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: 0834356
• 负责人: David Lowenthal
• 金额: $ 32万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0834356&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.

该项目开发了一个系统，可提高大量处理器的并行效率 - 最多或数十万个，而无需大规模运行程序。该系统称为MPI-PPA：MPI性能预测和建议。 MPI-PPA作为输入作为科学计算应用以及输入变量，包括所需的处理器数量，p。 仅在少于P处理器上执行执行 - 以便这些执行将迅速发生-MPI -PPA将产生一个预测的程序阶段列表，这些阶段列表将实现可伸缩性差，从而使程序员可以快速解决这些阶段，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可以使用统计的回归来进行统计回归的预测，从而可以使用任何预测函数来制定任何预测函数。 MPI-PPA不需要重大的程序理解，这是一个重要方面，当时计算科学家通常是其科学领域而不是计算机科学领域的专家。 MPI-PPA的方法涉及对统计技术的严重依赖，因此该项目的工作将是计算机科学（PI）和统计（CO-PI）之间的跨学科。 MPI -PPA将通过使用NAS和ASCI代码等基准套件以及大规模应用程序（例如Paradis和Raptor）来验证国家实验室的大规模应用程序。这项工作的更广泛影响是多重的。 首先，MPI-PPA将对计算科学家和集群管理员有益。 在好处中，将是一个简单而快速的性能调整系统，总体集群效率的提高以及各个应用程序的响应时间减少。 该项目开发的技术将以性能调整和预测软件的形式转移，并通过与劳伦斯·利弗莫尔国家实验室合作提供向公众提供。 其次，统计学与计算机科学之间的更多跨学科互动将通过佐治亚大学的监督统计咨询中心培养。第三，努力将继续招募来自该地区庞大的历史黑人学院和大学的学生，例如莫尔豪斯大学。