Abstract
AbstractParallel Java environments present challenging problems for performance tools because of Java's rich language system and its multi‐level execution platform combined with the integration of native‐code application libraries and parallel run‐time software. In addition to the desire to provide robust performance measurement and analysis capabilities for the Java language itself, the coupling of different software execution contexts under a uniform performance model needs careful consideration of how events of interest are observed and how cross‐context parallel execution information is linked. This paper relates our experience in extending the TAU (Tuning and Analysis Utilities) performance system to a parallel Java environment based on mpiJava. We describe the complexities of the instrumentation model used, how performance measurements are made, and the overhead incurred. A parallel Java application simulating the game of Life is used to show the performance system's capabilities. Copyright © 2003 John Wiley & Sons, Ltd.
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