SHF:Small: Collaborative Research: Tailoring Memory Systems for Data-Intensive HPC Applications

SHF:Small：协作研究：为数据密集型 HPC 应用定制内存系统

• 批准号: 1717660
• 负责人: Xubin He
• 金额: $ 31万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1717660&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Tailoring; SHF; Small; Collaborative; Research; Tailoring

High-performance computing is of strategic importance for computational science and engineering in the United States, and is on an accelerated path to sustaining scientific discovery at much increased flops. To advance the scientific discovery to the next level and allow new science missions to be accomplished in a timely manner, it is critical to address the memory performance holistically in high-performance computing platforms. This project provides architectural and system support and optimization for building memory systems tailored for data-intensive applications, e.g., big data analytics. This project offers research and educational opportunities for both undergraduate and graduate students, and trains a new generation of computer scientists and engineers in the area of high-performance computing. The objective of this project is to address the research challenges in building an efficient memory system by designing new techniques from several aspects. It develops a novel centralized memory refresh scheme at the cluster-level to manage memory refresh overhead, which has been increasingly performance-impacting and energy-consuming. It designs a new memory scheduling policy, taking advantages of new memory characteristics. It makes memory characteristics/peculiarities be available to the processor and operating system, so that they can make well-informed decisions to fully exploit memory performance potentials. It leverages in-memory computing to enable efficient in-situ processing. The integration of all these techniques provides a holistic solution to building an efficient memory system tailored for data-intensive applications.

高性能计算对于美国的计算科学和工程具有战略重要性，并且正处于越来越多的拖曳率上维持科学发现的加速途径。为了将科学发现提升到一个新的水平，并允许及时完成新的科学任务，在高性能计算平台中整体上解决记忆性能至关重要。该项目为用于数据密集型应用程序（例如大数据分析）量身定制的构建内存系统提供了体系结构和系统的支持和优化。该项目为大学生和研究生提供了研究和教育机会，并在高性能计算领域培训了新一代的计算机科学家和工程师。该项目的目的是通过设计来自多个方面的新技术来应对建立有效记忆系统的研究挑战。它在集群级别开发了一种新型的集中记忆刷新方案，以管理内存刷新开销，这越来越多地影响性能和消耗性能。它设计了一种新的内存调度策略，具有新的内存特征的优势。它使处理器和操作系统可以使用内存特征/特殊性，以便他们可以做出明智的决策以完全利用内存性能潜力。它利用内存计算来实现有效的原位处理。所有这些技术的集成为建立针对数据密集型应用程序量身定制的有效内存系统提供了整体解决方案。