计算机应用研究2013，Vol.30Issue(8)：2415-2418,4.DOI:10.3969/j.issn.1001-3695.2013.08.043
基于CUDA的并行粒子群优化算法的设计与实现
Research and implementation of parallel particle swarm optimization based on CUDA
摘要
Abstract
This paper raised a fine-grained PSO algorism based on GPU acceleration,which could reduce the computing time for processing large amounts of data and solve large-scale complex problems.The implementation of proposed method based on compute unified device architecture (CUDA),in order to accelerate the convergence rate of whole swarm,a larger number of GPU threads used to parallel process a single update and fitness evaluation alone.For ensuring the stability of the code and it easier to program,fully used several numerical library provide by CUDA.Experiments based on several benchmark test functions show that more than 90 times speeds obtained with the same calculation precision,it compared to CPU-based sequential implementation.关键词
粒子群优化算法/并行计算/GPU/统一计算设备架构Key words
particle swarm optimization(PSO) algorithm/ parallel computing/ GPU / CUDA分类
信息技术与安全科学引用本文复制引用
蔡勇,李光耀,王琥..基于CUDA的并行粒子群优化算法的设计与实现[J].计算机应用研究,2013,30(8):2415-2418,4.基金项目
国家"863"计划资助项目(2012AA111802) （2012AA111802）
国家自然科学基金资助项目(11172097) （11172097）
