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

点击翻译按钮获取中文摘要

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.