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Abstract: Achieving peak performance from library subroutines usually requires extensive, machine-dependent tuning by hand. Automatic tuning systems have emerged in response, and they typically operate by (1) generating a large number of possible implementations of a subroutine, and (2) selecting the fastest implementation by an exhaustive, empirical search. This paper presents quantitative data that motivates the development of such a search-based system, and discusses two problems which arise in... (Update)
Context of citations to this paper: More
.... the brute force of parameter searching with modeling techniques is a sensible extension to our current search method (e.g. Vuduc et al. [28]) OCEANS also combines parameter space modeling with search. 7 Conclusion The compiler optimizations we do yield the best available...
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BibTeX entry: (Update)
R. Vuduc, J. Demmel, and J. Bilmes. Statistical Models for Automatic Performance Tuning. In Proceedings of the 2001. http://citeseer.ist.psu.edu/vuduc01statistical.html More
@article{ vuduc01statistical,
author = "Richard Vuduc and James W. Demmel and Jeff Bilmes",
title = "Statistical Models for Automatic Performance Tuning",
journal = "Lecture Notes in Computer Science",
volume = "2073",
pages = "117--??",
year = "2001",
url = "citeseer.ist.psu.edu/vuduc01statistical.html" }
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