A comparison of empirical and model-driven optimization

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A comparison of empirical and model-driven optimization

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Conference on Programming Language Design and Implementation archive
Proceedings of the ACM SIGPLAN 2003 conference on Programming language design and implementation table of contents

San Diego, California, USA

SESSION: Code optimization I table of contents

Pages: 63 - 76

Year of Publication: 2003

ISBN:1-58113-662-5

Also published in ...

Authors

Kamen Yotov	Cornell University
Xiaoming Li	University of Illinois at Urbana-Champaign
Gang Ren	University of Illinois at Urbana-Champaign
Michael Cibulskis	University of Illinois at Urbana-Champaign
Gerald DeJong	University of Illinois at Urbana-Champaign
Maria Garzaran	University of Illinois at Urbana-Champaign
David Padua	University of Illinois at Urbana-Champaign
Keshav Pingali	Cornell University
Paul Stodghill	Cornell University
Peng Wu	IBM T.J. Watson Research Center

Sponsors

ACM: Association for Computing Machinery
SIGPLAN: ACM Special Interest Group on Programming Languages

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 3, Downloads (12 Months): 63, Citation Count: 25

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ABSTRACT

Empirical program optimizers estimate the values of key optimization parameters by generating different program versions and running them on the actual hardware to determine which values give the best performance. In contrast, conventional compilers use models of programs and machines to choose these parameters. It is widely believed that model-driven optimization does not compete with empirical optimization, but few quantitative comparisons have been done to date. To make such a comparison, we replaced the empirical optimization engine in ATLAS (a system for generating a dense numerical linear algebra library called the BLAS) with a model-driven optimization engine that used detailed models to estimate values for optimization parameters, and then measured the relative performance of the two systems on three different hardware platforms. Our experiments show that model-driven optimization can be surprisingly effective, and can generate code whose performance is comparable to that of code generated by empirical optimizers for the BLAS.

REFERENCES

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