使用分布式GPU网格化实现洪水淹没的高分辨率实时包围模拟和数据同化
Enable High-resolution, Real-time Ensemble Simulation and Data Assimilation of Flood Inundation using Distributed GPU Parallelization
摘要：
洪水事件的强度和演变的数值模拟受到多种不确定性来源的影响，如降水和地表条件。为了量化和抑制这些不确定性，基于集成的模拟和数据同化模型的洪泛淹没。浅水方程在x、y方向上解耦，圣维南方程选用惯性形式，实现快速计算。输入和输出因子的概率分布使用Monte Carlo样本来描述。随后，粒子滤波器被纳入，使水文观测同化，提高预测精度。为了实现高分辨率、实时的集成仿真，采用了基于统一计算设备架构（CUDA）的异构计算技术和分布式存储的多GPU（graphics processing unit）系统。为了保证仿真程序的并行效率和可扩展性，采用了多种优化技术。以福州市某城区为例，构建了空间分辨率为3 m、单元数为400万的模型，采用8个Tesla P100 GPU并行计算96个模型实例。在这些设置下，一个1小时的水力过程的集成模拟需要2.0分钟，这实现了2680的估计加速比相比，在CPU上运行的单线程。计算结果表明，粒子滤波方法在提供径流量、淹没面积、淹没水深等关键水文要素的置信区间的同时，有效地抑制了模拟的不确定性。所提出的方法在处理洪水模拟中的不确定性以及提高预测效率方面显示出很好的能力。
英文摘要：
Numerical modeling of the intensity and evolution of flood events are affected by multiple sources of uncertainty such as precipitation and land surface conditions. To quantify and curb these uncertainties, an ensemble-based simulation and data assimilation model for pluvial flood inundation is constructed. The shallow water equation is decoupled in the x and y directions, and the inertial form of the Saint-Venant equation is chosen to realize fast computation. The probability distribution of the input and output factors is described using Monte Carlo samples. Subsequently, a particle filter is incorporated to enable the assimilation of hydrological observations and improve prediction accuracy. To achieve high-resolution, real-time ensemble simulation, heterogeneous computing technologies based on CUDA (compute unified device architecture) and a distributed storage multi-GPU (graphics processing unit) system are used. Multiple optimization skills are employed to ensure the parallel efficiency and scalability of the simulation program. Taking an urban area of Fuzhou, China as an example, a model with a 3-m spatial resolution and 4.0 million units is constructed, and 8 Tesla P100 GPUs are used for the parallel calculation of 96 model instances. Under these settings, the ensemble simulation of a 1-hour hydraulic process takes 2.0 minutes, which achieves a 2680 estimated speedup compared with a single-thread run on CPU. The calculation results indicate that the particle filter method effectively constrains simulation uncertainty while providing the confidence intervals of key hydrological elements such as streamflow, submerged area, and submerged water depth. The presented approaches show promising capabilities in handling the uncertainties in flood modeling as well as enhancing prediction efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
