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Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications

Shahzeb Siddiqui, Saber Feki
King Abdullah University of Science and Technology, Kingdom of Saudi Arabia
The Ninth International Workshop on Automatic Performance Tuning (iWAPT), 2014

@article{siddiqui2014historic,

   title={Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications},

   author={Siddiqui, Shahzeb and Feki, Saber},

   year={2014}

}

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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 used 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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