Parallelism and optimization of numerical ocean forecasting model

Abstract

According to the characteristics of Chinese marginal seas, the Marginal Sea Model of China (MSMC) has been developed independently in China. Because the model requires long simulation time, as a routine forecasting model, the parallelism of MSMC becomes necessary to be introduced to improve the performance of it. However, some methods used in MSMC, such as Successive Over Relaxation (SOR) algorithm, are not suitable for parallelism. In this paper, methods are developedto solve the parallel problem of the SOR algorithm following the steps as below. First, based on a 3D computing grid system, an automatic data partition method is implemented to dynamically divide the computing grid according to computing resources. Next, based on the characteristics of the numerical forecasting model, a parallel method is designed to solve the parallel problem of the SOR algorithm. Lastly, a communication optimization method is provided to avoid the cost of communication. In the communication optimization method, the non-blocking communication of Message Passing Interface (MPI) is used to implement the parallelism of MSMC with complex physical equations, and the process of communication is overlapped with the computations for improving the performance of parallel MSMC. The experiments show that the parallel MSMC runs 97.2 times faster than the serial MSMC, and root mean square error between the parallel MSMC and the serial MSMC is less than 0.01 for a 30-day simulation (172800 time steps), which meets the requirements of timeliness and accuracy for numerical ocean forecasting products.