Parallelization of BP algorithm and example verification based on CUDA

CUDA is a generally used GPGPU(General Purpose Computing on GPU)model. BP algorithm is one of the most widely used neural network model at present. A method of parallelizing BP algorithm using CUDA is proposed in this paper. When this method are used to train BP neural network, data are transferred to GPU before training. Process of computing inputs, outputs, errors of hidden layer and output layer and updating weights, biases are realized on GPU. Training handwritten digital images with this method has speed-up ratio between 6.12 and 8.17 compared to training on four cores CPU. When this two results are respectively used to recognize the same test set, the recognition rate based on training result on GPU increases 0.05% ~ 0.22% compared to that of CPU.