A Study of Parallel Evolution Strategy - Pattern Search on a GPU Computing Platform

摘要

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.