Performance Evaluation of Parallel Programs — Modeling and Monitoring with the Tool PEPP

Abstract

There are many possibilities how to parallelize an algorithm and how to map a program onto a parallel or distributed system. Performance models help to predict which implementation and which mapping are the best for a given algorithm and for a given computer configuration. Stochastic graph modeling is an appropriate method, since the execution order of tasks, their runtime distribution, and branching probabilities are represented. In this paper the modeling capabilities and the analysis techniques implemented in our tool PEPP are presented. In order to obtain relevant modeling results, measured and not only estimated model parameters are needed. They can be obtained through monitoring existing programs. A method to carry out monitoring efficiently is model-driven monitoring: model tasks are mapped onto their corresponding program activities which allows systematic and automatic program instrumentation. Model parameters can easily be calculated since the set of events is the same in modeling and monitoring. A model without timing information can be enhanced to a performance model with realistic parameters.