NDLI: Statistical Performance Analysis for Scientific Applications

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Statistical Performance Analysis for Scientific Applications

Content Provider	ACM Digital Library
Author	Xing, Fei Lu, Charng-Da You, Haihang
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.
Starting Page	1
Ending Page	8
Page Count	8
File Format	PDF
ISBN	9781450328937
DOI	10.1145/2616498.2616555
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical method Variable clustering Performance analysis Principal component analysis
Content Type	Text
Resource Type	Article

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