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Collaborative Research: CSR: Medium: Fortuna: Characterizing and Harnessing Performance Variability in Accelerator-rich Clusters

Collaborative Research: CSR: Medium: Fortuna: Characterizing and Harnessing Performance Variability in Accelerator-rich Clusters
合作研究:CSR:Medium:Fortuna:表征和利用富含加速器的集群中的性能变异性
批准号:
2312688
负责人:
Shivaram Venkataraman
金额:
$66.69万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-10-01 至 2026-09-30

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中文摘要
翻译
大型计算集群,包括数据中心和超级计算机,用于包括科学计算和机器学习在内的各种应用。现代计算集群通常使用专门的加速器硬件来加速计算。加速器丰富的集群运营商的目标是在集群的所有用户之间拥有高资源利用率。然而,由于加速器之间的性能差异,这些系统通常未得到充分利用;也就是说,即使相同的应用程序在相同类型的加速器上运行,应用程序的性能在加速器之间也会有所不同。这项提议将开发ForTuna,这是一套工具,可供集群运营商和研究人员用来表征和利用加速器之间的可变性。首先,福图纳将使用新的方法来表征各种加速器硬件存在多大的性能差异。其次,《财富》杂志将确定哪些应用程序更有可能受到性能变化的影响。最后,Fortuna将包括新的调度机制,可以使用可变性测量和应用程序知识来提高利用率。拟议研究的广泛影响包括算法和工具的开源实施,这些算法和工具将适用于许多大规模集群,并为更广泛的行业采用奠定基础。该项目还将根据作为提案一部分开发的工具,利用不同的硬件和软件创建关于系统设计原理的课程模块。这将教会下一代学生如何设计硬件和软件,以提高未来系统的利用率。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Large computing clusters, including data centers and supercomputers, are used for a variety of applications including scientific computations and machine learning. Modern compute clusters typically use specialized accelerator hardware to speed up computations. Operators of accelerator-rich clusters aim to have high resource utilization across all users of the cluster. However, these systems are often under-utilized due to performance variability across accelerators; that is, application performance varies across accelerators even when the same application is run on the same type of accelerator. This proposal will develop Fortuna, a set of tools that can be used by cluster operators and researchers to characterize and harness variability across accelerators. First, Fortuna will use new methodologies to characterize how much performance variability exists across a wide range of accelerator hardware. Second, Fortuna will identify which applications are more likely to suffer from performance variability. Finally, Fortuna will include new scheduling mechanisms that can use variability measurements and knowledge about applications to improve utilization.Broader impacts of the proposed research include open-source implementations of algorithms and tools, which will be applicable to many large-scale clusters and lay the groundwork for wider industry adoption. The project will also create course modules on system design principles with heterogeneous hardware and software, based on the tools developed as a part of the proposal. This will teach the next generation of students how to design hardware and software to improve utilization of future systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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CAREER: Resource Efficient Systems for Machine Learning on Structured Data
  • 批准号:
    2237306
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $67.61万
  • 财政年份:
    2023
  • 负责人:
    Shivaram Venkataraman
  • 依托单位:
Collaborative Research: Frameworks: Diamond: Democratizing Large Neural Network Model Training for Science
  • 批准号:
    2311767
  • 项目类别:
    Standard Grant
  • 资助金额:
    $75.0万
  • 财政年份:
    2023
  • 负责人:
    Shivaram Venkataraman
  • 依托单位:
III: Small: A New Machine Learning Approach for Improved Entity Identification
  • 批准号:
    1815538
  • 项目类别:
    Standard Grant
  • 资助金额:
    $32.04万
  • 财政年份:
    2018
  • 负责人:
    Shivaram Venkataraman
  • 依托单位:
国内基金
海外基金
Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    SATOSHI NAWATA
  • 依托单位:
Cell Research
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