  As in various fields like scientific research and industrial application, the
computation time optimization is becoming a task that is of increasing
importance because of its highly parallel architecture. The graphics processing
unit is regarded as a powerful engine for application programs that demand
fairly high computation capabilities. Based on this, an algorithm was
introduced in this paper to optimize the method used to compute the gray-level
co-occurrence matrix (GLCM) of an image, and strategies (e.g., "copying",
"image partitioning", etc.) were proposed to optimize the parallel algorithm.
Results indicate that without losing the computational accuracy, the speed-up
ratio of the GLCM computation of images with different resolutions by GPU by
the use of CUDA was 50 times faster than that of the GLCM computation by CPU,
which manifested significantly improved performance.
