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

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Abstract

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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Acknowledgements

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.

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Authors and Affiliations

  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 Resources, China Institute of Water Resources and Hydropower Research, Beijing, 100038, People’s Republic of China

    Guangyuan Kan, Xiaoyan He, Liuqian Ding & Jiren Li

  2. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China

    Guangyuan Kan & Yang Hong

  3. Hydrometeorology and Remote Sensing (HyDROS) Laboratory, School Civil Engineering and Environmental Science, and Advanced Radar Research Center, University of Oklahoma, Norman, OK, 73072, USA

    Yang Hong

  4. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing IWHR Corporation, Beijing, 100048, People’s Republic of China

    Ke Liang

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  1. Guangyuan Kan
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Correspondence to Guangyuan Kan.

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Kan, G., He, X., Ding, L. et al. Heterogeneous parallel computing accelerated generalized likelihood uncertainty estimation (GLUE) method for fast hydrological model uncertainty analysis purpose. Engineering with Computers 36, 75–96 (2020). https://doi.org/10.1007/s00366-018-0685-4

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  • DOI: https://doi.org/10.1007/s00366-018-0685-4

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