分析
  • 批准号:
    2333899
  • 财政年份:
    2023
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
SHF: Small: K-Way Speculation for Mapping Applications with Dependencies on Modern HPC Systems
SHF:小型:依赖现代 HPC 系统的地图应用程序的 K-Way 推测
  • 批准号:
    2334273
  • 财政年份:
    2023
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF:SMALL: Compile-Parallelize-Schedule-Retarget-Repeat (EASER) Paradigm for Dealing with Extreme Heterogeneity
合作研究:SHF:SMALL:处理极端异构性的编译-并行化-调度-重定向-重复 (EASER) 范式
  • 批准号:
    2333895
  • 财政年份:
    2023
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF:SMALL: Compile-Parallelize-Schedule-Retarget-Repeat (EASER) Paradigm for Dealing with Extreme Heterogeneity
合作研究:SHF:SMALL:处理极端异构性的编译-并行化-调度-重定向-重复 (EASER) 范式
  • 批准号:
    2146852
  • 财政年份:
    2022
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
OAC Core: SHF: SMALL: ICURE -- In-situ Analytics with Compressed or Summary Representations for Extreme-Scale Architectures
OAC 核心:SHF:SMALL:ICURE——针对超大规模架构的压缩或摘要表示的原位分析
  • 批准号:
    2007775
  • 财政年份:
    2020
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
OAC Core: SHF: SMALL: ICURE -- In-situ Analytics with Compressed or Summary Representations for Extreme-Scale Architectures
OAC 核心:SHF:SMALL:ICURE——针对超大规模架构的压缩或摘要表示的原位分析
  • 批准号:
    2034850
  • 财政年份:
    2020
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
SHF: Small: K-Way Speculation for Mapping Applications with Dependencies on Modern HPC Systems
SHF:小型:依赖于现代 HPC 系统的地图应用程序的 K-Way 推测
  • 批准号:
    2007793
  • 财政年份:
    2020
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant
SI2-SSE: Collaborative Research: Software Elements for Transfer and Analysis of Large-Scale Scientific Data
SI2-SSE:协作研究:用于大规模科学数据传输和分析的软件元素
  • 批准号:
    1339757
  • 财政年份:
    2013
  • 资助金额:
    $ 89.87万
  • 项目类别:
    Standard Grant

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