Practical Performance Prediction for Parallel Programs*

Thomas J. LeBlanc

Department of Computer Science, University of Rochester

Abstract

This talk describes a toolkit for performance tuning and prediction that bridges the gap between the predictive power of scalability analysis and the accuracy of runtime measurements. The Lost Cycles Toolkit automates the process of constructing a performance model for a parallel application by using a priori knowledge of the sources and characteristics of overhead in parallel systems to guide and constrain the modeling process. The toolkit integrates empirical model-building techniques from statistics with measurement and modeling techniques for parallel programs to automatically generate accurate models of application performance. The talk will describe the various parts of the toolkit, and present several examples to illustrate its use in practical performance prediction.

* This talk represents joint work with Mark Crovella (now at Boston University) and Wagner Meira, Jr.