Statistical Performance Analysis for Scientific Applications
Authors: 
Fei Xing, Haihang You, Charng-Da LuAuthors Info & Claims
Fei Xing, Haihang You, Charng-Da LuAuthors Info & ClaimsArticle No.: 62, Pages 1 - 8
Abstract
As high-performance computing (HPC) heads towards the exascale era, application performance analysis becomes more complex and less tractable. It usually requires considerable training, experience, and a good working knowledge of hardware/software interaction to use performance tools effectively, which becomes a barrier for domain scientists. Moreover, instrumentation and profiling activities from a large run can easily generate gigantic data volume, making both data management and characterization another challenge. To cope with these, we develop a statistical method to extract the principal performance features and produce easily interpretable results. This paper introduces a performance analysis methodology based on the combination of Variable Clustering (VarCluster) and Principal Component Analysis (PCA), describes the analysis process, and gives experimental results of scientific applications on a Cray XT5 system. As a visualization aid, we use Voronoi tessellations to map the numerical results into graphical forms to convey the performance information more clearly.
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Index Terms
- Statistical Performance Analysis for Scientific Applications
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July 2014
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In-Cooperation
- NSF: National Science Foundation
- Drexel University
- Indiana University: Indiana University
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Association for Computing Machinery
New York, NY, United States
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Published: 13 July 2014
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XSEDE '14
XSEDE '14: Annual Conference of the Extreme Science and Engineering Discovery Environment
July 13 - 18, 2014
GA, Atlanta, USA
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XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;
Overall Acceptance Rate 129 of 190 submissions, 68%
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