Automatic Selection of Tuning Plugins in PTF Using Machine Learning
Robert Mijaković, Michael Gerndt · 2020
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.