Parallel computing performance of distributed hydrological model accelerated by GPU
- Article
- Figures
- Metrics
- Preview PDF
- Reference
- Related
- Cited by
- Materials
Abstract:
With the development of distributed hydrological models towards larger watersheds and finer granularity, computational efficiency gradually became a bottleneck, and parallel computing technology emerged as an effective solution to this challenge. In the realm of parallel computing for distributed hydrological models, most of the existing studies have primarily focused on CPU-based parallel techniques, with relatively limited research on GPU-based parallel methods. Furthermore, investigations on distributed hydrological models incorporating physical mechanisms remain scarce.This study centered around the physically-based distributed hydrological model WEP-L（water and energy transfer processes in large river basins）and explored the utilization of GPU-based parallel computing techniques. From a spatial perspective, the WEP-L model divides the watershed into numerous sub-basin units, where each unit's runoff calculations are independent, offering spatial parallelism. The interdependencies between simulation units were taken into account while allocating jobs to several computer units for parallel execution. Consequently, the runoff process of the model was parallelized based on sub-basins, dividing the Poyang Lake basin into 8,712 sub-units, and employing GPU threads to execute parallel computations through kernel functions.It is founded that the distributed hydrological model's suggested GPU-based parallel approach significantly accelerated the process. With an increase in GPU thread count, the parallel computing time steadily reduced. The parallel performance was most efficient when the total thread count closely approached the number of divided sub-basins. In the experimental Poyang Lake basin with 8,712 sub-basin units in the WEP-L model, the maximum speedup reached around 2.5. Secondly, the performance of GPU parallel computing was influenced not only by the degree of parallelism but also by the computational workload. With an increase in computational workload, both serial and parallel computation times increased. However, due to the smaller rate of increase in parallel computation time compared to the serial method, the speedup gradually increased, albeit at a diminishing rate. When the number of sub-basin units in the experimental WEP-L model increased to 24,897, the speedup ratio reached 3.5, indicating the considerable potential for GPU parallel algorithms in the computation of physically-based large-scale watershed distributed hydrological models.In conclusion, GPU-based parallel algorithms showed great promise for computing large-scale, physically-based, watershed-distributed hydrological models. The results indicated that the enhancement of parallel efficiency was contingent not only on the number of parallel threads activated but also on the size of the computational workload. The parallel calculation time decreased gradually as the number of GPU threads rose. As the computing demand rose, the speedup ratio increased correspondingly. GPU-based parallel computing represents the current trend in parallel computing. This study could provide valuable experience for other researchers exploring GPU parallel algorithms, contributing to the facilitation of interdisciplinary collaboration between computer science and water resources engineering.
Keywords:
Project Supported:
Clc Number:
Mobile website
