黄磊, 张李超, 鄢然. 数字散斑识别算法中的GPU高性能运算应用研究[J]. 应用光学, 2015, 36(5): 762-767. DOI: 10.5768/JAO201536.0502006
引用本文: 黄磊, 张李超, 鄢然. 数字散斑识别算法中的GPU高性能运算应用研究[J]. 应用光学, 2015, 36(5): 762-767. DOI: 10.5768/JAO201536.0502006
Huang Lei, Zhang Li-chao, Yan Ran. Application of high-performance GPU computing in digital speckle pattern recognition algorithms[J]. Journal of Applied Optics, 2015, 36(5): 762-767. DOI: 10.5768/JAO201536.0502006
Citation: Huang Lei, Zhang Li-chao, Yan Ran. Application of high-performance GPU computing in digital speckle pattern recognition algorithms[J]. Journal of Applied Optics, 2015, 36(5): 762-767. DOI: 10.5768/JAO201536.0502006

数字散斑识别算法中的GPU高性能运算应用研究

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

  • 摘要: 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。

     

    Abstract: 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.

     

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