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.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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M. Mimi McClure
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University of Arizona
United States
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