AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters
Physics of Fluids Group, MESA+ Institute, and J. M. Burgers Centre for Fluid Dynamics, University of Twente, PO Box 217, 7500 AE Enschede, The Netherlands
arXiv:1705.01423 [physics.flu-dyn], (3 May 2017)
@article{zhu2017afidgpu,
title={AFiD-GPU: a versatile Navier-Stokes Solver for Wall-Bounded Turbulent Flows on GPU Clusters},
author={Zhu, Xiaojue and Phillips, Everett and Spandan, Vamsi and Donners, John and Ruetsch, Gregory and Romero, Josh and Ostilla-Monico, Rodolfo and Yang, Yantao and Lohse, Detlef and Verzicco, Roberto and Fatica, Massimiliano and Stevens, Richard J.A.M.},
year={2017},
month={may},
archivePrefix={"arXiv"},
primaryClass={physics.flu-dyn}
}
The AFiD code, an open source solver for the incompressible Navier-Stokes equations ({color{blue}burl{this http URL}}), 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.
May 6, 2017 by hgpu
