Using GPU and OpenACC to Accelerate the Maze Optimal Routing Algorithm

in order to improve the efficiency of maze optimal routing problem, a GPU acceleration programming model OpenACC is used in this paper. By analyzing an algorithm which solves the maze problem based on ant colony algorithm, we complete the task mapping on the model. Though GPU acceleration, ant colony searching process was changed into parallel matrix operations. To decrease the algorithm accessing overhead and increase operating speed, data were rationally organized and stored for GPU. Experiments of different scale maze matrix show that the parallel algorithm greatly reduces the operation time. Speedup will be increased with the expansion of the matrix size. In our experiments, the maximum speedup is about 6.1. The algorithm can solve larger matrices with a high level of processing performance by adding efficient OpenACC instruction to serial code and organizing the data structure for parallel accessing.