# AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters

## TL;DR

This paper presents AFiD-GPU, a GPU-accelerated solver for wall-bounded turbulent flows that significantly reduces computation time and enables new parameter ranges, verified through benchmark simulations.

## Contribution

The paper introduces a GPU port of the AFiD solver with a novel transpose scheme, achieving high scalability and performance for large-scale turbulence simulations.

## Key findings

- GPU version reduces wall clock time by an order of magnitude
- Enables simulations in unprecedented parameter ranges
- Results agree well with experimental and previous computational data

## Abstract

The AFiD code, an open source solver for the incompressible Navier-Stokes equations ({\color{blue}\burl{http://www.afid.eu}}), has been ported to GPU clusters to tackle large-scale wall-bounded turbulent flow simulations. The GPU porting has been carried out in CUDA Fortran with the extensive use of kernel loop directives (CUF kernels) in order to have a source code as close as possible to the original CPU version; just a few routines have been manually rewritten. A new transpose scheme, which is not limited to the GPU version only and can be generally applied to any CFD code that uses pencil distributed parallelization, has been devised to improve the scaling of the Poisson solver, the main bottleneck of incompressible solvers. The GPU version can reduce the wall clock time by an order of magnitude compared to the CPU version for large meshes. Due to the increased performance and efficient use of memory, the GPU version of AFiD can perform simulations in parameter ranges that are unprecedented in thermally-driven wall-bounded turbulence. To verify the accuracy of the code, turbulent Rayleigh-B\'enard convection and plane Couette flow are simulated and the results are in good agreement with the experimental and computational data that published in previous literatures.