Collaborative Research: SHF: Small: Rethinking Performance Variation for Emerging Applications - An Application-centric and Cross-layer Approach

协作研究：SHF：小型：重新思考新兴应用程序的性能变化 - 以应用程序为中心的跨层方法

• 批准号: 2134202
• 负责人: Qing Liu
• 金额: $ 19万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134202&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Small; Rethinking

High-performance computing (HPC) is moving rapidly to the unparalleled level of exaflops in 2021, when the first exascale systems will be ready for science production. Despite the peak performance obtained by simplistic benchmarks during the maintenance window when no other users are allowed to access the system, applications routinely suffer from performance variations as a result of intra- or inter-application interference over storage and network. The consequence is the low system utilization and prolonged time to insights for applications. To address this challenge, this project aims to develop new methods in memory and input/output (I/O) that can significantly reduce the performance variation for large scientific applications. This project provides integrated research and education activities to nurture next-generation computer researchers and engineers in the area of HPC, particularly for those from under-represented groups, to strengthen the U.S. competitiveness in computational science and engineering. This project aims to address the performance variation issue on HPC systems using a novel application-centric approach across the system stack. To address increasing resource contention, a selective hint-sharing scheme is designed to reduce the overall performance variation, and a cluster-partition technique is developed to regulate the scale of hint sharing. In addition, a feedback mechanism is incorporated to adjust the hint traffic according to the degree of performance-variation reduction. Based upon memory-access similarity, memory pages or work nodes sharing high similarity are grouped together to optimize the memory-system performance. Furthermore, a rule-based I/O re-routing scheme, where I/O traffic is re-routed based upon not only the interference profile, but also the requirements of downstream data analytics. In particular, an error-bounded coarsening technique that reacts to performance variation by adjusting the fidelity of an HPC application is explored. The integrated research activities in this project will significantly improve the understanding and methods in managing performance variations for large computational science and engineering applications.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.

高性能计算（HPC）在2021年迅速移动到无与伦比的Exaflops水平，届时第一个Exascale系统将准备好科学生产。尽管在维护窗口中，当不允许其他用户访问系统时，通过简单的基准获得了峰值性能，但由于对存储和网络的内置或施法间干扰，应用程序通常会遭受性能变化。结果是系统利用率较低，并延长了用于应用程序的见解。为了应对这一挑战，该项目旨在在内存和输入/输出（I/O）中开发新的方法，以显着降低大型科学应用的性能变化。该项目提供了综合的研究和教育活动，以培养HPC领域的下一代计算机研究人员和工程师，特别是对于代表不足的群体的计算机研究人员，以增强美国在计算科学和工程方面的竞争力。该项目旨在通过整个系统堆栈中以新颖的以应用程序为中心的方法来解决HPC系统上的性能变化问题。为了解决不断增加的资源争夺，选择性提示共享方案旨在减少整体性能变化，并开发了一种群集分区技术来调节提示共享的规模。此外，还合并了一种反馈机制，以根据降低性能变化的程度来调整提示流量。基于内存访问相似性，将共享高相似性的内存页面或工作节点分组在一起以优化内存系统性能。此外，基于规则的I/O重新布置方案，其中I/O流量不仅基于干扰配置文件，而且还基于下游数据分析的要求。特别是，通过调整HPC应用程序的保真度来对性能变化做出反应的误差的粗化技术。该项目中的集成研究活动将显着提高大型计算科学和工程应用程序绩效变化方面的理解和方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响来审查标准的评估值得支持的。

项目成果

Locality-based transfer learning on compression autoencoder for efficient scientific data lossy compression

• DOI: 10.1016/j.jnca.2022.103452
• 发表时间: 2022-06
• 期刊: J. Netw. Comput. Appl.
• 作者: Nan Wang;Tong Liu;Jinzhen Wang;Qing Liu;Shakeel Alibhai;Xubin He