Automatic performance debugging of SPMD-style parallel programs

Automatic performance debugging of parallel applications includes two main steps: locating performance bottlenecks and uncovering their root causes for performance optimization. Previous work fails to resolve this challenging issue in two ways: first, several previous efforts automate locating bottl...

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Bibliographic Details
Published in:Journal of parallel and distributed computing Vol. 71; no. 7; pp. 925 - 937
Main Authors: Liu, Xu, Zhan, Jianfeng, Zhan, Kunlin, Shi, Weisong, Yuan, Lin, Meng, Dan, Wang, Lei
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 01.07.2011
Elsevier
Subjects:
Algorithms
Applied sciences
Automatic performance debugging
Automation
Computation
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Computer systems performance. Reliability
Data processing. List processing. Character string processing
Debugging
Exact sciences and technology
Memory organisation. Data processing
Optimization
Parallel programming
Performance bottleneck
Performance optimization
Root cause analysis
Roots
Searching
Software
SPMD parallel programs
Root cause analysis
Performance optimization
Automatic performance debugging
SPMD parallel programs
Performance bottleneck
Performance evaluation
Supercomputer
Bottleneck
Cloud computing
Data mining
Distributed computing
Open source software
Optimization
Rough set theory
Parallel program
Database
Localization
Multimodel
High performance
Parallel algorithm
Data analysis
Debugging
Program verification
Search algorithm
Program performance
FORTRAN
Data models
Metric
Disparity
ISSN:0743-7315, 1096-0848
Online Access:Get full text
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Summary:Automatic performance debugging of parallel applications includes two main steps: locating performance bottlenecks and uncovering their root causes for performance optimization. Previous work fails to resolve this challenging issue in two ways: first, several previous efforts automate locating bottlenecks, but present results in a confined way that only identifies performance problems with a priori knowledge; second, several tools take exploratory or confirmatory data analysis to automatically discover relevant performance data relationships, but these efforts do not focus on locating performance bottlenecks or uncovering their root causes. The simple program and multiple data (SPMD) programming model is widely used for both high performance computing and Cloud computing. In this paper, we design and implement an innovative system, AutoAnalyzer, that automates the process of debugging performance problems of SPMD-style parallel programs, including data collection, performance behavior analysis, locating bottlenecks, and uncovering their root causes. AutoAnalyzer is unique in terms of two features: first, without any prior knowledge, it automatically locates bottlenecks and uncovers their root causes for performance optimization; second, it is lightweight in terms of the size of performance data to be collected and analyzed. Our contributions are three-fold: first, we propose two effective clustering algorithms to investigate the existence of performance bottlenecks that cause process behavior dissimilarity or code region behavior disparity, respectively; meanwhile, we present two searching algorithms to locate bottlenecks; second, on the basis of the rough set theory, we propose an innovative approach to automatically uncover root causes of bottlenecks; third, on the cluster systems with two different configurations, we use two production applications, written in Fortran 77, and one open source code—MPIBZIP2 ( http://compression.ca/mpibzip2/), written in C++, to verify the effectiveness and correctness of our methods. For three applications, we also propose an experimental approach to investigating the effects of different metrics on locating bottlenecks. ► We propose two algorithms to investigate the existence of performance bottlenecks. ► We present two searching algorithms to locate bottlenecks. ► We propose an innovative approach to uncovering root causes of bottlenecks. ► We use three applications to verify the effectiveness of our systems. ► We investigate the effects of different metrics on locating bottlenecks.
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ISSN:0743-7315
1096-0848
DOI:10.1016/j.jpdc.2011.03.006