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\iteman{io-port 01728268}
\itemau{Vuduc, Richard; Demmel, James W.; Bilmes, Jeff}
\itemti{Statistical models for automatic performance tuning.}
\itemso{Alexandrov, Vassil N. (ed.) et al., Computational Science - ICCS 2001. International conference, San Francisco, CA, USA, May 28-30, 2001. Proceedings. Part 1. Berlin: Springer. Lect. Notes Comput. Sci. 2073, 117-126 (2001).}
\itemab
Summary: 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, at compile-time, by (1) generating a large number of possible implementations of a subroutine, and (2) selecting a fast implementation by an exhaustive, empirical search. This paper applies statistical techniques to exploit the large amount of performance data collected during the search. First, we develop a heuristic for stopping an exhaustive compile-time search early if a near-optimal implementation is found. Second, we show how to construct run-time decision rules, based on run-time inputs, for selecting from among a subset of the best implementations. We apply our methods to actual performance data collected by the PHiPAC tuning system for matrix multiply on a variety of hardware platforms.
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\itemli{http://link.springer.de/link/service/series/0558/bibs/2073/20730117}
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