AMReX: Block-Structured Adaptive Mesh Refinement for Multiphysics Applications
September 25, 2020 ยท Declared Dead ยท ๐ The international journal of high performance computing applications
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Weiqun Zhang, Andrew Myers, Kevin Gott, Ann Almgren, John Bell
arXiv ID
2009.12009
Category
cs.MS: Mathematical Software
Cross-listed
cs.CE,
cs.DC
Citations
124
Venue
The international journal of high performance computing applications
Last Checked
1 month ago
Abstract
Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of ECP applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modelling. 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 multi-physics algorithms. AMReX supports algorithms that solve systems of partial differential equations (PDEs) in simple or complex geometries, and those that use particles and/or particle-mesh operations to represent component physical processes. In this paper, 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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Mathematical Software
๐
๐
Old Age
๐
๐
Old Age
CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication
R.I.P.
๐ป
Ghosted
Mathematical Foundations of the GraphBLAS
R.I.P.
๐ป
Ghosted
The DUNE Framework: Basic Concepts and Recent Developments
R.I.P.
๐ป
Ghosted
Format Abstraction for Sparse Tensor Algebra Compilers
R.I.P.
๐ป
Ghosted
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted