A GPU-Accelerated and LTS-Based Finite Volume Shallow Water Model
Peng Hu, Zixiong Zhao, Aofei Ji, Wei Li, Zhiguo He, Qifeng Liu, Youwei Li, Zhixian Cao
Abstract
This paper presents a GPU (Graphics Processing Unit)-accelerated and LTS (Local-time-Step)-based finite volume Shallow Water Model (SWM). The model performance is compared against the other five model versions (Single CPU versions with/without LTS, Multi-CPU versions with/without LTS, and a GPU version) by simulating three flow scenarios: an idealized dam-break flow; an experimental dam-break flow; a field-scale scenario of tidal flows. Satisfactory agreements between simulation results and the available measured data/reference solutions (water level, flow velocity) indicate that all the six SWM versions can well simulate these challenging shallow water flows. Inter-comparisons of the computational efficiency of the six SWM versions indicate the following. First, GPU acceleration is much more efficient than multi-core CPU parallel computing. Specifically, the speed increase in the GPU can be as high as a hundred, whereas those for multi-core CPU are only 2–3. Second, implementing the LTS can bring considerable reduction: the additional maximum speed-ups can be as high as 10 for the single-core CPU/multi-core CPU versions, and as high as five for the GPU versions. Third, the GPU + LTS version is computationally the most efficient in most cases; the multi-core CPU + LTS version may run as fast as a GPU version for scenarios over some intermediate number of cells.