Combining Performance Aspects of Irregular Gauss-Seidel Via Sparse Tiling

  • Michelle Mills Strout
  • Larry Carter
  • Jeanne Ferrante
  • Jonathan Freeman
  • Barbara Kreaseck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2481)


Finite Element problems are often solved using multigrid techniques. The most time consuming part of multigrid is the iterative smoother, such as Gauss-Seidel. To improve performance, iterative smoothers can exploit parallelism, intra-iteration data reuse, and inter-iteration data reuse. Current methods for parallelizing Gauss-Seidel on irregular grids, such as multi-coloring and owner-computes based techniques, exploit parallelism and possibly intra-iteration data reuse but not inter-iteration data reuse. Sparse tiling techniques were developed to improve intra-iteration and inter-iteration data locality in iterative smoothers. This paper describes how sparse tiling can additionally provide parallelism. Our results show the effectiveness of Gauss-Seidel parallelized with sparse tiling techniques on shared memory machines, specifically compared to owner-computes based Gauss-Seidel methods. The latter employ only parallelism and intra-iteration locality. Our results support the premise that better performance occurs when all three performance aspects (parallelism, intra-iteration, and inter-iteration data locality) are combined.


Sparse Matrix Iteration Space Iteration Point Partition Numbering Data Reuse 
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

  • Michelle Mills Strout
    • 1
  • Larry Carter
    • 1
  • Jeanne Ferrante
    • 1
  • Jonathan Freeman
    • 1
  • Barbara Kreaseck
    • 1
  1. 1.University of CaliforniaSan DiegoUSA

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