基于GPU的现代并行优化算法

Research on Parallel Modern Optimization Algorithms Using GPU

针对现代优化算法在处理相对复杂问题中所面临的求解时间复杂度较高的问题,引入基于GPU的并行处理解决方法。首先从宏观角度阐释了基于计算统一设备架构CUDA的并行编程模型,然后在GPU环境下给出了基于CUDA架构的5种典型现代优化算法(模拟退火算法、禁忌搜索算法、遗传算法、粒子群算法以及人工神经网络)的并行实现过程。通过对比分析在不同环境下测试的实验案例统计结果,指出基于GPU的单指令多线程并行优化策略的优势及其未来发展趋势。

In order to deal with the relatively high time-complexity of practical issue,parallel modern optimization based on GPU was presented in this paper.Firstly,CUDA parallel programming architecture and programming model were summarized at a macroscopic level.Then the parallel processes of five typical modern optimization algorithms（Simulated Annealing,Tabu Search,Genetic Algorithms,Particle Swarm Optimization and Artificial Neural Network） using CUDA programming model were provided.Experimental statistics measured in different environment indicate that the parallel method can obtain better performance on average than CPU.Finally the parallel optimization strategy was discussed and the outlook of future direction of parallel optimization algorithm was also pointed out.