Abstract: The weather research and forecasting model(WRF) is a widely used mesoscale numerical weather forecasting system that plays an important role in the fields of atmospheric research and meteorological operational forecasting.Stencil computation is a common nested loop pattern in scientific and engineering applications.WRF performs a large number of complex stencil computation on spatial grids to solve numerical equations of atmospheric dynamics and thermodynamics.The stencils in WRF are featured by multi-dimensionality,multi-variables,particularity of physical model boundaries,and complexity of physical and dynamic processes.This study analyzes the typical stencil pattern in WRF,identifies and abstracts the concept of “intermediate variable”,and implements three optimization schemes,namely,intermediate variable computation merging,intermediate variable dimensio-nality reduction storage,and intermediate variables extraction.The optimization schemes effectively improve the data locality,increase data reuse and spatial reuse rates,and reduces redundant computing and memory access overhead.The results show that the WRF 4.2 typical hotspot functions achieve significant performance improvements on both Intel CPU and Hygon CPU,with the highest speedup ratios of 21.3% and 17.8% respectively.

Key words: WRF, Stencil computation, Intermediate variable, Optimization scheme, Data locality, Hotspot function, Performance improvement