AMReX: Block-structured adaptive mesh refinement for multiphysics applications. (November 2021)

• Record Type: Journal Article
• Authors: Zhang, Weiqun; Myers, Andrew; Gott, Kevin; Almgren, Ann; Bell, John
• Other Names: Germann Tim guest-editor.
• Is Part Of: International journal of high performance computing applications. Volume 35:Number 6(2021)
• Page Start: 508
• Page End: 526
• Publication Date: 2021-11
• Subjects: Adaptive mesh refinement -- co-design -- particles -- performance portability -- structured mesh/grids; High performance computing -- Periodicals; Supercomputers -- Periodicals; 004.1105
• DOI: 10.1177/10943420211022811
• Languages: English
• ISSNs: 1094-3420

Abstract

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.