A study of parallel evolution strategy: pattern search on a GPU computing platform
Lamar University, Beaumont, TX, USA
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC ’09
@conference{zhu2009study,
title={A study of parallel evolution strategy: pattern search on a gpu computing platform},
author={Zhu, W.},
booktitle={Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation},
pages={765–772},
year={2009},
organization={ACM}
}
This paper presents a massively parallel Evolution Strategy – Pattern Search Optimization (ES-PS) algorithm with graphics hardware acceleration on bound constrained nonlinear continuous optimization functions. The algorithm is specifically designed for a graphic processing unit (GPU) hardware platform featuring ‘Single Instruction – Multiple Thread’ (SIMT). GPU computing is an emerging desktop parallel computing platform. The hybrid ES-PS optimization method is implemented in the GPU environment and compared to a similar implementation on CPU hardware. Computational results indicate that GPU-accelerated SIMT-ES-PS method is orders of magnitude faster than the corresponding CPU implementation. The main contribution of this paper is the parallelization analysis and performance analysis of the hybrid ES-PS with GPU acceleration.
January 23, 2011 by hgpu
