Parallelization Load Balance Strategy for a Global Grid-Point Model

The Global/Regional Assimilation and PrEdiction System (GRAPES) is a new-generation operational numerical weather prediction (NWP) model developed by the China Meteorological Administration (CMA).It is a grid-point model with a code structure different from that of spectral models used in other operational NWP centers such as the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and Japan Meteorological Agency (JMA), especially in the context of parallel computing. In the GRAPES global model, a semi-implicit semi-Lagrangian scheme is used for the discretization over a sphere, which requires careful planning for the busy communications between the arrays of processors, because the Lagrangian differential scheme results in shortened trajectories interpolated between the grid points at the poles and in the associated adjacent areas. This means that the latitude-longitude partitioning is more complex for the polar processors. Therefore, a parallel strategy with efficient computation, balanced load, and synchronous communication shall be developed. In this paper, a message passing approach based on MPI (Message Passing Interface) group communication is proposed.Its key-point is to group the polar processors in row with matrixtopology during the processor partitioning.A load balance task distribution algorithm is also discussed. Test runs on the IBM-cluster 1600 at CMA show that the new algorithm is of desired scalability, and the readjusted load balance scheme can reduce the absolute wall clock time by 10% or more. The quasi-operational runs of the model demonstrate that the wall clock time secured by the strategy meets the real-time needs of NWP operations.