A Real-time Flood Inundation Prediction on SX-Aurora TSUBASA

Due to extreme weather, record-breaking heavy rainfalls frequently cause severe flood damages. Thus, there is a strong demand for predicting flood scales to mitigate damages. In this paper, we propose a real-time flood inundation prediction system on a shared HPC system. Although the Rainfall-Runoff Inundation (RRI) model has been developed for predicting large-scale flood inundation, it is necessary to improve the performance for real-time prediction. Since the RRI model is highly memory-bound, we port the RRI simulation code to the latest vector computing system, SX-Aurora TSUBASA (SX-AT), which provides high sustained memory bandwidth. We discuss performance optimization of the RRI code at the node level and MPI parallelization strategies. The RRI code also needs to output intermediate results at a high frequency. Thus, the RRI code is split into file I/O operation and kernel computation, which are assigned to different kinds of processors using the heterogeneity of SX-AT. Furthermore, we discuss a resource demand estimation method to minimize the amount of shared computing resources used for prediction in order to reduce the impact on other users sharing the system. In our evaluation, we demonstrate that SX-AT with only 32 cores can meet the real-time simulation requirement of simulating 7-hour flood inundation for the Tohoku region of Japan within 20 minutes. The evaluation results also demonstrate that the proposed method can adaptively adjust the computing resource amount used for the real-time simulation, and thus reduce the computing resource by 75% in comparison with the worst-case scenario of conservative static resource allocation.