ACStack: Adaptive Composite Stack for Adjoint Models in Code Optimization

Adjoint model is one of the key technologies of numerical weather forecasting. In adjoint model, the observed information can be inputted into the model continually to improve the quality of the initial data and thus to improve the quality of forecast. However, adjoint models usually adopt the Calculate-All strategy for implementation, which is of low computational efficiency in complex numerical simulations. To improve the efficiency, we propose an Adaptive Storage Strategy in this paper. An Adaptive Composite Stack is also designed to manage the data flows. To further demonstrate the applicability of our method, the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) is chosen as the prediction model. Three cases of MM5 with different resolutions are studied and the results are presented. Experimental results show that the strategy can greatly improve the computing efficiency of adjoint models.

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