Automatic scalability analysis of parallel programs based on modeling techniques

Abstract

When implementing parallel programs for parallel computer systems the performance scalability of these programs should be tested and analyzed on different computer configurations and problem sizes. Since a complete scalability analysis is too time consuming and is limited to only existing systems, extensions of modeling approaches can be considered for analyzing the behavior of parallel programs under different problem and system scenarios. In this paper, a method for automatic scalability analysis using modeling is presented. Initially, we identify the important problems that arise when attempting to apply modeling techniques to scalability analysis. Based on this study, we define the Parallelization Description Language (PDL) that is used to describe parallel execution attributes of a generic program workload. Based on a parallelization description, stochastic models like graph models or Petri net models can be automatically generated from a generic model to analyze performance for scaled parallel systems as well as scaled input data. The complexity of the graph models produced depends significantly on the type of parallel computation described. We present several computation classes where tractable graph models can be generated and then compare the results of these automatically scaled models with their exact solutions using the modeling tool PEPP.KeywordsGraph ModelProblem SizeParallel ProgramScalability AnalysisComputation ClassThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.