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 deployed 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.

Gengbin Zheng, Gagan Gupta, Eric Bohm, Isaac Dooley, and Laxmikant V. Kale, "Simulating Large Scale Parallel Applications using Statistical Models for Sequential Execution Blocks",  in the Proceedings of the 16th International Conference on Parallel and Distributed Systems (ICPADS 2010)

@inproceedings{BigSimSEB10,
  author	= "Gengbin Zheng and Gagan Gupta and Eric Bohm and Isaac Dooley and Laxmikant V. Kale",
  title		= "{Simulating Large Scale Parallel Applications using Statistical Models for Sequential Execution Blocks}",
  booktitle     = "Proceedings of the 16th International Conference on Parallel and Distributed Systems (ICPADS 2010)",
  month		= "December",
  year		= "2010",
  number	= "10-15",
  address       = "Shanghai, China",
}