CSR-PSCE, SM: MPI-PPA: Improving Efficiency of Large-Scale Clusters Through Statistical Performance Prediction

CSR-PSCE、SM：MPI-PPA：通过统计性能预测提高大规模集群的效率

• 批准号: 0936251
• 负责人: David Lowenthal
• 金额: $ 30.5万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0936251&HistoricalAwards=false
• 关键词: CSR; PSCE; SM; MPI; PPA

This project develops a system that improves parallel efficiency on large numbers of processors - up to tens or hundreds of thousands - without running a program at scale. This system is called MPI-PPA: MPI Performance Prediction and Advisement. MPI-PPA takes as input a scientific computing application along with the input variables, including the desired number of processors, p. With executions on fewer than p processors only - so that these executions will occur quickly - MPI-PPA will produce a list of program phases that are predicted to achieve poor scalability, allowing the programmer to quickly address and possibly re-implement these phases - as well as a prediction for the entire program run.MPI-PPA makes these predictions using statistical regression to develop a prediction function that can be used with any number of processors. MPI-PPA will not require significant program comprehension, an important aspect when considering that computational scientists are typically experts in their scientific domain and not in computer science. The approach of MPI-PPA involves heavy reliance on statistical techniques, so the work in this project will be interdisciplinary between computer science (the PI) and statistics (the co-PI). MPI-PPA will be validated by using benchmark suites such as NAS and ASCI codes, along with large-scale applications - such as Paradis and Raptor - that are of interest to national labs.The broader impact of this work is multifold. First, MPI-PPA will be beneficial for computational scientists as well as cluster administrators. Among the benefits will be a simple and fast performance tuning system, an increase in overall cluster efficiency, and a reduction in response times for individual applications. The technology developed in this project will be transferred, in the form of performance tuning and prediction software, and made available to the public through cooperation with Lawrence Livermore National Laboratory. Second, more interdisciplinary interaction between statistics and computer science will be fostered through the supervised statistical consulting center at the University of Georgia. Third, efforts will continue recruiting students from strong historically black colleges and universities in the area, such as Morehouse University.

这个项目开发了一个系统，可以在大量处理器上提高并行效率-多达数万或数十万-而不需要大规模运行程序。这个系统被称为MPI-PPA：MPI性能预测和建议。MPI-PPA将科学计算应用程序连同包括所需处理器数量在内的输入变量一起作为输入。MPI-PPA只在少于p个处理器上执行--以便这些执行将快速发生--MPI-PPA将产生一个程序阶段列表，这些阶段被预测为实现较差的可伸缩性，从而允许程序员快速处理并可能重新实现这些阶段-以及对整个程序运行的预测。MPI-PPA将不需要大量的程序理解，这是一个重要的方面，因为考虑到计算科学家通常是其科学领域的专家，而不是计算机科学。MPI-PPA的方法涉及到对统计技术的严重依赖，因此本项目的工作将是计算机科学(PI)和统计学(co-PI)之间的交叉学科。MPI-PPA将通过使用NAS和ASCI代码等基准套件以及国家实验室感兴趣的大规模应用程序(如Paradis和Raptor)进行验证。这项工作的更广泛影响是多方面的。首先，MPI-PPA对计算科学家和集群管理员都是有益的。其中的好处包括简单快速的性能调优系统、整体集群效率的提高以及单个应用程序的响应时间缩短。该项目开发的技术将以性能调整和预测软件的形式转让，并通过与劳伦斯·利弗莫尔国家实验室的合作向公众提供。其次，将通过佐治亚大学受监督的统计咨询中心促进统计学和计算机科学之间更多的跨学科互动。第三，将继续努力从该地区历史上实力雄厚的黑人学院和大学招生，如莫尔豪斯大学。