Computational Technology Resources - CSETS

F. Andzembe and J. Koko

LIMOS, Université Blaise Pascal – CNRS UMR 6158, Clermont-Ferrand, France

doi:10.4203/csets.34.9

F. Andzembe, J. Koko, "Parallel Preconditioned Conjugate Gradient Algorithm on GPU", in F. Magoulès, (Editor), "Patterns for Parallel Programming on GPUs", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 9, pp 209-225, 2014. doi:10.4203/csets.34.9

Keywords: preconditioned conjugate gradient, parallel computing, graphics processing unit.

Abstract

We are proposing a parallel implementation of the preconditioned conjugate gradient algorithm on a GPU-platform. The preconditioning matrix is a first order approximate inverse derived from the SSOR preconditioner. Used through sparse matrix-vector multiplication, the proposed preconditioner is well-suited for massively parallel architectures like GPUs. Compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 16 times faster (8 times faster at worst).

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Computational Science, Engineering & Technology Series ISSN 1759-3158 CSETS: 34 PATTERNS FOR PARALLEL PROGRAMMING ON GPUS Edited by: F. Magoulès Chapter 9 Parallel Preconditioned Conjugate Gradient Algorithm on GPU F. Andzembe and J. Koko LIMOS, Université Blaise Pascal – CNRS UMR 6158, Clermont-Ferrand, France doi:10.4203/csets.34.9 purchase the full-text of this chapter Full Bibliographic Reference for this chapter F. Andzembe, J. Koko, "Parallel Preconditioned Conjugate Gradient Algorithm on GPU", in F. Magoulès, (Editor), "Patterns for Parallel Programming on GPUs", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 9, pp 209-225, 2014. doi:10.4203/csets.34.9 Keywords: preconditioned conjugate gradient, parallel computing, graphics processing unit. Abstract We are proposing a parallel implementation of the preconditioned conjugate gradient algorithm on a GPU-platform. The preconditioning matrix is a first order approximate inverse derived from the SSOR preconditioner. Used through sparse matrix-vector multiplication, the proposed preconditioner is well-suited for massively parallel architectures like GPUs. Compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 16 times faster (8 times faster at worst). purchase the full-text of this chapter (price £20) go to the previous chapter go to the next chapter return to the table of contents return to the book description purchase this book (price £85 +P&P)
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