A Survey of High Level Frameworks in Block-Structured Adaptive Mesh Refinement Packages

TL;DR

This survey reviews prominent block-structured adaptive mesh refinement frameworks, analyzing their design, evolution, and hardware adaptability across various scientific domains.

Contribution

It provides a comparative analysis of major SAMR packages and codes, highlighting their high-level frameworks and approaches to hardware changes.

Findings

Analyzed six major SAMR frameworks and codes.

Compared their adaptability to hardware architecture changes.

Identified differences in domain focus and generality.

Abstract

Over the last decade block-structured adaptive mesh refinement (SAMR) has found increasing use in large, publicly available codes and frameworks. SAMR frameworks have evolved along different paths. Some have stayed focused on specific domain areas, others have pursued a more general functionality, providing the building blocks for a larger variety of applications. In this survey paper we examine a representative set of SAMR packages and SAMR-based codes that have been in existence for half a decade or more, have a reasonably sized and active user base outside of their home institutions, and are publicly available. The set consists of a mix of SAMR packages and application codes that cover a broad range of scientific domains. We look at their high-level frameworks, and their approach to dealing with the advent of radical changes in hardware architecture. The codes included in this survey are BoxLib, Cactus, Chombo, Enzo, FLASH, and Uintah.