Flood inundation models are increasingly used for a wide variety of river and coastal management applications. Nevertheless, the computational effort to run these models remains a substantial constraint on their application. In this study four developments to the LISFLOOD-FP 2D flood inundation model have been documented that: 1) refine the parallelisation of the model; 2) reduce the computational burden of dry cells; 3) reduce the data movements between CPU and RAM; and 4) vectorise the core numerical solver. The value of each of these developments in terms of compute time and parallel efficiency was tested on 12 test cases. For realistic test cases, improvements in single core performance of between 4.2x and 8.4x were achieved, which when combined with parallelisation on 16 cores resulted in computation times 34-60x shorter than previous LISFLOOD-FP models on one core. Results were compared to a sample of commercial models for context.

洪水淹没模型越来越多地用于各种河流和沿海管理应用中。尽管如此，运行这些模型的计算量仍然对它们的应用构成了实质性的限制。在这项研究中，LISFLOOD-FP 2D洪水淹没模型的四个发展已被记录在案：1）改进模型的并行化；2）减轻干细胞的计算负担；3）减少CPU和RAM之间的数据移动；4）对核心数值求解器进行矢量化处理。在12个测试案例上测试了每种开发在计算时间和并行效率方面的价值。对于实际的测试案例，单核性能提高了4.2倍至8.4倍，与16个内核上的并行化结合使用时，其计算时间比以前在一个内核上的LISFLOOD-FP模型缩短了34-60倍。将结果与用于背景的商业模型样本进行比较。