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

高性能计算（HPC）正在迅速发展，到2021年将达到前所未有的百亿亿次浮点运算水平，届时首批百亿亿次系统将为科学生产做好准备。尽管在不允许其他用户访问系统的维护窗口期间，通过简单的基准测试获得了峰值性能，但由于存储和网络上的应用程序内部或应用程序之间的干扰，应用程序通常会受到性能变化的影响。其结果是较低的系统利用率和较长的应用程序洞察时间。为了应对这一挑战，该项目旨在开发内存和输入/输出（I/O）方面的新方法，以显著减少大型科学应用的性能变化。该项目提供综合研究和教育活动，以培养高性能计算领域的下一代计算机研究人员和工程师，特别是那些来自代表性不足的群体的研究人员和工程师，以加强美国在计算科学和工程方面的竞争力。该项目旨在解决高性能计算系统的性能变化问题，在整个系统堆栈中使用一种新颖的以应用程序为中心的方法。为了解决日益增加的资源争用问题，设计了选择性提示共享方案来降低整体性能变化，并开发了集群分区技术来调节提示共享的规模。此外，还引入了反馈机制，根据性能变化减少的程度来调整提示流量。基于内存访问相似度，将具有高相似度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重路由方案，其中I/O流量重路由不仅基于干扰概况，而且还基于下游数据分析的要求。特别是，通过调整高性能计算应用程序的保真度来应对性能变化的误差有界粗化技术进行了探索。该项目的综合研究活动将显著提高对大型计算科学和工程应用中管理性能变化的理解和方法。该奖项反映了美国国家科学基金会的法定使命，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。

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.