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

CSR：小型：协作研究：EEDAG：探索异构多核系统的节能并行任务生成和调度

• 批准号: 1118043
• 负责人: Ziliang Zong
• 金额: $ 5.67万
• 依托单位: Texas State University - San Marcos
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118043&HistoricalAwards=false
• 关键词: CSR; Small; Collaborative; Research; EEDAG

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.

该项目致力于优化并行算法执行中的能源效率。在下一代超级计算机上运行大规模并行应用时，高能耗是一个突出的限制，这些超级计算机包含异构多核处理器和互连，从而促使人们重新思考传统的方法来建模，设计和调度并行任务，并考虑能源效率。在这项合作研究中，该团队探索了高能效的并行任务设计、调度和实现，并开发了一个功率分析工具（PowerPack），可以测量不同计算组件的分解运行时功耗（如处理器、内存、网络和磁盘）。研究结果将通过维护一个活跃的项目网站，发表同行评审的期刊和会议论文，向其他研究人员提供代码，并在专业会议上展示研究结果。研究成果的可用性将为其他研究人员提供充分的机会，进一步研究并行应用程序的能源效率。通过德克萨斯州立大学的合作？圣马科斯、科罗拉多矿业学院和马奎特大学的PI通过让研究生和本科生接触高性能计算领域的技术基础，特别是节能计算领域的技术基础，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广泛传播拟议的节能并行任务设计和调度技术以及开发的功率分析工具包。