Astrophysics on GPUs: introducing AGILE 1.0

2026/07/21 by Oliver Porth, Adrian Kelly, Olaf Willocx +10
#astro-ph.IM #astro-ph.HE #astro-ph.SR

paper · pdf

Abstract

We present AGILE, a GPU-enabled adaptive mesh refinement (AMR) framework for the solution of (near-) conservation laws which occur in astro- and solar-physical applications. AGILE is written in modern fortran 2003, inherits a part of its modules and mesh handling from MPI-AMRVAC, and achieves excellent GPU performance via OpenACC offloading. We here discuss the design decisions which enable AGILE to perform cost-efficient and scalable deeply nested AMR simulations with moderate block sizes of e.g. 16³ cells. AGILE currently implements several physics modules, ie. hydrodynamics, frozen-field hydrodynamics, magnetohydrodynamics and special-relativistic hydrodynamics and can easily be extended further through its modular design. Besides strong scaling tests to up to 2048 GPUs and standard benchmarks which show consistent performance across a large range of devices and problem sizes, we demonstrate AGILE's capabilities by means of state-of-the art science applications with all currently available physics modules.

Citations

• Observational Evidence of Active Galactic Nuclei Feedback
• Hyperbolic Divergence Cleaning for the MHD Equations

Related

• RAMSES-GPU: Cell-by-Cell Adaptive Mesh Refinement with Magneto-Hydrodynamics and Self-Gravity on Graphics Processing Units
• The Kratos Framework for Heterogeneous Astrophysical Simulations: Fundamental Infrastructures and Hydrodynamics
• Characterizing GPU Energy Usage in Exascale-Ready Portable Science Applications
• GPU accelerated MHD in the DISPATCH framework using directive-based programming
• Asynchronous-Many-Task Systems: Challenges and Opportunities -- Scaling an AMR Astrophysics Code on Exascale machines using Kokkos and HPX