Preprints
https://doi.org/10.5194/nhess-2021-344
https://doi.org/10.5194/nhess-2021-344
 
17 Nov 2021
17 Nov 2021
Status: this preprint was under review for the journal NHESS. A final paper is not foreseen.

Improving computational efficiency of GLUE method for hydrological model uncertainty and parameter estimation using CPU-GPU hybrid high performance computer cluster

Depeng Zuo1, Guangyuan Kan1,2, Hongquan Sun3, Hongbin Zhang2, and Ke Liang2 Depeng Zuo et al.
  • 1Beijing Key Laborat ory of Urban Water Cycle and Sponge City Technology, College of Water Science, Beijing Normal University, Beijing 100875, P. R. China
  • 2State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing 100038, P. R. China
  • 3National Institute of Natural Hazards, Beijing 100085 P.R. China

Abstract. The Generalized Likelihood Uncertainty Estimation (GLUE) method has been thrived for decades, huge number of applications in the field of hydrological model have proved its effectiveness in uncertainty and parameter estimation. However, for many years, the poor computational efficiency of GLUE hampers its further applications. A feasible way to solve this problem is the integration of modern CPU-GPU hybrid high performance computer cluster technology to accelerate the traditional GLUE method. In this study, we developed a CPU-GPU hybrid computer cluster-based highly parallel large-scale GLUE method to improve its computational efficiency. The Intel Xeon multi-core CPU and NVIDIA Tesla many-core GPU were adopted in this study. The source code was developed by using the MPICH2, C++ with OpenMP 2.0, and CUDA 6.5. The parallel GLUE method was tested by a widely-used hydrological model (the Xinanjiang model) to conduct performance and scalability investigation. Comparison results indicated that the parallel GLUE method outperformed the traditional serial method and have good application prospect on super computer clusters such as the ORNL Summit and Sierra of the TOP500 super computers around the world.

This preprint has been withdrawn.

Depeng Zuo et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-344', Anonymous Referee #1, 01 Mar 2022
  • RC2: 'Comment on nhess-2021-344', Anonymous Referee #2, 24 Apr 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-344', Anonymous Referee #1, 01 Mar 2022
  • RC2: 'Comment on nhess-2021-344', Anonymous Referee #2, 24 Apr 2022

Depeng Zuo et al.

Depeng Zuo et al.

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Latest update: 06 Dec 2022
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This preprint has been withdrawn.

Short summary
The Generalized Likelihood Uncertainty Estimation (GLUE) method has been thrived for decades, huge number of applications in the field of hydrological model have proved its effectiveness in uncertainty and parameter estimation. In this study, we developed a CPU-GPU hybrid computer cluster-based highly parallel large-scale GLUE method to improve its computational efficiency.
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