Modeling Billion-Node Torus Networks Using Massively Parallel Discrete-Event Simulation
PADS '11 Proceedings of the 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation
Performance modeling for systematic performance tuning
State of the Practice Reports
Modeling a leadership-scale storage system
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
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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, 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. The difficulty of these challenges increases proportionally with the 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.