A Performance Adviser for the Development of Parallel Programs

Abstract

The increasing complexity of parallel computing systems has brought about a crisis in parallel performance evaluation and tuning. Tools for performance measurement and visualization become necessary parts of programming environments for parallel computers. In this paper we describe a tool — which we call the Performance Adviser — that offers two different levels of performance information (high level and low level), guides the users to specific problem areas in the source code, and suggests actions to improve the performance of their parallel programs. Working behind the Performance Adviser is an expert system which derives high level concepts from the source code and a critical path analysis metric which derives low level performance information from the performance data collected in the execution of the program.