Numerical prediction of coastal inundation can be complex due to the multiple physical processes involved and typically requires two-dimensional numerical model extents, particularly in areas with complex along-shore morphology. Such model domains often incur relatively high computational expense. Recent extreme inundation studies for Wellington, New Zealand, were executed using the numerical tool, XBeach. Here, the two-dimensional physical dynamics associated with multiple small embayments and both reef and sandy beach substrates, require large, high spatial resolution numerical model extents, informed by a multi-source elevation surface. XBGPU, is a translation of key XBeach features into code that permits GPU-based acceleration. The present study presents a comparison between XBeach and XBGPU for the same numerical model configuration and extents. Three model resolutions were employed, ranging from a typical desktop CPU based XBeach model resolution, to the highest resolution model that will require High Performance Computing (HPC) scale resources. Two CPU HPC facilities were used and five GPUs to investigate the scalability of both XBeach and XBGPU. The latter ranged from desktop grade units to GPUs associated with professional computing facilities. XBeach scalability is investigated by mean of the speed-up ratio, the time saving ratio and the computational efficiency. These are in reference to the computational speed of a model running on one CPU core. The XBGPU speed-up ratio is presented as a function of the slowest GPU. Direct comparisons between XBeach and XBGPU were achieved by using computational capacity as a metric. The results indicate that even a desktop grade GPU can compete with the computational efficiency of HPC-scale CPU facilities. Small model resolutions presented inefficient scaling on both high-performance CPUs and GPUs while the high-resolution model presented near linear scalability for the high-performance GPUs. Nevertheless, HPCs can be efficient to solve large computational problems if enough CPUs are employed.

由于涉及多个物理过程，沿海洪水的数值预测可能很复杂，并且通常需要二维数值模型范围，特别是在具有复杂沿岸形态的区域。这样的模型域通常会产生相对较高的计算费用。最近对新西兰惠灵顿的极端洪水研究是使用数值工具 XBeach 进行的。在这里，与多个小海湾以及珊瑚礁和沙滩基质相关的二维物理动力学需要大的、高空间分辨率的数值模型范围，并由多源高程表面提供信息。XBGPU 是将关键 XBeach 功能转换为允许基于 GPU 加速的代码。本研究对相同数值模型配置和范围的 XBeach 和 XBGPU 进行了比较。采用了三种模型分辨率，从基于典型桌面 CPU 的 XBeach 模型分辨率到需要高性能计算 (HPC) 规模资源的最高分辨率模型。使用了两个 CPU HPC 设施和五个 GPU 来研究 XBeach 和 XBGPU 的可扩展性。后者的范围从桌面级单元到与专业计算设施相关的 GPU。XBeach 的可扩展性是通过加速比、省时比和计算效率来研究的。这些是指在一个 CPU 内核上运行的模型的计算速度。XBGPU 加速比作为最慢 GPU 的函数呈现。XBeach 和 XBGPU 之间的直接比较是通过使用计算能力作为衡量标准来实现的。结果表明，即使是桌面级 GPU 也可以与 HPC 规模的 CPU 设施的计算效率相媲美。小模型分辨率在高性能 CPU 和 GPU 上表现出低效的缩放，而高分辨率模型在高性能 GPU 上表现出接近线性的可扩展性。然而，如果使用足够多的 CPU，HPC 可以有效地解决大型计算问题。小模型分辨率在高性能 CPU 和 GPU 上表现出低效的缩放，而高分辨率模型在高性能 GPU 上表现出接近线性的可扩展性。然而，如果使用足够多的 CPU，HPC 可以有效地解决大型计算问题。小模型分辨率在高性能 CPU 和 GPU 上表现出低效的缩放，而高分辨率模型在高性能 GPU 上表现出接近线性的可扩展性。然而，如果使用足够多的 CPU，HPC 可以有效地解决大型计算问题。