吉林大学学报(理学版)

• 计算机科学 • 上一篇    下一篇

一种基于GPU的二维离散多分辨率小波变换加速方法

刘磊1, 张子佳1,2, 刘雷2, 张睿1   

  1. 1. 吉林大学 计算机科学与技术学院, 长春 130012; 2. 中国科学院 计算技术研究所, 北京 100190
  • 收稿日期:2014-05-09 出版日期:2015-03-26 发布日期:2015-03-24
  • 通讯作者: 张睿 E-mail:rui@jlu.edu.cn

A Method of GPUBased Accelerating 2DMulti-resolutions Discrete Wavelet Transform

LIU Lei1, ZHANG Zijia1,2, LIU Lei2, ZHANG Rui1   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, China;2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2014-05-09 Online:2015-03-26 Published:2015-03-24
  • Contact: ZHANG Rui E-mail:rui@jlu.edu.cn

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.

关键词: 小波变换, 多分辨率, GPU加速

Abstract:

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.

Key words: wavelet transform, multiresolutions, GPU acclerate

中图分类号: 

  • TP302.7