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

该项目开发了一种系统，可以提高大量处理器（多达数万或数十万）的并行效率，而无需大规模运行程序。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以及输入变量（包括所需的处理器数量p）作为输入。仅在少于p个处理器上执行时，MPI-PPA将生成一个程序阶段列表，这些阶段预计将实现较差的可扩展性，允许程序员快速解决并可能重新实现这些阶段-以及整个程序运行的预测。MPI-PPA使用统计回归来开发可用于任意数量处理器的预测函数，从而进行这些预测。MPI-PPA不需要很好的程序理解能力，考虑到计算科学家通常是他们科学领域的专家，而不是计算机科学的专家，这是一个重要的方面。MPI-PPA的方法严重依赖于统计技术，因此该项目的工作将是计算机科学（PI）和统计学（co-PI）之间的跨学科工作。MPI-PPA将通过使用基准套件（如NAS和ASCI代码）以及国家实验室感兴趣的大型应用程序（如Paradis和Raptor）来验证。这项工作的广泛影响是多方面的。首先，MPI-PPA将有利于计算科学家和集群管理员。其中的好处包括一个简单而快速的性能调优系统，提高整体集群效率，减少单个应用程序的响应时间。该项目开发的技术将以性能调优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开放。其次，通过乔治亚大学的统计咨询中心，将促进统计学和计算机科学之间更多的跨学科互动。第三，将继续努力从莫尔豪斯大学（Morehouse University）等该地区历史悠久的黑人学院和大学招收学生。

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.