Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

Shahzeb Siddiqui, Fatemah Alzayer, Saber Feki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The performance optimization of scientific applications usually requires an in-depth knowledge of the hardware and software. A performance tuning mechanism is suggested to automatically tune OpenACC parameters to adapt to the execution environment on a given system. A historic learning based methodology is suggested to prune the parameter search space for a more efficient auto-tuning process. This approach is applied to tune the OpenACC gang and vector clauses for a better mapping of the compute kernels onto the underlying architecture. Our experiments show a significant performance improvement against the default compiler parameters and drastic reduction in tuning time compared to a brute force search-based approach.
Original languageEnglish (US)
Title of host publicationHigh Performance Computing for Computational Science -- VECPAR 2014
PublisherSpringer Nature
Pages224-235
Number of pages12
ISBN (Print)9783319173528
DOIs
StatePublished - Apr 18 2015

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