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GPU Algorithms for Efficient Exascale Discretizations

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arxiv 2109.05072 v1 pith:RLGPNV4M submitted 2021-09-10 cs.DC cs.MScs.NAmath.NA

GPU Algorithms for Efficient Exascale Discretizations

Ahmad Abdelfattah , Valeria Barra , Natalie Beams , Ryan Bleile , Jed Brown , Jean-Sylvain Camier , Robert Carson , Noel Chalmers
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Veselin Dobrev Yohann Dudouit Paul Fischer Ali Karakus Stefan Kerkemeier Tzanio Kolev Yu-Hsiang Lan Elia Merzari Misun Min Malachi Phillips Thilina Rathnayake Robert Rieben Thomas Stitt Ananias Tomboulides Stanimire Tomov Vladimir Tomov Arturo Vargas Tim Warburton Kenneth Weiss
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classification cs.DC cs.MScs.NAmath.NA
keywords exascalealgorithmsapplicationsefficienthigh-orderseveralactivitiescapability
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In this paper we describe the research and development activities in the Center for Efficient Exascale Discretization within the US Exascale Computing Project, targeting state-of-the-art high-order finite-element algorithms for high-order applications on GPU-accelerated platforms. We discuss the GPU developments in several components of the CEED software stack, including the libCEED, MAGMA, MFEM, libParanumal, and Nek projects. We report performance and capability improvements in several CEED-enabled applications on both NVIDIA and AMD GPU systems.

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