Image Sobel Edge Extraction Algorithm Accelerated by OpenCL

Aiming at the low processing speed of the Sobel edge detection algorithm and the equipment limitations of Compute Unified Device Architecture (CUDA) implementation algorithm acceleration, a Sobel edge detection parallel algorithm based on Open Computing Language (OpenCL) architecture is proposed. The algorithm uses the heterogeneous mode of CPU + GPU to achieve algorithm acceleration. According to the parallel structure and hardware characteristics of Graphics Processing Unit (GPU), the parallel algorithm adopts two acceleration technologies, multi-level storage technology, and vector access technology, which optimizes the data storage structure, improves the data access efficiency, and reduces the complexity of the algorithm. Unlike the CUDA implementation algorithm acceleration for NVIDIA graphics card devices, the OpenCL parallel improved algorithm has no device limitations. Experimental results show that compared with the CPU serial algorithm, the OpenMP parallel algorithm, and the CUDA parallel algorithm, the parallel algorithm has obtained 9.55 times, 2.23 times, and 1.17 times speedup, respectively. The parallel algorithm in this paper shows good data expansibility and platform portability and can provide technical support for the deep application of massive image data.