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Statistical Performance Analysis for Scientific Applications

Published: 13 July 2014 Publication History

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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Published In

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XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment
July 2014
445 pages
ISBN:9781450328937
DOI:10.1145/2616498
  • General Chair:
  • Scott Lathrop,
  • Program Chair:
  • Jay Alameda
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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

Publication History

Published: 13 July 2014

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Author Tags

  1. performance analysis
  2. principal component analysis
  3. statistical method
  4. variable clustering

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  • Research-article
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  • Refereed limited

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XSEDE '14

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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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