数字散斑识别算法中的GPU高性能运算应用研究

数字散斑相关方法有着测量环境简单、全场非接触等优点，但算法效率一直是限制其发展的瓶颈之一。GPU有着天然的并行性，GPU高性能运算可以为计算机图形处理带来极大的效率提升。利用CUDA平台编程对传统的数字散斑逐点搜索算法、十字搜索算法及遗传算法进行GPU高性能并行处理，并与传统方法比较分析。实验结果表明，对于尺寸为150150像素的散斑图像，3种方法效率分别提升了20倍、8倍、31倍；对于尺寸为500500像素的散斑图像，3种方法效率分别提升了183倍、33倍、44倍；对于尺寸为1 0001 000像素的散斑图像,3种方法效率分别提升了424倍、116倍、44倍。

Application of high-performance GPU computing in digital speckle pattern recognition algorithms

Digital speckle correlation method has the advantages of low demand of the measurement environment, overall non-contact measure, however, the algorithm efficiency has been one of the bottlenecks limiting its development. The graphics processing unit (GPU) has the natural parallelism, GPU high performance computing brings great efficiency on computer image processing. By programming with the compute unified device architecture (CUDA) platform, the GPU high-performance parallel processing was applied for the traditional digital speckle point-by-point search algorithm, cross search algorithm and genetic algorithm.Comparing with the traditional method, the experimental results show that the efficiency of these 3 methods improve by 20,8,31 times respectively for 150150 pixels speckle image, while by 183,33,44 times respectively for 500500 pixels speckle images and by 424,116,44 times respectively for 1 0001 000 pixels speckle images.