GPU Acceleration for GRAPES Meteorological Model

There is heavy computation involved in Global / Regional Assimilation and Prediction System (GRAPES) which is a typical non-linear discrete system of the Earth´s atmosphere developed for numerical weather prediction. Researchers have recently paid a lot of attention to the parallel acceleration of the GRAPES model by low-cost, low-power, high-performance Graphics Processor Units (GPUs). We implement in this paper the acceleration for the GRAPES model in GPUs, which is however not as efficient as supposed. On this basis, we further propose strategies to optimize the performance, including the ease of data transmission time, reducing the amount of device memory loaded and used to store various objects, and avoiding the branches of thread control flows. One can find from the experiments that, parallel acceleration with GPUs helps to improve the performance of GRAPES in terms of measures like accuracy and timeliness.