II-New: Infrastructure for Energy-Aware High Performance Computing (HPC) and Data Analytics on Heterogeneous Systems

该项目建立了一个全面的研究基础设施，以满足俄亥俄州立大学一个大型研究团队的需求，允许在许多计算机科学领域进行实验研究，包括高性能计算、数据分析、存储和虚拟化。该项目将在许多计算机科学领域取得重大进展，通过积极传播研究人员的软件，其影响将得到加强。该计划将大大促进人力资源发展、计算机科学教育和相关领域的多样性。更具体地说，研究人员将获得并部署一个基础设施，其中包括三种类型的加速器，传统节点和节能节点，节点子集上的大型主存储器（包括固态硬盘（SSD）），以及用于细粒度功率测量的硬件。所请求的资源将允许对许多流行的或新兴的集群配置进行实验，以满足各种计算密集型和/或数据密集型工作负载的需求。这种内部托管和可重新配置的集群还将允许功率测量、电压裕度降低实验以及物理故障存在时的故障检测和恢复研究——这些在国家超级计算和云基础设施中通常都是不可行的。该基础设施是由高性能计算目前正在经历的多重范式转换所驱动的。它们包括越来越多地使用加速器或协处理器，提高能源的临界性和超越性能的弹性，与数据分析应用程序的协同作用，以及大规模主存储器或SSD技术的普及。这些趋势为本项目中要探索的各种科学、医学和企业应用程序提供了机遇和挑战。

The project builds a comprehensive research infrastructure to meet the needs of a large research team at the Ohio State University allowing experimental research in a number of computer science areas including high performance computing, data analytics, storage, and virtualization. The project will lead to significant advances in many computer science areas, and its impact will be enhanced through active dissemination of software from the investigators. The project will contribute substantially to human resource development, education in computer science increasing diversity in related areas. More specifically, researchers will acquire and deploy an infrastructure that includes three types of accelerators, conventional as well as energy-efficient nodes, large main memory including Solid State Drive (SSD) on a subset of nodes, and hardware for fine-grained power measurements. The requested resources will allow experimentation with a number of popular or emerging cluster configurations that address needs of a variety of compute-intensive and/or data-intensive workloads. Such an internally hosted and reconfigurable cluster will also allow power measurements, voltage margin reduction experiments, and failure detection and recovery studies in the presence of physical failures - none of which is typically feasible at national supercomputing and cloud infrastructures. The infrastructure is motivated by the multiple paradigm shifts that high performance computing is presently undergoing. They include increasing use of accelerators or coprocessors, increased criticality of energy and resilience beyond performance, synergy with data analytic applications, and popularity of massive main memory or SSD technologies. These trends provide opportunities and challenges for a variety of scientific, medical, and enterprise applications to be explored in this project.