Introduction to AMReX - A New Framework for Block-Structured Adaptive Mesh Refinement Calculations

Speaker: Andrew Myers, Lawrence Berkeley National Laboratory
July 3, 2018

Presentation

Abstract

AMReX is a new software framework that supports the development of block-structured adaptive mesh refinement (AMR) algorithms for solving systems of partial differential equations on exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving the essentially local descriptions of different physical processes in complex multiphysics algorithms.

AMReX supports solution strategies ranging from level-by-level approaches (with or without subcycling in time) with multilevel synchronization to full-hierarchy approaches, and any combination thereof. In addition to the conventional representation of field variables on a cell, edge, face, or node-centered mesh, AMReX provides support for particle data, embedded boundary (cut cell) representations of complex geometries, and linear solvers. Both particles and embedded boundary representations introduce additional irregularity and complexity to the way data is stored and operated on, requiring special attention in the presence of the dynamically changing hierarchical mesh structure and AMR time-stepping approaches.

AMReX provides the basis for the temporal and spatial discretization strategy for a large number of applications. Current applications include accelerator design, astrophysics, combustion, cosmology, microfluidics, materials science, and multiphase flow. In this talk I will give an overview of the software components provided by AMReX and how those components are used to support different applications that use the framework.

Biography

Andrew Myers is one of the core AMReX developers and contributes to several of the AMReX-supported application codes. He holds a BS in Physics from the University of North Carolina at Chapel Hill and a PhD in Physics from the University of California at Berkeley, where he used AMR radiation-hydrodynamic simulations to study star formation in the interstellar medium. He joined LBNL in 2014 as a postdoctoral researcher, where he worked on high order adaptive-mesh particle-in-cell methods for cosmological and plasma physics applications. Since 2017, he has worked as a Computer Systems Engineer at the Center for Computational Sciences and Engineering at Lawrence Berkeley National Laboratory.

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