Abstract

Parallel 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 runtime 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
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.