GPU Algorithms for Efficient Exascale Discretizations
Ahmad Abdelfattah, Valeria Barra, Natalie Beams, Ryan Bleile, Jed, Brown, Jean-Sylvain Camier, Robert Carson, Noel Chalmers, Veselin Dobrev,, Yohann Dudouit, Paul Fischer, Ali Karakus, Stefan Kerkemeier, Tzanio Kolev,, Yu-Hsiang Lan, Elia Merzari, Misun Min, Malachi Phillips
TL;DR
This paper discusses GPU-accelerated high-order finite-element algorithms developed for exascale computing, highlighting software stack improvements and performance gains on NVIDIA and AMD GPUs.
Contribution
It introduces new GPU algorithms and software enhancements for high-order finite-element methods tailored for exascale systems, advancing computational efficiency.
Findings
Performance improvements on NVIDIA GPUs
Capability enhancements on AMD GPUs
Successful application in high-order finite-element problems
Abstract
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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