基于GPU的SVM参数优化并行算法

Optimization algorithm of SVM parallel parameters based on GPU

为了缩短支持向量机(support vector machine,SVM)参数优化时长,提高SVM参数优化的效率,提出了基于图形处理单元(graphic processing unit,GPU)的SVM参数优化并行算法.分析了基于网格搜索和粒子群优化算法的并行特性,基于GPU设计了该优化算法的并行化方案,并在单GeForce GT 650M GPU卡上进行了试验验证.结果表明,并行化网格搜索和并行化粒子群参数优化算法不仅可以取得与非并行化参数优化算法相同的优化效果,而且执行时间大大减小,其中并行粒子群参数优化算法的加速比可高达26.85,大幅提升了SVM的参数优化效率.

To shorten the parameter optimization time of support vector machine（ SVM） and improve the efficiency of parameter optimization,the optimization algorithm of SVM parallel parameters was proposed based on graphic processing unit（ GPU）. The parallel possibility was analyzed for grid search（ GS）algorithm and particle swarm optimization（ PSO） algorithm. Based on GPU,the parallel methods of GS and PSO were proposed. The experiments were executed on Ge Force GT 650 M GPU card to conduct 5-cross validation. The results show that compared with the original optimization algorithms,the proposed parallel optimization algorithm can obtain the same optimum performance with greatly reduced execution time. The speed-up ratio can reach 26. 85 for parallel PSO algorithm, and the SVM parameter optimization performance is improved greatly.