Research of Fast 2-D Walsh Transformation Based on GPU

TONG Ying; ZHANG Jian

Chinese

您当前的位置：

首页 >

文章列表页 >

Research of Fast 2-D Walsh Transformation Based on GPU

更新时间：2026-09-04

- Research of Fast 2-D Walsh Transformation Based on GPU
- Vol. 28, Issue 1, Pages: 46-49(2011)
- 作者机构：
  
  南京工程学院机构通信工程学院,江苏,南京,211167
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2011
- 稿件说明：
移动端阅览
童莹, 张健. 基于GPU的快速二维沃尔什变换研究[J]. 微电子学与计算机, 2011, 28(1): 46-49,53.

TONG Ying, ZHANG Jian. Research of Fast 2-D Walsh Transformation Based on GPU[J]. 2011, 28(1): 46-49.
童莹, 张健. 基于GPU的快速二维沃尔什变换研究[J]. 微电子学与计算机, 2011, 28(1): 46-49,53. DOI：

TONG Ying, ZHANG Jian. Research of Fast 2-D Walsh Transformation Based on GPU[J]. 2011, 28(1): 46-49. DOI：

摘要

提出了一种基于GPU(Graphics Processing Unit

图形处理器)CUDA(Compute Unified Device Architecture

计算统一设备架构)平台的快速二维沃尔什变换(Walsh Transform)实现方法.该方法利用GPU的并行结构和硬件特点

从算法实现、存储类型、逻辑构架设置等方面提高了沃尔什变换的运算速度.实验结果表明

随着图像分辨率的增加

沃尔什变换在GPU上运行时间远低于CPU

GPU比CPU具有更明显的加速效果.

Abstract

Fast 2-D Walsh Transformation algorithm is presented based on NVIDIAs GPU which support Compute Unified Device Architecture (CUDA) .On the basis of the parallel architectureand hardware characteristic of GPU

the paper introduces three methods to improve the implementation performance:optimizing algorithm

texture Storage technology

and setting up logic Device Architecture.The experiment result shows that with the increasing of picture resolution

the runtime of 2-D Walsh Transformation based on GPU is far fewer than on the CPU.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design of ultra-low power real-time clock circuits

Implementation and verification of a dual-chain power gating strategy for low power design

Lightweight countermeasures against Spectre attacks on the branch prediction unit

A feature-oriented context understanding and slice analysis network for tuberculosis detection

Steganography model guided by attention mechanism and improved dense convolution

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰