TY - GEN
T1 - Exploiting performance portability in search algorithms for autotuning
AU - Roy, Amit
AU - Balaprakash, Prasanna
AU - Hovland, Paul D.
AU - Wild, Stefan M.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/18
Y1 - 2016/7/18
N2 - 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.
AB - 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.
KW - Autotuning
KW - Empirical search heuristics
KW - Performance portability
UR - http://www.scopus.com/inward/record.url?scp=84991571443&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2016.85
DO - 10.1109/IPDPSW.2016.85
M3 - Conference contribution
AN - SCOPUS:84991571443
T3 - Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
SP - 1535
EP - 1544
BT - Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium, IPDPS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016
Y2 - 23 May 2016 through 27 May 2016
ER -