Real-time pluvial flood forecasting model for mega cities based on a heterogeneous supercomputer

In recent years, China has frequently been affected by urban flooding especially in megacities with populations exceeding ten million, which are concentrated in the East Asian monsoon climate zone. Hydrodynamic models are effective tools for predicting flood disasters. However, large urban areas with hundreds of square kilometers result in a high computational burden for modeling. This study develops a high-resolution pluvial flood forecasting model based on a national supercomputing center. The model effectively leverages the heterogeneous architecture of supercomputer and can access precipitation forecast data from ECMWF to achieve real-time predictions for mega cities. Hydraulic modeling is based on the diffusion wave equation, discretized by finite difference method. The model divides the study area into several equal rectangular partition depending on preset spatial parameters. Structured grids are defined. Employing MPI (Message Passing Interface) as parallel tool, one CPU core and one DCU (Deep Computing Unit) are used for calculation of each partition. The model is validated using two rainfall and water depth datasets collected from Tsinghua Campus. Taking Chengdu, with an area of approximately 600 km² and a resolution of 1 m, as the study area, five partitioning schemes are set up to compare the computing time for CPU-only and CPU+DCU computation. Model performance is tested by simulating 3-hour surface runoff process with over 600 million grids. The results show that when over 6000 CPU cores and 6000 DCUs are used, the model can complete the simulation in 10 minutes. It represents a speedup of about 5 times compared to equal number of CPU cores computation without DCUs, and approximately 500 times faster than using 64 CPU cores. The model demonstrates near-linear speedup when using only CPUs, suggesting that it is approximately 30,000 times faster than single CPU core computation. By analyzing computational time of each process during model execution, hydrodynamic calculation is faster by tens of times on DCUs than on CPUs. Message passing and input/output time on CPUs will impact the scalability of the model.