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Parallel Optimization for Sparse Matrix–Vector on GPU

  • Meng Jia Yin
  • Xian Bin Xu
  • Hua Chen
  • Shui Bing He
  • Jing Hu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 217)

Abstract

Graphics processing units (GPUs) have been used in the general-purpose computation field. Sparse matrix–vector multiplication (SpMV) algorithm is one of the most important scientific computing kernel algorithms. In this paper, we discuss implementing optimizing sparse matrix–vector multiplication on GPUs using CUDA programming model. We used methods and strategy which including mapping thread, merging access, reusing data, and avoiding the branch. The experimental results show that the optimizations strategy to improve SpMV performance.

Keywords

Sparse matrix–vector multiplication (SpMV) Compute unified device architecture (CUDA) Graphics processing unit (GPU) Performance optimizations strategy (POS) 

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Meng Jia Yin
    • 1
    • 2
  • Xian Bin Xu
    • 1
    • 3
  • Hua Chen
    • 4
  • Shui Bing He
    • 1
    • 5
  • Jing Hu
    • 1
  1. 1.School of ComputerWuhan UniversityWuhanPeople’s Republic of China
  2. 2.School of Computer and Information ScienceHubei Engineering UniversityXiaoGanPeople’s Republic of China
  3. 3.School of Computer ScienceWuhan Donghu UniversityWuhanPeople’s Republic of China
  4. 4.Agricultural Bank of China Software Development Center Guangzhou Sub-centerPanyu, GuangzhouPeople’s Republic of China
  5. 5.Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of EducationBeijingPeople’s Republic of China

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