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


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

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



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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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