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: 2401244
• 负责人: Zhao Zhang
• 金额: $ 33.31万
• 依托单位: Rutgers University New Brunswick
• 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=2401244&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）用于包括科学计算和机器学习在内的各种应用。现代计算簇通常使用专门的加速器硬件来加快计算。富含加速器的群集的运营商旨在在集群的所有用户中具有高度的资源利用率。但是，由于加速器之间的性能变异性，这些系统通常被低估了。也就是说，即使在同一类型的加速器上运行相同的应用程序，应用程序性能也会因加速器而异。该提案将开发Fortuna，这是一套由集群操作员和研究人员使用的工具，以表征和利用加速器之间的变异性。首先，Fortuna将使用新的方法来表征各种加速器硬件的性能可变性。其次，Fortuna将确定哪些应用更可能患有性能变异性。最后，Fortuna将包括可以使用可变性测量和有关应用程序的知识来改善利用率的新调度机制。拟议研究的Boader影响包括算法和工具的开源实现，这些实施将适用于许多大型群集，并为更广泛的行业采用奠定基础。该项目还将基于提案的一部分开发的工具，创建具有异质硬件和软件的系统设计原理的课程模块。这将教导下一代学生如何设计硬件和软件以改善对未来系统的利用。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。