Simulating Large Scale Parallel Applications using Statistical Models for Sequential Execution Blocks
International Conference on Parallel and Distributed Systems (ICPADS) 2010
Publication Type: Paper
Repository URL: 201005_BigSimNAMD
Abstract
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.
TextRef
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)
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