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Journal Article

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


Lohse,  Detlef
Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Zhu, X., Phillips, E., Spandan, V., Donners, J., Ruetsch, G., Romero, J., et al. (2018). AFiD-GPU: A versatile Navier-Stokes solver for wall-bounded turbulent flows on GPU clusters. Computer Physics Communications, 229, 199-210. doi:10.1016/j.cpc.2018.03.026.

Cite as: https://hdl.handle.net/21.11116/0000-0001-B9BB-C
The AFiD code, an open source solver for the incompressible Navier-Stokes equations (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 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-Benard 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.