Efficient algebraic multigrid preconditioners on clusters of GPUs

dc.contributor.authorAbdullahi Hassan, Ambra
dc.contributor.authorCardellini, Valeria
dc.contributor.authorD'Ambra, Pasqua
dc.contributor.authorDi Serafino, Daniela
dc.contributor.authorFilippone, Salvatore
dc.date.accessioned2019-05-21T18:17:37Z
dc.date.available2019-05-21T18:17:37Z
dc.date.issued2019-05-10
dc.description.abstractMany scientific applications require the solution of large and sparse linear systems of equations using Krylov subspace methods; in this case, the choice of an effective preconditioner may be crucial for the convergence of the Krylov solver. Algebraic MultiGrid (AMG) methods are widely used as preconditioners, because of their optimal computational cost and their algorithmic scalability. The wide availability of GPUs, now found in many of the fastest supercomputers, poses the problem of implementing efficiently these methods on high-throughput processors. In this work we focus on the application phase of AMG preconditioners, and in particular on the choice and implementation of smoothers and coarsest-level solvers capable of exploiting the computational power of clusters of GPUs. We consider block-Jacobi smoothers using sparse approximate inverses in the solve phase associated with the local blocks. The choice of approximate inverses instead of sparse matrix factorizations is driven by the large amount of parallelism exposed by the matrix-vector product as compared to the solution of large triangular systems on GPUs. The selected smoothers and solvers are implemented within the AMG preconditioning framework provided by the MLD2P4 library, using suitable sparse matrix data structures from the PSBLAS library. Their behaviour is illustrated in terms of execution speed and scalability, on a test case concerning groundwater modelling, provided by the Jülich Supercomputing Center within the Horizon 2020 Project EoCoE.en_UK
dc.identifier.citationAbdullahi Hassan A, Cardellini V, D’Ambra P, et al., (2019) Efficient algebraic multigrid preconditioners on clusters of GPUs. Parallel Processing Letters, Volume 29, Issue 1, 2019, Article Number 1950001en_UK
dc.identifier.issn0129-6264
dc.identifier.urihttps://doi.org/10.1142/S0129626419500014
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14192
dc.language.isoenen_UK
dc.publisherWorld Scientific Publishingen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectClusters of GPUsen_UK
dc.subjectalgebraic multigriden_UK
dc.subjectblock-Jacobi smoothersen_UK
dc.subjectsparse approximate inversesen_UK
dc.titleEfficient algebraic multigrid preconditioners on clusters of GPUsen_UK
dc.typeArticleen_UK

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