A Parallel Algebraic Multigrid Solver on Graphics Processing Units
KAUST Grant NumberKUS-C1-016-04
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AbstractThe paper presents a multi-GPU implementation of the preconditioned conjugate gradient algorithm with an algebraic multigrid preconditioner (PCG-AMG) for an elliptic model problem on a 3D unstructured grid. An efficient parallel sparse matrix-vector multiplication scheme underlying the PCG-AMG algorithm is presented for the many-core GPU architecture. A performance comparison of the parallel solver shows that a singe Nvidia Tesla C1060 GPU board delivers the performance of a sixteen node Infiniband cluster and a multi-GPU configuration with eight GPUs is about 100 times faster than a typical server CPU core. © 2010 Springer-Verlag.
CitationHaase G, Liebmann M, Douglas CC, Plank G (2010) A Parallel Algebraic Multigrid Solver on Graphics Processing Units. High Performance Computing and Applications: 38–47. Available: http://dx.doi.org/10.1007/978-3-642-11842-5_5.
SponsorsThis publication is based on work supported in part by NSF grants OISE-0405349, ACI-0305466, CNS-0719626, and ACI-0324876, by DOE project DE-FC26-08NT4, by FWF project SFB032, by BMWF project AustrianGrid 2, and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
PublisherSpringer Science + Business Media