CSR: Small: Collaborative Research: EEDAG: Exploring Energy-Efficient Parallel Tasks Generation and Scheduling for Heterogeneous Multicore Systems

该项目致力于优化并行算法执行过程中的能效。在包含异质多核处理器和互连的下一代超级计算机上运行大规模并行应用程序时，高能源成本是一个突出的制约因素，这促使人们重新考虑通过考虑能源效率来建模、设计和调度并行任务的传统方法。在这项协作研究中，该团队探索了高能效并行任务的设计、调度和实现，并开发了一个电源配置工具(PowerPack)，该工具可以测量在运行大规模并行应用程序时不同计算组件(如处理器、内存、网络和磁盘)的分解运行时功耗。研究结果将通过维护活跃的项目网站、发布同行评议的期刊和会议论文、将代码提供给其他研究人员以及在专业会议上展示研究结果来广泛传播。研究成果的可获得性将为其他研究人员进一步研究并行应用的能效提供充足的机会。通过德克萨斯州立大学的合作？在圣马科斯、科罗拉多矿业学院和马奎特大学，PI通过让研究生和本科生了解高性能计算领域的技术基础，特别是节能计算，促进教学、学习和培训。与一些大学、数据中心和国家实验室的密切伙伴关系也将促进拟议的高能效并行任务设计和调度技术以及开发的电力概况工具包的广泛传播。

This project addresses optimizing energy efficiency in the execution of parallel algorithms. High energy cost is a salient constraint when running large scale parallel applications on the next generation of supercomputers that contain heterogeneous multicore processors and interconnections, motivating a rethinking of conventional approaches to modeling, designing and scheduling parallel tasks by taking energy-efficiency into consideration. In this collaborative research, this team explores energy-efficient parallel task design, scheduling, and implementation and develops an power profiling tool (PowerPack) that can measure decomposed runtime power consumption of different computing components (e.g. processors, memory, networks and disks) when running large scale parallel applications.The results of the research will be widely disseminated by maintaining an active project website, publishing peer-reviewed journal and conference papers, making the code available to other researchers, and presenting the research results in professional meetings. The availability of the research outcomes will provide ample opportunities for other researchers to further study the energy-efficiency of parallel applications. Through the collaboration of Texas State University ? San Marcos, Colorado School of Mines, and the Marquette University, PIs promote teaching, learning, and training by exposing graduate and undergraduate students to technological underpinnings in the fields of high performance computing in general and energy-efficient computing in particular. The close partnerships with a number of universities, data centers and national laboratories will also facilitate the broad dissemination of the proposed energy-efficient parallel tasks designing and scheduling techniques as well as the developed power profiling toolkits.