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

  • Guangyuan KanEmail author
  • Xiaoyan He
  • Liuqian Ding
  • Jiren Li
  • Yang Hong
  • Ke Liang
Original Article


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.


GLUE Xinanjiang model OpenMP CUDA GPU 



This research was funded by Beijing Natural Science Foundation (8184094), IWHR Research and Development Support Program (JZ0145B022018, JZ0145B022017), the Third Sub-Project: Flood Forecasting, Controlling and Flood Prevention Aided Software Development—Flood Control Early Warning Communication System and Flood Forecasting, Controlling and Flood Prevention Aided Software Development for Poyang Lake Area of Jiangxi Province (0628-136006104242, JZ0205A432013, SLXMB200902), and Construction project of Shaanxi province medium and small river hydrological monitoring and forecast system—construction of Guanzhong and north of Shaanxi flood forecast scheme (JZ0205A112015). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Guangyuan Kan
    • 1
    • 2
    Email author
  • Xiaoyan He
    • 1
  • Liuqian Ding
    • 1
  • Jiren Li
    • 1
  • Yang Hong
    • 2
    • 3
  • Ke Liang
    • 4
  1. 1.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 ResourcesChina Institute of Water Resources and Hydropower ResearchBeijingPeople’s Republic of China
  2. 2.State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic EngineeringTsinghua UniversityBeijingPeople’s Republic of China
  3. 3.Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School Civil Engineering and Environmental Science, and Advanced Radar Research CenterUniversity of OklahomaNormanUSA
  4. 4.State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower ResearchBeijing IWHR CorporationBeijingPeople’s Republic of China

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