中文摘要：

针对传统CPU平台下小波变换算法难满足当前高分辨率、大数据规模下的实时性要求,提出一种基于GPU的并行小波变换算法,并通过改善Local Memory访存数据的局部性和增加Global Memory访存带宽的优化技术,利用多Kernel并行提高多种分辨率下小波变换的性能.实验结果表明,与CPU串并行版本相比,GPU并行优化算法在高分辨率变换情况下,加速比最高可达30~60倍,可满足对变换实时性的要求.

英文摘要：

Since the classical wavelet transform algorithm on CPU hardly meets the real-time performance requirements,especially dealing with large scale data in high resolution,we presented a GPU-based parallel wavelet transform algorithm,which improves the locality of local memory access and increases the bandwidth of global memory access.It uses multi-kernel to improve the performance in the case of multi-resolutions.The experiment results show that compared to the performance of a classical algorithm on CPU,GPU gains the speedup of 30—60,accordingly,it can satisfy the real-time requirements for transformation.