Stochastic Modeling of Scaled Parallel Programs

Testingthe performance scalabilityof parallelprograms 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 algorithmicchoices under different problem and system scenarios. Hence, scalability analysis is oftenreserved to existing(and available)parallelmachines 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 wh...

• Type: Conference
• Year: 1994
• Where: ICPADS
• Authors: Allen D. Malony, Vassilis Mertsiotakis, Andreas Quick
• Keywords: Distributed And Parallel Computing, ICPADS 1994, Parallel Program, Scaled Parallel Programs, Stochastic Modeling