Similarity Analysis in Automatic Performance Debugging of SPMD Parallel Programs

TL;DR

This paper introduces an automated approach for analyzing SPMD parallel programs by combining similarity clustering and Rough Set methods to identify and understand performance issues.

Contribution

It presents a novel combination of clustering and Rough Set techniques for automatic performance debugging of SPMD parallel programs.

Findings

Successfully identified performance problems in a real parallel application.

Demonstrated the effectiveness of the combined analysis methods.

Provided insights into micro-level causes of performance issues.

Abstract

Different from sequential programs, parallel programs possess their own characteristics which are difficult to analyze in the multi-process or multi-thread environment. This paper presents an innovative method to automatically analyze the SPMD programs. Firstly, with the help of clustering method focusing on similarity analysis, an algorithm is designed to locate performance problems in parallel programs automatically. Secondly a Rough Set method is used to uncover the performance problem and provide the insight into the micro-level causes. Lastly, we have analyzed a production parallel application to verify the effectiveness of our method and system.