Automatic Performance Debugging of SPMD Parallel Programs

TL;DR

AutoAnalyzer is an innovative system that automatically detects performance bottlenecks and uncovers their root causes in SPMD parallel programs without prior knowledge, leading to significant performance improvements.

Contribution

The paper introduces a lightweight, automatic debugging system for SPMD programs that locates bottlenecks and uncovers root causes without apriori knowledge, using novel metrics and algorithms.

Findings

Identified bottlenecks in two production applications.

Achieved performance improvements of 20% to 170%.

Validated effectiveness and correctness of the methods.

Abstract

Automatic performance debugging of parallel applications usually involves two steps: automatic detection of performance bottlenecks and uncovering their root causes for performance optimization. Previous work fails to resolve this challenging issue in several ways: first, several previous efforts automate analysis processes, but present the results in a confined way that only identifies performance problems with apriori knowledge; second, several tools take exploratory or confirmatory data analysis to automatically discover relevant performance data relationships. However, these efforts do not focus on locating performance bottlenecks or uncovering their root causes. In this paper, we design and implement an innovative system, AutoAnalyzer, to automatically debug the performance problems of single program multi-data (SPMD) parallel programs. Our system is unique in terms of two dimensions: first, without any apriori knowledge, we automatically locate bottlenecks and uncover their root causes for performance optimization; second, our method is lightweight in terms of size of collected and analyzed performance data. Our contribution is three-fold. First, we propose a set of simple performance metrics to represent behavior of different processes of parallel programs, and present two effective clustering and searching algorithms to locate bottlenecks. Second, we propose to use the rough set algorithm to automatically uncover the root causes of bottlenecks. Third, we design and implement the AutoAnalyzer system, and use two production applications to verify the effectiveness and correctness of our methods. According to the analysis results of AutoAnalyzer, we optimize two parallel programs with performance improvements by minimally 20% and maximally 170%.