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.
翻译:利用非结构化电网的PDE离散化领域实现高效率的少数可行办法之一是在无结构化电网上使用无矩阵/部分组合的高阶有限要素方法,因为这些方法可以提高数据运动减少而导致的精确度和(或)减少计算时间。在本文中,我们概述了高效散变中心(CEED)的研发活动,该中心是Exasial 电子化项目的一个共同设计中心,侧重于开发下一代离散软件和算法,以便能够使用广泛的有限要素应用来高效运行未来的硬件。中东欧司是一个研究伙伴关系,涉及来自美国两家实验室和五所大学的30多名计算科学家,包括Nek 5000、MFEM、MAMA和PETCP的不固定化成员。我们与中东欧司的大规模平流化应用,还讨论以IMFEM、MMA和PETOC的外部精细化为基准的外部平流化应用。我们还讨论了中东欧司域域域图的大型平流化结构研究、高层次的升级数据库和基础,我们作为基础的大型平流化结构研究领域,我们为了中、高层次的中央司级数据库和高层次的高级结构研究提供了高层次数据库和高层次的进度分析活动。