Abstract: SURF algorithm has high computational complexity and can not meet the real-time requirement. To solve these problems, a parallel SURF algorithm based on OpenCL was presented. Firstly, data parallelism and task parallelism model were applied to the calculations of the integral images, Hessian detector, orientation and descriptor, and the detailed algorithm process was given. Secondly, the performance of the parallel algorithm was optimized from data transmission, memory access and load balancing. The experimental results show that the algorithm can achieve more than 10 times speedup for images with different resolution, and some high-resolution images can even reach up to 39.5 times. Furthermore, it can be applied to a variety of general purpose computing platforms.