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

高效且可扩展的计算系统对于科学发现和技术创新至关重要，而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。当今的 HPC 系统由数百万个组件组成，功耗已经达到兆瓦。为了满足关键任务应用对性能的不断要求，未来的系统将包含更多的组件并消耗更多的功率。为了解决扩展性能和限制功耗之间的矛盾需求，本研究开发了在受功率预算限制的新兴和未来计算机系统上实现高效和可扩展计算的支持技术。所提出的受功率限制的 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。这项研究针对由功耗感知多核 CPU 和众核加速器组成的新兴异构 HPC 集群，研究如何利用所有可用功率来最大限度地提高各种应用的组件、节点和集群级别的性能和能效。具体来说，本研究（1）为功率有限多核计算设计了一种新颖的应用感知跨组件调度系统，（2）为功率有限异构计算创建了协作混合计算框架，（3）开发了分析模型和技术来支持大规模功率有限计算。这项研究的完成促进了新颖的系统和软件设计，可以有效地利用每一瓦的计算功率；由此产生的分析模型构成了设计未来 HPC 系统、架构和构建模块的理论基础。该项目整合了教育部分，让研究生和本科生参与创新 HPC 研究，并扩大代表性不足的学生和 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.