About ADAM

ADAM (Accelerated fluid Dynamics on Adaptive Mesh refinement grids) is a high-performance fluid dynamics framework written in modern Fortran for device(GPU)-accelerated HPC simulations and traditional CPU-based superpc. It provides physics-agnostic SDK library offering AMR, IB, WENO, RK, parellel I/O, ecc... on top of which is easy to develop CFD solvers (currently a compressible Navier-Stokes solver named NASTO and an electromagnetic plasma solver named PRSIM are the principal applications developed with ADAM).

The primary focus is on large-scale scientific computing: ADAM targets exascale deployments on device-accelerated clusters, where thousands of devices (GPU) nodes work in concert via MPI with node-level parallelism provided by CUDA Fortran, OpenACC, or OpenMP offloading.

Key capabilities

• AMR — Morton-order linearized octree/quadtree with automatic refinement and coarsening
• Immersed Boundary — complex and moving geometries without body-fitted meshes
• High-order WENO — finite difference reconstructions up to 5th order for shock-capturing flows
• Multi-backend GPU — a single codebase compiles for CUDA Fortran (NVF), OpenACC (FNL), and OpenMP offloading (GMP)
• Parallel I/O — HDF5 output and restart files via MPI-IO

Applications

Application	Description
NASTO	Compressible 3D Navier-Stokes solver
PRISM	Maxwell equations solver for electromagnetic simulations
CHASE	CFD application
PATCH	Patch-based application
ASCOT	Binary-to-ASCII output converter

Authors

• Andrea di Mascio — andrea.dimascio@univaq.it
• Federico Negro — federico.negro.01@gmail.com
• Giacomo Rossi — giacomo.rossi@amd.com
• Francesco Salvadore — f.salvadore@cineca.it
• Stefano Zaghi — stefano.zaghi@cnr.it

Copyrights

ADAM is dual-licensed under the MIT License and the GNU Lesser General Public License v3.0 (LGPLv3). You may choose either license.

Copyright (C) Andrea Di Mascio, Federico Negro, Giacomo Rossi, Francesco Salvadore, Stefano Zaghi.

Citing ADAM

If you use ADAM in work that leads to a scientific publication, please cite the following paper:

S. Zaghi, F. Salvadore, A. Di Mascio, G. Rossi — Efficient GPU parallelization of adaptive mesh refinement technique for high-order compressible solver with immersed boundary — Computers and Fluids, 266 (2023) 106040. DOI: 10.1016/j.compfluid.2023.106040

The paper describes the ADAM framework architecture, the AMR/IB coupling strategy, the GPU parallelization approach (CUDA Fortran), and demonstrates strong scaling on a shock–sphere interaction benchmark. A preprint is available in docs/papers/zaghi-2023-computer_fluids.pdf.

BibTeX entry:

@article{zaghi2023adam,
  author  = {Zaghi, S. and Salvadore, F. and {Di Mascio}, A. and Rossi, G.},
  title   = {Efficient {GPU} parallelization of adaptive mesh refinement technique
             for high-order compressible solver with immersed boundary},
  journal = {Computers \& Fluids},
  volume  = {266},
  pages   = {106040},
  year    = {2023},
  doi     = {10.1016/j.compfluid.2023.106040},
}