Lightweight GPU-Accelerated Parallel Processing of the SCHISM Model Using CUDA Fortran

• DOI: 10.3390/jmse13040662
• Publication Date: 2025-03-27T15:01:00Z
• Authors: Hongchun Zhang; Qian Cao; Changmao Wu; Guangjun Xu; Yuli Liu; Xingru Feng; Meibing Jin; Changming Dong

The SCHISM model is widely used for ocean numerical simulations, but its computational efficiency is constrained by the substantial resources it requires. To enhance its performance, this study develops GPU–SCHISM, a GPU-accelerated parallel version of SCHISM using the CUDA Fortran framework, and this study evaluates its acceleration performance on a single GPU-enabled node. The research results demonstrate that the GPU–SCHISM model achieves computational acceleration while maintaining high simulation accuracy. For small-scale classical experiments, a single GPU improves the efficiency of the Jacobi solver—identified as a performance hotspot—by 3.06 times and accelerates the overall model by 1.18 times. However, increasing the number of GPUs reduces the computational workload per GPU, which hinders further acceleration improvements. The GPU is particularly effective for performing higher-resolution calculations, leveraging its computational power. For large-scale experiments with 2,560,000 grid points, the GPU speedup ratio is 35.13; however, CPU has more advantages in small-scale calculations. Moreover, a comparison between CUDA and OpenACC-based GPU acceleration shows that CUDA outperforms OpenACC under all experimental conditions. This study marks the first successful GPU acceleration of the SCHISM model within the CUDA Fortran framework, laying a preliminary foundation for lightweight GPU-accelerated parallel processing in ocean numerical simulations.