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DOE OSTI · 1422524

Exploiting Performance Portability in Search Algorithms for Autotuning

Abstract

Autotuning seeks the best configuration of an application by orchestrating hardware and software knobs that affect performance on a given machine. Autotuners adopt various search techniques to efficiently find the best configuration, but they often ignore lessons learned on one machine when tuning for another machine. We demonstrate that a surrogate model built from performance results on one machine can speedup the autotuning search by 1.6X to 130X on a variety of modern architectures.

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BibTeXRIS

Roy, Amit, Balaprakash, Prasanna, Hovland, Paul, Wild, Stefan M.. 2016-01-01. Exploiting Performance Portability in Search Algorithms for Autotuning. https://doi.org/10.1109/ipdpsw.2016.85

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