摘要
提出一种基于GPGPU的CUDA架构快速影像匹配并行算法,它能够在SIMT模式下完成高性能并行计算。并行算法根据GPU的并行结构和硬件特点,采用执行配置技术、高速存储技术和全局存储技术三种加速技术,优化数据存储结构,提高数据访问效率。实验结果表明,并行算法充分利用GPU的并行处理能力,在处理1280×1024分辨率的8位灰度图像时可达到最高多处理器warp占有率,速度是基于CPU实现的7倍。CUDA在高运算强度数据处理中呈现出的实时处理能力和计算能力,为进一步加速影像匹配性能和GPU通用计算提供了新的方法和思路。
With the development of satellite remote sensing technology, it is the key issue in remote sensing field to transform mossive 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）. Afast image matching parallel algorithm is presented based on general-purpose compu- ting 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 （SlMT） Pattern. Qn 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 1 280× 1 024 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.
出处
《测绘学报》
EI
CSCD
北大核心
2010年第1期46-51,共6页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(40771177)
国家863计划(2006AA12Z136)
河南省重点科技攻关项目(072102360026)
