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Empirical Optimization for a Sparse Linear Solver: A Case Study

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Abstract

This paper describes initial experiences with semi-automated performance tuning of a sparse linear solver in LS-DYNA, a large, widely used engineering application. Through a collection of tools supporting empirical optimization, we alleviate the burden of performance tuning for mapping today’s sophisticated engineering software to increasingly complex hardware platforms. We describe a tool that automatically isolates code segments to create benchmark subsets for the purposes of performance tuning. We present a collection of automatically generated empirical results that demonstrate the sensitivity of the application’s performance to optimization parameters. Through this case study, we demonstrate the importance of developing automatic performance tuning support for performance-sensitive applications.

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Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA, 90292-6695, USA

    Yoon-Ju Lee, Pedro C. Diniz, Mary W. Hall & Robert Lucas

Authors
  1. Yoon-Ju Lee
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  2. Pedro C. Diniz
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  3. Mary W. Hall
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  4. Robert Lucas
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Correspondence to Yoon-Ju Lee.

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Lee, YJ., Diniz, P.C., Hall, M.W. et al. Empirical Optimization for a Sparse Linear Solver: A Case Study. Int J Parallel Prog 33, 165–181 (2005). https://doi.org/10.1007/s10766-005-3581-7

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  • DOI: https://doi.org/10.1007/s10766-005-3581-7

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