A GPU-accelerated smoothed particle hydrodynamics (SPH) model for the shallow water equations

Smoothed particle hydrodynamics (SPH) is a fully Lagrangian meshless computational method for solving the fluid dynamics equations. In recent years, it has also been employed to solve the shallow water equations (SWEs) and promising results have been obtained. However, SPH models are computationally very demanding and the SPH-SWE models considered in this work have no exception. In this paper, the Graphic Processing Units (GPUs) are explored to accelerate an SPH-SWE model for wider applications. Unlike Central Processing Units (CPUs), GPUs are highly parallelized, which makes it suitable for accelerating scientific computing algorithms like SPH. The aim is to design a GPU-based SPH model for solving the two-dimensional SWEs with variable smoothing lengths. Furthermore, a quad-tree neighbour searching method is implemented to further optimize the model performance. An idealized benchmark test and two real-world dam-break cases have been simulated to demonstrate the superior performance of the current GPU-accelerated high-performance SPH-SWE model.