A Weighted-Equal-Area Parallel Data Partitioning Algorithm for Global Numerical Weather Prediction Models

The computation on the polar regions plays an crucial role in the design of global numerical weather prediction (NWP) models, which details itself in the following two aspects: the particular treatment of polar regions in the models dynamic framework and the load-balancing problem caused by the parallel data partitioning strategies. The latter has become the bottleneck of massive parallelization of NWP models. To address this problem, a novel spherical data partitioning algorithm based on the weighted-equal-area approach is proposed. The weight describes the computational distribution across the entire sphere. The new algorithm takes the collar amount and the weight function as its parameters and performs the spherical partitioning as follows: the north and the south polar regions are partitioned into a singular subdomain; then the remaining sphere surface is partitioned into some collars along the latitude; and finally each collar is partitioned into subdomains along the longitude. This partitioning method can result in two polar caps plus a number of collars with increasing partition counts as we approach the equator. After a theoretical analysis of the quality relevant to the partition performed by the algorithm, we take the PSTSWM, which is a spectral shallow water model based on the spherical harmonic transform technique, as our test-bed to validate our method. The preliminary results indicate that the algorithm can result in good parallel load balance and a promising prospect can be expected for its application within the global atmospheric model of GRAPES.