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.
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References
OpenACC Standard specification. www.openacc-standard.org
OpenMP 4.0 specification. www.openmp.org/mp-documents/OpenMP4.0.0.pdf
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Acknowledgments
The authors would like to thank NVIDIA for the hardware donation to KAUST as CUDA center of research and KAUST IT Research Computing for their support.
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Siddiqui, S., AlZayer, F., Feki, S. (2015). Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science -- VECPAR 2014. VECPAR 2014. Lecture Notes in Computer Science(), vol 8969. Springer, Cham. https://doi.org/10.1007/978-3-319-17353-5_19
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DOI: https://doi.org/10.1007/978-3-319-17353-5_19
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