CSR: Rethinking System Software for Overprovisioned, High-Performance Computing Systems

CSR：重新思考用于过度配置的高性能计算系统的系统软件

• 批准号: 1526015
• 负责人: David Lowenthal
• 金额: $ 49万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1526015&HistoricalAwards=false
• 关键词: CSR; Rethinking; System; Software; Overprovisioned

Currently, the high-performance computing (HPC) community is focused on achieving exaflop performance, which is about a 30-fold improvement from the performance of the best supercomputer in the world today. Because of practical, financial, and environmental concerns, the Department of Energy is setting a power limit for achieving an exaflop at 20 megawatts. As today's top machines generally consume between five and 20 megawatts---and yet are an order of magnitude or more away from the exaflop performance target, significant hardware and software advances in HPC systems are necessary. One way to improve hardware is to use overprovisioned systems, which contain more machines than can be fully powered simultaneously. While overprovisioned systems have the potential to significantly improve power and performance, software will need to be redesigned to support such systems.The focus of this proposal is to design and implement software infrastructure that will support overprovisioned systems. The key advance in the infrastructure is support of system-wide optimizations, i.e., optimizations that span multiple applications. This is in stark contrast to the current focus in HPC systems of optimizing on a per-application basis. The developed software will consist of a job profiler, a scheduler that performs analysis on multiple jobs at a time, and a cluster-wide run-time system that jointly optimizes multiple applications based on the output of the scheduler analysis.Achieving exascale computing is an important national priority and will impact many critical application domains, such as climate/weather, renewable energy, nuclear energy, materials science, and national security. The work described here will improve whole-system performance on power-constrained HPC systems, which is one important step towards the exascale goal. The project plans to transfer technology resulting from this research in the form of the proposed software stack via longstanding collaborations with several national laboratories.

目前，高性能计算 (HPC) 社区致力于实现 exaflop 性能，这比当今世界上最好的超级计算机的性能提高了约 30 倍。出于实际、财务和环境方面的考虑，能源部将实现百亿亿次运算的功率限制设定为 20 兆瓦。 由于当今顶级机器的功耗通常在 5 到 20 兆瓦之间，但距离百亿亿次浮点运算性能目标还差一个数量级或更多，因此 HPC 系统的硬件和软件方面需要取得重大进展。 改进硬件的一种方法是使用过度配置的系统，其中包含的机器数量多于能够同时完全供电的机器数量。 虽然过度配置的系统有可能显着提高功率和性能，但需要重新设计软件以支持此类系统。该提案的重点是设计和实施支持过度配置系统的软件基础设施。 基础设施的关键进步是支持系统范围的优化，即跨多个应用程序的优化。这与 HPC 系统当前关注的基于每个应用程序的优化形成鲜明对比。 开发的软件将包括一个作业分析器、一个一次对多个作业进行分析的调度程序，以及一个集群范围的运行时系统，该系统根据调度程序分析的输出联合优化多个应用程序。重要的国家优先事项，并将影响许多关键应用领域，例如气候/天气、可再生能源、核能、材料科学和国家安全。 这里描述的工作将提高功率受限的 HPC 系统的整个系统性能，这是实现百亿亿次目标的重要一步。 该项目计划通过与几个国家实验室的长期合作，以拟议的软件堆栈的形式转让这项研究产生的技术。