GPGPU And Image Matching Parallel Algorithm Based On SIFT

Currently, using GPU for general-purpose computing has been a hot research topic of the world. The early GPGPU programming is used in graphics API development. The disadvantage of this development method is very difficult and costly. CUDA(Compute Unified Device Architecture) is a tool introduced by NVIDIA which is designed for GPGPU. Its simple programming style and efficient multi-threaded processing model make it has more efficiency in using of GPU hardware resources when processing computing-intensive tasks.On the other side, as a feature point based matching algorithm, SIFT can process the matching problem between two images with translation, rotation and affine transformation. Strong matching ability and good robustness make the SIFT algorithm has a wide application in image match area.In this paper, the hardware architecture and software systems of CUDA are deeply analyzed first; Secondly, tasks dividing, performance and optimization strategy of CUDA program are described; Then GT200 architecture and the next-generation architecture Fermi are compared, and improvements and advantages of Fermi are pointing out.Finally, the implementation of SIFT algorithm on CUDA are described. Compared with its implementation on CPU, implementation on CUDA is able to achieve a good speedup.