Parallel Image Matching Algorithm Based on GPGPU

With the development of satellite remote sensing technology,it is the key issue in remote sensing field to transform massive data into user information in short time. The traditional image matching algorithms for optimization and implementation which were designed for common processor CPU,could not be effectively applied on graphics processing unit (GPU). A fast image matching parallel algorithm is presented based on general-purpose computing on graphics processing units (GPGPU) which support Compute Unified Device Architecture (CUDA). The algorithm can execute high performance parallel computing in Single Instruction Multiple Thread (SIMT) Pattern. On the basis of the parallel architecture and hardware characteristic of GPU,the parallel algorithm introduces three speedup methods to improve the implementation performance:execution configuration technology,high-speed storage technology and global storage technology optimizes the data storage structure and improves the data access efficiency. The experiment result shows that GPU can with high efficiency implement the parallel algorithm and processing efficiency of 8-bit 1280×1024 pictures can be up to the highest Multiprocessor Warp Occupancy,processing speed is 7 times faster than CPU-based implementation. The comparison between CUDA and CPU in image matching algorithms shows the advance of the CUDA in high arithmetic intensity real-time processing and computing data processing and this provides new methods and ideas to optimize image matching performance and GPGPU.