GPU algorithms for Efficient Exascale Discretizations

Abdelfattah, Ahmad; Barra, Valeria; Beams, Natalie; Bleile, Ryan; Brown, Jed; Camier, Jean-Sylvain; Carson, Robert; Chalmers, Noel; Dobrev, Veselin; Dudouit, Yohann; Fischer, Paul; Karakus, Ali; Kerkemeier, Stefan; Kolev, Tzanio; Lan, Yu-Hsiang; Merzari, Elia; Min, Misun; Phillips, Malachi; Rathnayake, Thilina; Rieben, Robert; Stitt, Thomas; Tomboulides, Ananias; Tomov, Stanimire; Tomov, Vladimir; Vargas, Arturo; Warburton, Tim; Weiss, Kenneth

doi:10.1016/j.parco.2021.102841

GPU algorithms for Efficient Exascale Discretizations

Journal Article · Sat Sep 25 00:00:00 EDT 2021 · Parallel Computing

DOI:https://doi.org/10.1016/j.parco.2021.102841· OSTI ID:1845216

^[1]; ^[2]; ^[1]; Bleile, Ryan ^[3]; Brown, Jed ^[2]; ^[4]; ^[3]; ^[5]; ^[4]; ^[4]; Fischer, Paul ^[6]; Karakus, Ali ^[7]; Kerkemeier, Stefan ^[8]; ^[4]; ^[8]; Merzari, Elia ^[9]; ^[8]; ^[10]; ^[10]; ^[3] more »

Univ. of Tennessee, Knoxville, TN (United States). Innovative Computing Lab.
Univ. of Colorado, Boulder, CO (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Center for Applied Scientific Computing
Advanced Micro Devices Inc., Austin, TX (United States)
Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
Middle East Technical Univ., Ankara (Turkey)
Argonne National Lab. (ANL), Lemont, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States); Pennsylvania State Univ., University Park, PA (United States)
Univ. of Illinois at Urbana-Champaign, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States); Aristotle Univ. of Thessaloniki (Greece)
Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)

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. Furthermore, 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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: AC02-06CH11357; AC05-00OR22725; AC52-07NA27344

OSTI ID:: 1845216

Report Number(s):: LLNL-JRNL-816034; 1025529

Journal Information:: Parallel Computing, Journal Name: Parallel Computing Journal Issue: N/A Vol. 108; ISSN 0167-8191

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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