Performance measurement, visualization and modeling of parallel and distributed programs using the AIMS toolkit

Abstract

Abstract Writing large‐scale parallel and distributed scientific applications that make optimum use of the multiprocessor is a challenging problem. Typically, computational resources are underused due to performance failures in the application being executed. Performance‐tuning tools are essential for exposing these performance failures and for suggesting ways to improve program performance. In this paper, we first address fundamental issues in building useful performance‐tuning tools and then describe our experience with the AIMS toolkit for tuning parallel and distributed programs on a variety of platforms. AIMS supports source‐code instrumentation, run‐time monitoring, graphical execution profiles, performance indices and automated modeling techniques as ways to expose performance problems of programs. Using several examples representing a broad range of scientific applications, we illustrate AIMS' effectiveness in exposing performance problems in parallel and distributed programs.