TY - GEN
T1 - Automatic selection of tuning plugins in PTF using machine learning
AU - Mijakovic, Robert
AU - Gerndt, Michael
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Performance tuning of scientific codes often requires tuning many different aspects like vectorization, OpenMP synchronization, MPI communication, and load balancing. The Periscope Tuning Framework (PTF), an online automatic tuning framework, relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. Individual plugins can be combined for convenience into meta-plugins. Since each plugin can take considerable execution time for testing various combination of the tuning parameters, it is desirable to automatically predict the tuning potential of plugins for programs before their application. We developed a generic automatic prediction mechanism based on machine learning techniques for this purpose. This paper demonstrates this technique in the context of the Compiler Flags Selection plugin, that tunes the parameters of a user specified compiler for a given application.
AB - Performance tuning of scientific codes often requires tuning many different aspects like vectorization, OpenMP synchronization, MPI communication, and load balancing. The Periscope Tuning Framework (PTF), an online automatic tuning framework, relies on a flexible plugin mechanism providing tuning plugins for different tuning aspects. Individual plugins can be combined for convenience into meta-plugins. Since each plugin can take considerable execution time for testing various combination of the tuning parameters, it is desirable to automatically predict the tuning potential of plugins for programs before their application. We developed a generic automatic prediction mechanism based on machine learning techniques for this purpose. This paper demonstrates this technique in the context of the Compiler Flags Selection plugin, that tunes the parameters of a user specified compiler for a given application.
KW - Autotuning
KW - Machine learning
KW - PTF
KW - Parallel computing
KW - Performance tuning
KW - Program characterization
UR - http://www.scopus.com/inward/record.url?scp=85091566495&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW50202.2020.00069
DO - 10.1109/IPDPSW50202.2020.00069
M3 - Conference contribution
AN - SCOPUS:85091566495
T3 - Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020
SP - 349
EP - 358
BT - Proceedings - 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2020
Y2 - 18 May 2020 through 22 May 2020
ER -