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GPU Algorithms for Efficient Exascale Discretizations
arXiv - CS - Numerical Analysis Pub Date : 2021-09-10 , DOI: arxiv-2109.05072
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, Thilina Rathnayake, Robert Rieben, Thomas Stitt, Ananias Tomboulides, Stanimire Tomov, Vladimir Tomov, Arturo Vargas, Tim Warburton, Kenneth Weiss

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.

中文翻译:

用于高效 Exascale 离散化的 GPU 算法

在本文中,我们描述了美国 Exascale 计算项目内的高效 Exascale 离散化中心的研究和开发活动,针对 GPU 加速平台上的高阶应用程序的最先进的高阶有限元算法。我们讨论了 CEED 软件堆栈的几个组件中的 GPU 开发,包括 libCEED、MAGMA、MFEM、libParanumal 和 Nek 项目。我们报告了 NVIDIA 和 AMD GPU 系统上几个启用 CEED 的应用程序的性能和功能改进。
更新日期:2021-09-14
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