A knowledge-based performance tuning tool for parallel programs

Abstract

The increasing complexity of parallel computing systems has brought about in crisis in parallel performance evaluation and tuning. Tools for performance measurement and visualization become necessary parts of programming environments for parallel computers. However, today's performance analysis systems offer little more than basic measurement and analysis facilities for the sources of poor performance, such as load imbalance, communication overhead, and synchronization loss. Our experience in parallel programming shows that a system which can provide higher level performance measurement and analysis is more helpful in the performance tuning of parallel program. For example, whether the programmer adopts a proper program strategy or algorithm is one of the most important factors which affect the performance of parallel programs. Therefore, we argue that a helpful performance tuning tool should be able to assist programmers to optimise the strategy or algorithm in their parallel programs. In this paper we introduce an intelligent performance tuning tool which detects and analyses the strategy and algorithm concepts in parallel programs, helps users rapidly identify the location and cause of the performance problems, and provides suggestions to improve the performance of their parallel programs.