Historic Learning Approach for Auto-tuning OpenACC Accelerated Scientific Applications
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}
}
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.
July 3, 2014 by hgpu
