@inbook {IOPORT.01728268,
    author       = {Vuduc, Richard and Demmel, James W. and Bilmes, Jeff},
    title        = {Statistical models for automatic performance tuning.},
    year         = {2001},
    booktitle    = {Computational Science - ICCS 2001. International conference, San Francisco, CA, USA, May 28--30, 2001. Proceedings. Part 1},
    isbn         = {3-540-42232-3},
    pages        = {117-126},
    publisher    = {Berlin: Springer},
    abstract     = {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.},
    identifier   = {01728268},
}