Exploiting Performance Portability in Search Algorithms for Autotuning
|Title||Exploiting Performance Portability in Search Algorithms for Autotuning|
|Year of Publication||2015|
|Authors||Roy, A, Balaprakash, P, Hovland, PD, Wild, SM|
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.