基于OpenCL机器视觉算法GPU实现

针对不断增长的对机器视觉算法处理效率和实时性的要求,研究基于异构编程框架OpenCL对机器视觉算法在通用计算机图形处理单元(GPU)上的并行处理和加速方法,提出结合存储分配、指令流优化、数据重用等方法的并行优化策略。在Sobel边缘检测、Canny边缘检测、Harris角点检测、高斯图像金字塔4个不同并行度视觉算法上进行验证,验证结果表明,在不考虑数据传输的情况下,对比CPU串行实现取得了平均6.16的加速比,对比OpenCV的GPU库(即CUDA实现)取得了1.12-5.47的加速比,验证了所提优化策略的有效性。

GPU implementation of machine vision algorithm based on OpenCL

Aiming at the increasing demands for the processing efficiency and real-time performance of the machine vision algorithm,the parallel processing and accelerating method of machine vision algorithm based on heterogeneous programming framework open computing language(OpenCL)on computer graphics processing unit(GPU)was studied,and a parallel optimization strategy based on storage allocation,instruction flow optimization and data reuse was proposed.It was validated by four different degrees of parallelism vision algorithms including Sobel edge detection,Canny edge detection,Harris corner detection and Gaus-sian image pyramid.The results show that,without considering the data transmission,the average 6.16 acceleration ratio is obtained by comparing the central processing unit(CPU)serial implementation,and acceleration ratio of the 1.12-5.47 is obtained by comparing GPU library of OpenCV(i.e.the CUDA implementation),which verifies the effectiveness of the proposed optimization strategy.