Vectorized Sparse Matrix Multiply for Compressed Row Storage Format

  • Eduardo F. D’Azevedo
  • Mark R. Fahey
  • Richard T. Mills
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3514)


The innovation of this work is a simple vectorizable algorithm for performing sparse matrix vector multiply in compressed sparse row (CSR) storage format. Unlike the vectorizable jagged diagonal format (JAD), this algorithm requires no data rearrangement and can be easily adapted to a sophisticated library framework such as PETSc. Numerical experiments on the Cray X1 show an order of magnitude improvement over the non-vectorized algorithm.


Sparse Matrix Point Stencil Vector Processor Permutation Vector Theoretical Peak Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eduardo F. D’Azevedo
    • 1
  • Mark R. Fahey
    • 2
  • Richard T. Mills
    • 2
  1. 1.Computer Science and Mathematics DivisionOak Ridge National LaboratoryOak RidgeUSA
  2. 2.Center for Computational SciencesOak Ridge National LaboratoryOak RidgeUSA

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