A GPU-Accelerated Full 2D Shallow Water Model Using an Edge Loop Method on Unstructured Meshes: Implementation and Performance Analysis

Flood-induced disasters can cause significant harm and economic losses. Using numerical simulations to provide real-time predictions of flood events is an effective method to address this issue. To develop a high-efficiency and adaptable tool for fast flood prediction in complex terrains, this work utilizes Graphic Processing Units (GPUs) to accelerate a full 2D shallow water model on unstructured meshes. Furthermore, a novel Edge Loop Method (ELM) based on the winged-edge data structure is applied to the model to improve the computational efficiency of solving fluxes. A benchmark test and a real-world dam-break case were simulated to verify the accuracy and performance of the current model. The results demonstrate that the ELM accelerates the model by 2.51 and 4.08 times compared to the eight-core CPU-based model, and 14.97 and 19.84 times compared to the single-core CPU-based model in two cases. Notably, when compared to the GPU-based model using the Cell Loop Method (CLM), the computational efficiency of the ELM is improved by 18.34% and 24.29%, respectively. In particular, a quantitative analysis of the performance explains the advantage of the ELM from the perspective of its implementation mechanism, further demonstrating that the ELM exhibits higher computational efficiency as the total number of cells increases. Based on the advantages of high efficiency in the GPU-based model using the ELM, the proposed model can effectively forecast real-world flood events in regions characterized by complex terrains.