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Abstract: Achieving peak performance from library subroutines usually requires extensive, machine-dependent tuning by hand. Automatic tuning systems have been developed in response which 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. In this paper, we show how statistical modeling of the performance feedback data collected during the search phase can be used in... (Update)
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BibTeX entry: (Update)
@inproceedings{ vuduc2000:statmodel,
author = {Richard Vuduc and James Demmel and Jeff Bilmes},
title = {Statistical Modeling of Feedback Data in an Automatic Tuning System}
,
booktitle = {Third ACM Workshop on Feedback-Directed Dynamic Optimi
zation},
location = {Monterey, CA},
month = {December},
year = {2000},
url = {citeseer.ist.psu.edu/vuduc00statistical.html} }
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