Simulating large scale parallel applications using statistical models for sequential execution blocks

Predicting sequential execution blocks of a large scale parallel application is an essential part of accurate prediction of the overall performance of the application. When simulating a future machine that is not yet fabricated, or a prototype system only available at a small scale, it becomes a significant challenge. Using hardware simulators may not be feasible due to excessively slowed down execution times and insufficient resources. These challenging issues become increasingly difficult in proportion to scale of the simulation. In this paper, we propose an approach based on statistical models to accurately predict the performance of the sequential execution blocks that comprise a parallel application. We de-ployed these techniques in a trace-driven simulation framework to capture both the detailed behavior of the application as well as the overall predicted performance. The technique is validated using both synthetic benchmarks and the NAMD application. Index Terms—parallel simulator, performance prediction, trace-driven, machine learning, statistical model I