TY - JOUR
T1 - GPU algorithms for Efficient Exascale Discretizations
AU - Abdelfattah, Ahmad
AU - Barra, Valeria
AU - Beams, Natalie
AU - Bleile, Ryan
AU - Brown, Jed
AU - Camier, Jean Sylvain
AU - Carson, Robert
AU - Chalmers, Noel
AU - Dobrev, Veselin
AU - Dudouit, Yohann
AU - Fischer, Paul
AU - Karakus, Ali
AU - Kerkemeier, Stefan
AU - Kolev, Tzanio
AU - Lan, Yu Hsiang
AU - Merzari, Elia
AU - Min, Misun
AU - Phillips, Malachi
AU - Rathnayake, Thilina
AU - Rieben, Robert
AU - Stitt, Thomas
AU - Tomboulides, Ananias
AU - Tomov, Stanimire
AU - Tomov, Vladimir
AU - Vargas, Arturo
AU - Warburton, Tim
AU - Weiss, Kenneth
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Exascale applications
KW - Finite element methods
KW - GPU acceleration
KW - High-order discretizations
KW - High-performance computing
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U2 - 10.1016/j.parco.2021.102841
DO - 10.1016/j.parco.2021.102841
M3 - Article
AN - SCOPUS:85115934300
SN - 0167-8191
VL - 108
JO - Parallel Computing
JF - Parallel Computing
M1 - 102841
ER -