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DOE OSTI · 1154980

Techniques for Automated Performance Analysis

Marcus, Ryan C.
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Abstract

The performance of a particular HPC code depends on a multitude of variables, including compiler selection, optimization flags, OpenMP pool size, file system load, memory usage, MPI configuration, etc. As a result of this complexity, current predictive models have limited applicability, especially at scale. We present a formulation of scientific codes, nodes, and clusters that reduces complex performance analysis to well-known mathematical techniques. Building accurate predictive models and enhancing our understanding of scientific codes at scale is an important step towards exascale computing.

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97 MATHEMATICS AND COMPUTINGCOMPUTER SCIENCE

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BibTeXRIS

Marcus, Ryan C.. 2014-09-02. Techniques for Automated Performance Analysis. https://doi.org/10.2172/1154980

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