AMReX: Block-structured adaptive mesh refinement for multiphysics applications

AMReX: Block-structured adaptive mesh refinement for multiphysics applications
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AMReX:适用于多物理场应用的块结构自适应网格细化

DOI:
10.1177/10943420211022811
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发表时间:
2020
期刊:
The International Journal of High Performance Computing Applications
影响因子:
--
通讯作者:
J. Bell
J. Bell
中科院分区:
--
文献类型:
--
作者:
Weiqun Zhang;A. Myers;K. Gott;A. Almgren;J. Bell

文献摘要

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块结构自适应网格细化 (AMR) 为加速器设计、增材制造、天体物理学、燃烧、宇宙学、多相流和风力发电厂建模领域的许多百亿亿次计算项目应用提供了时间和空间离散化策略的基础。 AMReX 是一个软件框架,它提供了一个统一的基础设施,其中包含这些和其他 AMR 应用程序所需的功能,以便能够有效且高效地利用从笔记本电脑到百亿亿次架构的机器。与统一网格相比,AMR 降低了计算成本和内存占用,同时保留了复杂多物理场算法中不同物理过程的准确描述。 AMReX 支持求解简单或复杂几何形状的偏微分方程组的算法,以及使用粒子和/或粒子网格运算来表示组件物理过程的算法。在本文中,我们将讨论 AMReX 框架的核心元素,例如数据容器和迭代器,以及一些专门的操作来满足应用程序项目的需求。此外,我们将重点介绍 AMReX 团队正在追求的战略,即在一系列基于加速器的架构中为各种不同的应用程序实现高性能代码。
Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.