CAREER: Cross-Layer Power-Bounded High Performance Computing on Emerging and Future Heterogeneous Computer Clusters

职业：新兴和未来异构计算机集群上的跨层功率受限高性能计算

• 批准号: 1453775
• 负责人: Rong Ge
• 金额: $ 45.35万
• 依托单位: Marquette University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-02-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453775&HistoricalAwards=false
• 关键词: CAREER; Cross; Layer; Power; Bounded

Highly efficient and scalable computing systems are crucial to scientific discovery and technology innovation critical to national security and human society. However, the scalability of HPC systems is increasingly constrained by the power requirement and the necessity to limit the power density of components and server rooms. Comprising millions of components, today?s HPC systems already consume megawatts of power; to meet an insatiate demand for performance from mission-critical applications, future systems will consist of even more components and consume more power. To resolve the conflicting needs of scaling performance and limiting power, this research develops enabling technology for efficient and scalable computing on emerging and future computer systems bounded by power budgets.The proposed power-bounded HPC approach recognizes power as a scarce resource and exploits hardware overprovisioning to scale performance within a power budget. Targeting at emerging heterogeneous HPC clusters comprising power-aware multicore CPUs and manycore accelerators, this research studies how to utilize all available power to maximize performance and power efficiency at component, node, and cluster levels for a wide range of applications. Specifically, this research (1) designs a novel application-aware cross-component scheduling system for power-bounded multicore computing, (2) creates a cooperative hybrid computing framework for power-bounded heterogeneous computing, and (3) develops analytical models and techniques to support large scale power-bounded computing. The completion of this research promotes novel system and software designs that efficiently utilize every watt of power on computation; the resulting analytical models form the theoretical foundation for designing future HPC systems, architectures, and building blocks. This project integrates educational components that engage graduate and undergraduate students in innovative HPC research, and broaden the participation of underrepresented and K-12 students.

高效且可扩展的计算系统对于对国家安全和人类社会至关重要的科学发现和技术创新至关重要。但是，HPC系统的可伸缩性越来越受到功率需求以及限制组件和服务器室功率密度的必要性的限制。如今，由数百万个组件组成，HPC系统已经消耗了兆瓦的力量。为了满足关键任务应用程序对绩效的不良需求，未来的系统将由更多的组件组成，并消耗更多的功率。为了解决缩放性能和限制能力的冲突需求，这项研究开发了能够在由功率预算界限的新兴和未来计算机系统上进行有效且可扩展的计算的技术。拟议中的功率为功率的HPC方法将功率识别为稀缺的资源，并利用硬件过度承认在电力预算中缩放性能。该研究针对新兴的异质HPC群集，其中包括功能感知的多核CPU和多核加速器，该研究研究了如何利用所有可用的功率来最大程度地提高组件，节点和集群水平的性能和功率效率，以实现广泛的应用。具体而言，这项研究（1）设计了一种用于功率结合的多核算计算的新型应用吸引的跨组件调度系统，（2）为功率结合的异质计算创建了合作的混合计算框架，（3）开发分析模型和技术以支持大型强力型计算。这项研究的完成促进了新型系统和软件设计，这些设计有效地利用了计算的每一瓦幂；由此产生的分析模型构成了设计未来HPC系统，体系结构和构件的理论基础。该项目集成了教育组成部分，这些组件使研究生和本科生参与创新的HPC研究，并扩大了代表性不足和K-12学生的参与。