Improving the performance of large-scale computation of near-bed orbital velocities under ideal wave conditions

Conceptual overview of the proposed hydroinformatic workflow. Geospatial raster inputs (bathymetry, wave height, and wave period) are processed using analytical wave theory and a parallelized computational framework. The workflow leverages CPU and GPU acceleration to compute near-bed orbital velocities across large raster domains, achieving significant speedup and reduced memory usage. This technical note introduces an efficient workflow for the large-scale computation of near-bed flow velocities under wave action, based on linear airy wave theory. As a case study, rasterized datasets of bathymetry, significant wave height, and wave period for the German Bight are used to estimate the maximum orbital velocity at the seabed. This is implemented within a modular hydroinformatic framework, combining geographic raster data (pre-)processing using pluggable models and interactive visualization. It employs just-in-time compilation via the python library Numba. This allows for execution across all cores of the Central Processing Unit (CPU) or, if present, a Graphics Processing Unit (GPU) using the Compute Unified Device Architecture (CUDA) architecture. This design facilitates large parallel computation across thousands of CUDA cores, enabling faster processing of extensive raster domains for iterative model refinement. Performance benchmarks reveal a more than fourfold reduction in runtime and 70% lower memory consumption compared to a standard CPU implementation. The findings underscore how classical hydrodynamic formulations can be effectively integrated into scalable, open, and reproducible workflows for rapid spatial evaluation of marine processes. The presented method offers a practical foundation for area-wide analyses of seabed dynamics, offshore engineering design, and morphodynamic sensitivity studies within modern hydroinformatics.