2581

A study of parallel evolution strategy: pattern search on a GPU computing platform

Weihang Zhu
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}

}

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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.
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