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European Conference on Parallel Processing

Euro-Par 2011: Euro-Par 2011: Parallel Processing Workshops pp 471–480Cite as

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Parallel Sparse Linear Solver GMRES for GPU Clusters with Compression of Exchanged Data

Parallel Sparse Linear Solver GMRES for GPU Clusters with Compression of Exchanged Data

  • Jacques M. Bahi30,
  • Raphaël Couturier30 &
  • Lilia Ziane Khodja30 
  • Conference paper
  • 1399 Accesses

  • 2 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7155)

Abstract

GPU clusters have become attractive parallel platforms for high performance computing due to their ability to compute faster than the CPU clusters. We use this architecture to accelerate the mathematical operations of the GMRES method for solving large sparse linear systems. However the parallel sparse matrix-vector product of GMRES causes overheads in CPU/CPU and GPU/CPU communications when exchanging large shared vectors of unknowns between GPUs of the cluster. Since a sparse matrix-vector product does not often need all the unknowns of the vector, we propose to use data compression and decompression operations on the shared vectors, in order to exchange only the needed unknowns. In this paper we present a new parallel GMRES algorithm for GPU clusters, using compression vectors. Our experimental results show that the GMRES solver is more efficient when using the data compression technique on large shared vectors.

Keywords

  • GMRES
  • GPU cluster
  • CUDA
  • MPI
  • data compression

This work was supported by Région de Franche-Comté.

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References

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Author information

Authors and Affiliations

  1. LIFC laboratory, University of Franche-Comte, Rue Engel-Gros, BP 527, 90016, Belfort Cedex, France

    Jacques M. Bahi, Raphaël Couturier & Lilia Ziane Khodja

Authors
  1. Jacques M. Bahi
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  2. Raphaël Couturier
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  3. Lilia Ziane Khodja
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Editor information

Editors and Affiliations

  1. Scilytics, Koellnerhofgasse 3/15A, 1010, Vienna, Austria

    Michael Alexander

  2. ICAR-CNR, Via P. Castellino, 111, 80131, Napoli, Italy

    Pasqua D’Ambra

  3. University of Amsterdam, 1090, Amsterdam, Netherlands

    Adam Belloum

  4. Innovative Computing Laboratory, The University of Tennessee, USA

    George Bosilca

  5. Department of Experimental Medicine and Clinic, University Magna Græcia, 88100, Catanzaro, Italy

    Mario Cannataro

  6. Computer Science Department, University of Pisa, Italy

    Marco Danelutto

  7. Second University of Naples, Italy

    Beniamino Di Martino

  8. TU München, Boltzmannstr. 3, 85748, Garching, Germany

    Michael Gerndt

  9. Equipe Runtime, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Emmanuel Jeannot & Raymond Namyst & 

  10. Equipe HIEPACS, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Jean Roman

  11. Oak Ridge National Laboratory, Computer Science and Mathematics Division, 37831-6164, Oak Ridge, TN, USA

    Stephen L. Scott

  12. Department of Scientific Computing, University of Vienna, Nordbergstr. 15/3C, 1090, Vienna, Austrial

    Jesper Larsson Traff

  13. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    Geoffroy Vallée

  14. Technische Universität München, Germany

    Josef Weidendorfer

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Bahi, J.M., Couturier, R., Khodja, L.Z. (2012). Parallel Sparse Linear Solver GMRES for GPU Clusters with Compression of Exchanged Data. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29737-3_52

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  • DOI: https://doi.org/10.1007/978-3-642-29737-3_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29736-6

  • Online ISBN: 978-3-642-29737-3

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