Parallel preconditioned conjugate gradient algorithm on GPU

Rudi Helfenstein, Jonas Koko
Clermont Universite, Universite Blaise Pascal, LIMOS, BP 10448, F-63000 Clermont-Ferrand, France
Journal of Computational and Applied Mathematics (April 2011)


   title={Parallel preconditioned conjugate gradient algorithm on GPU},

   author={Helfenstein, R. and Koko, J.},

   journal={Journal of Computational and Applied Mathematics},





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We propose a parallel implementation of the Preconditioned Conjugate Gradient algorithm on a GPU-platform. The preconditioning matrix is an approximate inverse derived from the SSOR preconditioner. Used through sparse matrix-vector multiplication, the proposed preconditioner is well-suited for the massively parallel GPU architecture. As compared to CPU implementation of the conjugate gradient algorithm, our GPU preconditioned conjugate gradient implementation is up to 10 times faster (8 times faster at worst).
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