HPCVIEW: A Tool for Top-down Analysis of Node Performance

  • Authors:

  • John Mellor-Crummey;Robert J. Fowler;Gabriel Marin;Nathan Tallent

  • Affiliations:

  • Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005-1892 johnmc@cs.rice.edu;Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005-1892 rjf@cs.rice.edu;Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005-1892 mgabi@cs.rice.edu;Department of Computer Science, MS 132, Rice University, 6100 Main Street, Houston, TX 77005-1892 eraxxon@alumni.rice.edu

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2002

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Abstract

It is increasingly difficult for complex scientific programs to attain a significant fraction of peak performance on systems that are based on microprocessors with substantial instruction-level parallelism and deep memory hierarchies. Despite this trend, performance analysis and tuning tools are still not used regularly by algorithm and application designers. To a large extent, existing performance tools fail to meet many user needs and are cumbersome to use. To address these issues, we developed HPCVIEW—a toolkit for combining multiple sets of program profile data, correlating the data with source code, and generating a database that can be analyzed anywhere with a commodity Web browser. We argue that HPCVIEW addresses many of the issues that have limited the usability and the utility of most existing tools. We originally built HPCVIEW to facilitate our own work on data layout and optimizing compilers. Now, in addition to daily use within our group, HPCVIEW is being used by several code development teams in DoD and DoE laboratories as well as at NCSA.