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Keywords: Stochastic modeling, PEPP
Testing the performance scalability of parallel programs can be a time consuming task, involving many performance runs for different computer configurations, processor numbers, and problem sizes. Ideally, scalability issues would be addressed during parallel program design, but tools are not presently available that allow program developers to study the impact of algorithmic choices under different problem and system scenarios. Hence, scalability analysis is often reserved to existing (and available) parallel machines as well as implemented algorithms. In this paper, we propose techniques for analyzing scaled parallel programs using stochastic modeling approaches. Although allowing more generality and flexibility in analysis, stochastic modeling of large parallel programs is difficult due to solution tractability problems. We observe, however, that the complexity of parallel program models depends significantly on the type of parallel computation, and we present several computation classes where tractable, approximate graph models can be generated. Our approach is based on a parallelization description of programs to be scaled. From this description, “scaled” stochastic graph models are automatically generated. Different approximate models are used to compute lower and upper bounds of the mean runtime. We present evaluation results of several of these scaled (approximate) models and compare their accuracy and modeling expense (i.e., time to solution) with other solution methods implemented in our modeling tool PEPP. Our results indicate that accurate and efficient scalability analysis is possible using stochastic modeling together with model approximation techniques.
Created: Wed Feb 18 12:52:48 2004
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