Stochastic modeling of scaled parallel programs

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