AMReX and pyAMReX: Looking beyond the exascale computing project

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

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Publication Details

Journal
The International Journal of High Performance Computing Applications
Published
2024-08-29
DOI
https://doi.org/10.1177/10943420241271017
Citations
7
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
2.44

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article

AMReX and pyAMReX: Looking beyond the exascale computing project

Ann Almgren, Thierry Antoun, Axel Huebl, Weiqun Zhang et al.
7 citations
The International Journal of High Performance Computing Applications
Advanced Data Storage Technologies
2.44
article

AMReX and pyAMReX: Looking beyond the exascale computing project

Ann Almgren, Thierry Antoun, Axel Huebl, Weiqun Zhang, John B. Bell, Alexander Sinn, Andrew T. Myers
article en
7 citations

Abstract

AMReX is a software framework for the development of block-structured mesh applications with adaptive mesh refinement (AMR). AMReX was initially developed and supported by the AMReX Co-Design Center as part of the U.S. DOE Exascale Computing Project (ECP), and is continuing to grow post-ECP. In addition to adding new functionality and performance improvements to the core AMReX framework, we have also developed a Python binding, pyAMReX, that provides a bridge between AMReX-based application codes and the data science ecosystem. pyAMReX provides zero-copy application GPU data access for AI/ML, in situ analysis and application coupling, and enables rapid, massively parallel prototyping. In this paper we review the overall functionality of AMReX and pyAMReX, focusing on new developments, new functionality, and optimizations of key operations. We also summarize capabilities of ECP projects that used AMReX and provide an overview of new, non-ECP applications.

The International Journal of High Performance Computing ApplicationsVol. 38(6)
Lawrence Berkeley National Laboratory (US), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Deutsches Elektronen-Synchrotron DESY (DE)
U.S. Department of Energy, Office of Science, National Nuclear Security Administration, Advanced Scientific Computing Research, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory
Openalex Percentile: Top 10%
Advanced Data Storage Technologies
2.44
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