AFiD-GPU: A versatile Navier–Stokes solver for wall-bounded turbulent flows on GPU clusters


The AFiD code, an open source solver for the incompressible Navier–Stokes equations (, 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 has been devised to improve the scaling of the Poisson solver, which is the main bottleneck of incompressible solvers. For large meshes the GPU version of the code shows good strong scaling characteristics, and the wall-clock time per step for the GPU version is an order of magnitude smaller than for the CPU version of the code. 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–Bénard convection and plane Couette flow are simulated and the results are in excellent agreement with the experimental and computational data that have been published in literature.



GPU, Parallelization, Turbulent flows, Finite-difference scheme, Rayleigh-Benard convection, Plane Couette flow


Copyright 2017 Computer Physics Communications. This is a pre-print version of a published paper that is aviailable at : Recommended citation:Zhu, Xiaojue, Everett Phillips, Vamsi Spandan, John Donners, Gregory Ruetsch, Joshua Romero, Rodolfo Ostilla-Mónico et al. "AFiD-GPU: a versatile Navier–Stokes solver for wall-bounded turbulent flows on GPU clusters." Computer physics communications 229 (2018): 199-210.doi:10.1016/j.cpc.2018.03.026. This item has been deposited in accordance with publisher copyright and licensing terms and with the author's permission.