Dynamic Performance Prediction For Scalable Parallel Computing
J Clement Mark, J Quinn Michael · 1995
Performance prediction is necessary in order to deal with multi-dimensional performance effects on parallel systems. The compiler-generated analytical model developed in this paper accounts for the effects of cache behavior, CPU execution time and message passing overhead for real programs written in high level data-parallel languages. The performance prediction technique is shown to be effective in analyzing several non-trivial applications as the problem size and number of processors vary. We leverage technology from the maple symbolic manipulation system and the S-PLUS statistical package in order to present users with critical performance information necessary for performance devugging, architectural enhancement and procurement of parallel systems. The usability of these results is improved through specifying confidence intervals as well as predicted execution times for parallel applications. Cost optimal analysis techniques are also developed using the symbolic equations for execution time.