Efficient exascale discretizations: High-order finite element methods
Efficient exascale discretizations: High-order finite element methods
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DOI：
10.1177/10943420211020803
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发表时间：
2021-06
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通讯作者：
T. Kolev;P. Fischer;M. Min;J. Dongarra;Jed Brown;V. Dobrev;T. Warburton;S. Tomov;M. Shephard;A. Abdelfattah;V. Barra;Natalie N. Beams;Jean-Sylvain Camier;N. Chalmers;Yohann Dudouit;A. Karakus;I. Karlin;S. Kerkemeier;Yu-Hsiang Lan;David S. Medina;E. Merzari;A. Obabko;Will Pazner;T. Rathnayake;Cameron W. Smith;Lukas Spies;K. Swirydowicz;Jeremy L. Thompson;A. Tomboulides;V. Tomov
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文献类型：
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作者：
T. Kolev;P. Fischer;M. Min;J. Dongarra;Jed Brown;V. Dobrev;T. Warburton;S. Tomov;M. Shephard;A. Abdelfattah;V. Barra;Natalie N. Beams;Jean-Sylvain Camier;N. Chalmers;Yohann Dudouit;A. Karakus;I. Karlin;S. Kerkemeier;Yu-Hsiang Lan;David S. Medina;E. Merzari;A. Obabko;Will Pazner;T. Rathnayake;Cameron W. Smith;Lukas Spies;K. Swirydowicz;Jeremy L. Thompson;A. Tomboulides;V. Tomov
要有效地利用百亿亿级架构，需要重新考虑许多大规模应用中使用的数值算法。这些体系结构有利于暴露超细粒度并行性并最大化浮点操作与能源密集型数据移动的比率的算法。在非结构化网格上的PDE离散化区域中实现高效率的少数可行方法之一是使用无矩阵/部分组装的高阶有限元方法，因为这些方法可以由于减少数据运动而提高精度和/或降低计算时间。在本文中，我们提供了一个概述的研究和开发活动的中心，为有效的Exascale离散化（CEED），协同设计中心的Exascale计算项目，专注于下一代离散化软件和算法的开发，使广泛的有限元应用程序，以有效地运行在未来的硬件。CEED是一个研究合作伙伴关系，涉及来自美国两个国家实验室和五所大学的30多名计算科学家，包括Nek 5000，MFEM，MAGMA和PETSc项目的成员。我们讨论了CEED基于目标基准测试、小型应用程序和离散化库的协同设计活动，以及我们在大规模GPU架构性能优化方面的工作。我们还提供了一个广泛的研究和开发活动的概述，如非结构化自适应网格细化算法，无矩阵线性求解器，高阶数据可视化，并列出与几个ECP和外部应用程序的合作的例子。
Efficient exploitation of exascale architectures requires rethinking of the numerical algorithms used in many large-scale applications. These architectures favor algorithms that expose ultra fine-grain parallelism and maximize the ratio of floating point operations to energy intensive data movement. One of the few viable approaches to achieve high efficiency in the area of PDE discretizations on unstructured grids is to use matrix-free/partially assembled high-order finite element methods, since these methods can increase the accuracy and/or lower the computational time due to reduced data motion. In this paper we provide an overview of the research and development activities in the Center for Efficient Exascale Discretizations (CEED), a co-design center in the Exascale Computing Project that is focused on the development of next-generation discretization software and algorithms to enable a wide range of finite element applications to run efficiently on future hardware. CEED is a research partnership involving more than 30 computational scientists from two US national labs and five universities, including members of the Nek5000, MFEM, MAGMA and PETSc projects. We discuss the CEED co-design activities based on targeted benchmarks, miniapps and discretization libraries and our work on performance optimizations for large-scale GPU architectures. We also provide a broad overview of research and development activities in areas such as unstructured adaptive mesh refinement algorithms, matrix-free linear solvers, high-order data visualization, and list examples of collaborations with several ECP and external applications.