CASC2D分布式水文模型异构并行算法研究

Research of heterogeneous parallel algorithm for distributed hydrological model CASC2D

针对CASC2D模型精细化水文模拟时面临的计算耗时长、效率低等问题,在保持产汇流算法和流域拓扑结构的基础上,采用CPU+GPU的异构并行算法对CASC2D模型程序进行重新设计和优化,模型程序中的降雨、产流、坡面汇流和河道汇流过程均优化为并行计算,以提高CASC2D模型的计算效率。将优化后的CASC2D模型应用于前毛庄流域的洪水流量过程模拟,计算结果与原CASC2D模型保持一致。在栅格分辨率为30 m,计算步长为3 s时,与原CPU串行计算方法相比,并行加速比达到34倍以上,并且栅格单元数据精度越高,加速比提升越明显。异构并行算法可在不降低模拟精度的条件下显著提升CASC2D模型的计算效率,满足实时水文预报的应用需求。

Aiming at the problems of long computation time and low efficiency when CASC2D model is applied for refined hydrologic simulation,CASC2D model program is re-designed and optimized by CPU+GPU heterogeneous parallel algorithm without changing the algorithm of flow concentration and the basin topology.In order to improve the efficiency of model calculation,the model processes of rainfall,runoff generation,slope confluence and river confluence are optimized for parallel calculation.The optimized CASC2D model is applied to simulate the flood flow process in the Qianmaozhuang basin.The calculated results are consistent with the original CASC2D model.Compared with the traditional serial calculation method,the acceleration ratio of optimized model is more than 34times,given the grid resolution is 30 m and the calculation time step is 3 s.The higher the accuracy of raster cell data,the more distinct the acceleration ratio is improved.The heterogeneous parallel algorithm can significantly improve the calculation efficiency of CASC2D model without compromising the simulation accuracy,which meets practical application requirements of hydrological forecasting.