Massive Parallelization of the WRF GCE Model Toward a GPU-Based End-to-End Satellite Data Simulator Unit

Abstract

Modern weather satellites provide more detailed observations of cloud and precipitation processes. To harness these observations for better satellite data assimilations, a cloud-resolving model, known as the Goddard Cumulus Ensemble (GCE) model, was developed and used by the Goddard Satellite Data Simulator Unit (G-SDSU). The GCE model has also been incorporated as part of the widely used weather research and forecasting (WRF) model. The computation of the cloud-resolving GCE model is time-consuming. This paper details our massively parallel design of GPU-based WRF GCE scheme. With one NVIDIA Tesla K40 GPU, the GPU-based GCE scheme achieves a speedup of 361× as compared to its original Fortran counterpart running on one CPU core, whereas the speedup for one CPU socket (four cores) with respect to one CPU core is only 3.9×.