Finite-element sparse matrix vector multiplication on graphic processing units

Maryam Mehri Dehnavi, David M. Fernández, Dennis Giannacopoulos

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

A wide class of finite-element (FE) electromagnetic applications requires computing very large sparse matrix vector multiplications (SMVM). Due to the sparsity pattern and size of the matrices, solvers can run relatively slowly. The rapid evolution of graphic processing units (GPUs) in performance, architecture, and programmability make them very attractive platforms for accelerating computationally intensive kernels such as SMVM. This work presents a new algorithm to accelerate the performance of the SMVM kernel on graphic processing units.

Original languageEnglish (US)
Article number5512911
Pages (from-to)2982-2985
Number of pages4
JournalIEEE Transactions on Magnetics
Volume46
Issue number8
DOIs
Publication statusPublished - Aug 1 2010

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All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Keywords

  • Computer architecture
  • graphic processing units (GPUs)
  • parallel processing
  • sparse matrix vector multiplication (SMVM)

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