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基于CUDA的矩阵乘法和FFT性能测试
  • ISSN号：1000-3428
  • 期刊名称：《计算机工程》
  • 时间：0
  • 分类：TP312[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]
  • 作者机构：[1]中国科学院国家天文台,北京100012
  • 相关基金：国家“973”计划基金资助项目（2006CB806301）;国家自然科学基金资助项目（10473016,10673016）;中国科学院知识创新工程基金资助项目（KJCX2-YW-T04）
作者： 肖江[1], 胡柯良[1], 邓元勇[1]
关键词： 矩阵乘法, 快速傅里叶变换, 并行计算, GPU通用计算, matrix-multiplication, Fast Fourier Transform（FFT）, parallel computation, GPGPU
中文摘要：

针对NVIDIA公司的CUDA技术用Geforce8800GT在Visual Studi02008环境下进行测试，从程序运行时间比较判断CUBLAS库、CUDA内核程序、CUDA驱动API、C循环程序与Intel MKL库以及FFTW库与CUFFT库运行响应的差异。测试结果表明，在大规模矩阵乘法和快速傅里叶变换的应用方面，相对于CPU，利用GPU运算性能可提高25倍以上。

英文摘要：

IThis paper introduces the result of a test that evaluates the effectiveness of Compute Unified Device Architecture（CUDA） using NVDIA GeForce8800GT and the compiler Visual Studio 2008. It tests the speed of NVIDIA CUBLAS, CUDA kernel, common C program, Intel MKL BLAS, CUDA driver API program, FFTW and CUFFT Library in matrix-multiplication and Fast Fourier Transform（FFT）. Test result of the large scale data shows that the computing ability of GPU is 25 times better than that of CPU.

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期刊信息
  • 《计算机工程》
  • 北大核心期刊（2014版）
  • 主管单位:中国电子科技集团公司
  • 主办单位:华东计算技术研究所 上海市计算机学会
  • 主编：游小明
  • 地址：上海市桂林路418号
  • 邮编：200233
  • 邮箱：ecice06@ecict.com.cn
  • 电话：021-64846769
  • 国际标准刊号：ISSN：1000-3428
  • 国内统一刊号：ISSN：31-1289/TP
  • 邮发代号:4-310
  • 获奖情况:
  • 1999～2000、2001～2002年度信息产业部优秀期刊奖,2003-2004、2005-2006年度信息产业部电子精品科技...,2007-2008、2009-2010年度工业和信息产业部电子精...,012年度中国科技论文在线优秀期刊一等奖,2013年度中国科技论文在线优秀期刊二等奖
  • 国内外数据库收录:
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  • 被引量:84139