a b s t r a c tWe present a numerical scheme geared for high performance computation of wall-bounded turbulent flows. The number of all-to-all communications is decreased to only six instances by using a twodimensional (pencil) domain decomposition and utilizing the favourable scaling of the CFL time-step constraint as compared to the diffusive time-step constraint. As the CFL condition is more restrictive at high driving, implicit time integration of the viscous terms in the wall-parallel directions is no longer required. This avoids the communication of non-local information to a process for the computation of implicit derivatives in these directions. We explain in detail the numerical scheme used for the integration of the equations, and the underlying parallelization. The code is shown to have very good strong and weak scaling to at least 64 K cores.
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, 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-Bénard 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.
PROGRAM SUMMARYProgram Title: AFiD-GPU Licensing provisions(please choose one): GPLv3 Programming language: Fortan 90, CUDA Fortan, MPI External routines: PGI, CUDA Toolkit, FFTW3, HDF5 Nature of problem(approx. 50-250 words): Solving the three-dimensional Navier-Stokes equations coupled with a scalar field in a cubic box bounded between two walls and other four periodic boundaries. Solution method(approx. 50-250 words): Second order finite difference method for spatial discretization, third order Runge-Kutta scheme and Crank-Nicolson method for time advancement, two dimensional pencil distributed MPI parallelization, GPU accelerated routines. Additional comments including Restrictions and Unusual features (approx. 50-250 words): The open-source code is supported and updated on http://www.afid.eu.
In the Netherlands, for coastal and inland water applications, wave modelling with SWAN has become a main ingredient. However, computational times are relatively high. Therefore we investigated the parallel efficiency of the current MPI and OpenMP versions of SWAN. The MPI version is not that efficient as the OpenMP version within a single node. Therefore, in this paper we propose a hybrid version of SWAN. It combines the efficiency of the current OpenMP version on shared memory with the capability of the current MPI version to distribute memory over nodes. We describe the numerical algorithm. With initial numerical experiments we show the potential of this hybrid version. Parallel I/O, further optimization, and behavior for larger number of nodes will be subject of future research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.