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

高效率和可扩展的计算系统对于科学发现和技术创新至关重要，这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要求以及限制组件和服务器机房功率密度的必要性的限制。包括数百万个组件，今天？的HPC系统已经消耗了兆瓦级的功率;为了满足任务关键型应用程序对性能的无限需求，未来的系统将包含更多的组件，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，本研究开发了使能技术，在新兴的和未来的计算机系统上进行高效和可扩展的计算，所提出的功率受限HPC方法将功率视为稀缺资源，并利用硬件超量配置来在功率预算内扩展性能。针对新兴的异构HPC集群包括功耗感知的多核CPU和众核加速器，本研究研究如何利用所有可用的电源，以最大限度地提高性能和电源效率在组件，节点和集群级别的广泛应用。具体而言，本研究（1）设计了一个新的应用程序感知的跨组件调度系统的功率受限的多核计算，（2）创建一个合作的混合计算框架的功率受限的异构计算，（3）开发分析模型和技术，以支持大规模的功率受限的计算。这项研究的完成促进了新的系统和软件设计，有效地利用每瓦计算功率;由此产生的分析模型构成了设计未来HPC系统，架构和构建块的理论基础。该项目整合了教育组件，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。

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.