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

高效和可扩展的计算系统对于科学发现和技术创新至关重要，对国家安全和人类社会至关重要。然而，HPC系统的可扩展性越来越受到功率需求和限制组件和服务器机房功率密度的必要性的限制。由数以百万计的组件组成？今天的S高性能计算系统已经消耗了兆瓦的功率；为了满足任务关键型应用对性能的永不满足的需求，未来的系统将由更多的组件组成，并且消耗更多的功率。为了解决扩展性能和限制功率的冲突需求，该研究开发了在受限于功率预算的新兴和未来计算机系统上进行高效和可扩展计算的使能技术。所提出的受限功率HPC方法将功率视为稀缺资源，并利用硬件过度配置来在功率预算内扩展性能。针对新兴的包括节能多核CPU和多核加速器的异类HPC群集，本研究研究如何利用所有可用功率在组件、节点和集群级别针对广泛的应用程序实现性能和能效最大化。具体地说，本研究(1)设计了一种新颖的面向功耗受限多核计算的应用感知跨组件调度系统，(2)创建了面向功耗受限异质计算的协作式混合计算框架，(3)开发了支持大规模功耗受限计算的分析模型和技术。这项研究的完成促进了新的系统和软件设计，有效地利用了计算上的每一瓦特功率；由此产生的分析模型为设计未来的高性能计算系统、体系结构和构建块奠定了理论基础。该项目整合了使研究生和本科生参与创新的HPC研究的教育组成部分，并扩大了代表不足的学生和K-12学生的参与。

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.