Multi-GPU acceleration of high-resolution and large-scale urban flood modelling using MPI–OpenACC

High-resolution urban flood modelling is increasingly critical for disaster mitigation, but simulations remain computationally expensive, particularly when applying meter-scale grids over large spatial domains. Such computational constraints often restrict the practical use of high-resolution simulations in operational forecasting and scenario-based analyses. To address this challenge, this study investigates the use of multi-GPU acceleration to improve computational efficiency in large-scale urban flood simulations. We present a multi-GPU implementation of the H12 2D urban flood model based on an MPI–OpenACC framework. The H12 2D model is a physics-based two-dimensional urban flood model that supports CPU-based parallel execution and is extended here to GPU architectures. The proposed approach employs directive-based parallelization. This approach allows a single code base to be executed on both CPU and GPU systems without extensive code modification. Domain decomposition is managed using MPI, while computationally intensive kernels are offloaded to GPUs through OpenACC directives. This hybrid design ensures portability across heterogeneous high-performance computing environments and enables efficient use of multiple GPUs. We evaluate performance using spatial resolutions ranging from 1 to 20 m over two contrasting domains: an urban catchment in downtown Portland, Oregon (USA), and a downstream reach of the Han River basin (Republic of Korea). This study will discuss how computational performance varies with model resolution, domain size, and the distribution of computational workload across multiple GPUs, with a focus on scalability and parallel efficiency. The improved computational efficiency achieved in this study can support pseudo real-time urban flood prediction for early warning applications. In addition, the proposed framework facilitates large-scale, high-resolution simulations that can be used to generate ground-truth datasets for the development and validation of physics-informed or data-driven flood prediction models.