Research and implementation of parallel particle swarm optimization based on CUDA

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.