GPU algorithms for Efficient Exascale Discretizations

Date

2021-12-01

Author

Abdelfattah, Ahmad
Barra, Valeria
Beams, Natalie
Bleile, Ryan
Brown, Jed
Camier, Jean-Sylvain
Carson, Robert
Chalmers, Noel
Dobrev, Veselin
Dudouit, Yohann
Fischer, Paul
Karakuş, 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

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

High-performance computing, GPU acceleration, High-order discretizations

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115934300&origin=inward
https://hdl.handle.net/11511/93985

Journal

Parallel Computing

DOI

https://doi.org/10.1016/j.parco.2021.102841

Collections

Department of Mechanical Engineering, Article

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Citation Formats

A. Abdelfattah et al., “GPU algorithms for Efficient Exascale Discretizations,” Parallel Computing, vol. 108, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85115934300&origin=inward.