Heterogeneous parallel computing accelerated generalized likelihood uncertainty estimation (GLUE) method for fast hydrological model uncertainty analysis purpose

被引：0
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作者：
Guangyuan Kan
Xiaoyan He
Liuqian Ding
Jiren Li
Yang Hong
Ke Liang
机构：
[1] China Institute of Water Resources and Hydropower Research,State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood and Drought Disaster Reduction of the Ministry of Water Resources
[2] Tsinghua University,State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering
[3] University of Oklahoma,Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School Civil Engineering and Environmental Science, and Advanced Radar Research Center
[4] Beijing IWHR Corporation,State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research
来源：
Engineering with Computers | 2020年 / 36卷
关键词：
GLUE; Xinanjiang model; OpenMP; CUDA; GPU;
D O I：
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学科分类号：
摘要：
The generalized likelihood uncertainty estimation (GLUE) is a famous and widely used sensitivity and uncertainty analysis method. It provides a new way to solve the “equifinality” problem encountered in the hydrological model parameter estimation. In this research, we focused on the computational efficiency issue of the GLUE method. Inspired by the emerging heterogeneous parallel computing technology, we parallelized the GLUE in algorithmic level and then implemented the parallel GLUE algorithm on a multi-core CPU and many-core GPU hybrid heterogeneous hardware system. The parallel GLUE was implemented using OpenMP and CUDA software ecosystems for multi-core CPU and many-core GPU systems, respectively. Application of the parallel GLUE for the Xinanjiang hydrological model parameter sensitivity analysis proved its much better computational efficiency than the traditional serial computing technology, and the correctness was also verified. The heterogeneous parallel computing accelerated GLUE method has very good application prospects for theoretical analysis and real-world applications.
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页码：75 / 96
页数：21
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