GPU-accelerated sparse matrix-vector product for a hybridizable discontinuous Galerkin method
Tipus de documentText en actes de congrés
Condicions d'accésAccés restringit per política de l'editorial
The iterative solution of the large systems of equations that result from discontinuous Galerkin (DG) discretizations require the ability to carry out fast matrix-vector products. DG matrices have a sparse block structure with a constant number of non-zero equal-sized non-overlapping blocks per row. General-purpose sparse matrix-vector product algorithms are not designed to exploit the speci c structure of the DG matrices and, as a consequence, result in sub-optimal performance. To address this issue, we propose a sparse matrix-vector product for DG discretizations based on a dense tensor contraction. A GPU implementation of the proposed algorithm for a hybridizable discontinuous Galerkin (HDG) method is tested on the NVIDIA GEFORCE GTX 285. The results show that the tensor contraction performs at about 20 to 25 GFLOP/s in double precision with a sustained efficiency of more than 40% (60 GBytes/s) of the peak memory bandwidth (160 GBytes/s). Moreover, for HDG matrices in double precision, the proposed method is 2 times faster than the general sparse matrix-vector products provided by the GPU library CUSPARSE and about 30 times faster than MATLAB running on a CPU.
CitacióRoca, X.; Nguyeny, N.; Peraire, J. GPU-accelerated sparse matrix-vector product for a hybridizable discontinuous Galerkin method. A: AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. "49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition". Orlando, Florida: 2011, p. 1-12.
|RocaNguyenPeraireAIAA2011.pdf||Article principal||1019.Kb||Accés restringit|