A Parallel Algebraic Multigrid Solver on Graphics Processing Units

Gundolf Haase, Manfred Liebmann, Craig C. Douglas, Gernot Plank

Research output: Chapter in Book/Report/Conference proceedingChapter

54 Scopus citations


The 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.
Original languageEnglish (US)
Title of host publicationHigh Performance Computing and Applications
PublisherSpringer Nature
Number of pages10
ISBN (Print)9783642118418
StatePublished - 2010
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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