Combining performance aspects of irregular gauss-seidel via sparse tiling

  • Authors:

  • Michelle Mills Strout;Larry Carter;Jeanne Ferrante;Jonathan Freeman;Barbara Kreaseck

  • Affiliations:

  • University of California, San Diego, CA;University of California, San Diego, CA;University of California, San Diego, CA;University of California, San Diego, CA;University of California, San Diego, CA

  • Venue:
  • LCPC'02 Proceedings of the 15th international conference on Languages and Compilers for Parallel Computing
  • Year:
  • 2002

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Abstract

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.