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）社区的重点是实现Exaflop性能，这比当今世界上最好的超级计算机的性能提高了30倍。由于实用，财务和环境问题，能源部正在为在20兆瓦时获得Exaflop的功率限制。 由于当今的顶级机器通常消耗五到20兆瓦 - 但距Exaflop性能目标的数量级或更大的阶段，因此需要在HPC系统中进行重大硬件和软件进步。 改进硬件的一种方法是使用过度配置的系统，该系统包含的机器比同时完全供电的机器更多。 虽然规模过高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。该建议的重点是设计和实施将支持过度投放系统的软件基础架构。 基础架构的关键进步是支持全系统范围的优化，即跨越多个应用程序的优化。这与HPC系统的当前重点是每次申请的优化的焦点。 开发的软件将由一个工作探索器组成，该调度员一次对多个工作进行分析，以及一个群集的运行时系统，该系统可以根据调度程序分析的输出共同优化多个应用程序。ACHIEVINGEXASCALE COMPUTING是重要的国家优先级，它将影响许多关键的应用领域，例如气候/天气，可再生能源，核能，核能，核能，材料，材料，材料科学和国家安全，材料科学，国家安全，国家安全，国家安全和国家安全。 这里描述的工作将改善功率受限的HPC系统上的全系统性能，这是朝着Exascale目标迈出的重要一步。 该项目计划通过与几个国家实验室的长期合作，以拟议软件堆栈的形式转移这项研究的技术。