Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extreme Computing Research Center
KAUST Supercomputing Laboratory (KSL)
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AbstractThe 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.
CitationSiddiqui S, AlZayer F, Feki S (2015) Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications. High Performance Computing for Computational Science -- VECPAR 2014: 224–235. Available: http://dx.doi.org/10.1007/978-3-319-17353-5_19.
PublisherSpringer Science + Business Media
Conference/Event name11th International Conference on High Performance Computing for Computational Science, VECPAR 2014