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: University of Wisconsin-Madison
• 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
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312688&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; Medium; Fortuna

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.

大型计算集群，包括数据中心和超级计算机，用于各种应用，包括科学计算和机器学习。现代计算集群通常使用专门的加速器硬件来加速计算。加速器丰富的集群的运营商的目标是在集群的所有用户之间具有高资源利用率。然而，由于加速器之间的性能可变性，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，加速器之间的应用程序性能也会有所不同。该提案将开发福图纳，这是一套工具，可供集群运营商和研究人员用于描述和利用加速器之间的变异性。首先，福图纳将使用新的方法来表征在各种加速器硬件中存在多少性能可变性。其次，福图纳将确定哪些应用程序更有可能受到性能变化的影响。最后，福图纳将包括新的调度机制，可以使用可变性测量和应用程序的知识，以提高utility.Broader的影响，拟议的研究包括算法和工具的开源实现，这将适用于许多大规模的集群，并奠定了基础，更广泛的行业采用。该项目还将根据作为提案一部分开发的工具，创建关于使用不同硬件和软件的系统设计原则的课程模块。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。