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 Zuo, Guangyuan Kan, Hongquan Sun, Hongbin Zhang, and Ke Liang

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This preprint has been withdrawn.

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Depeng Zuo, Guangyuan Kan, Hongquan Sun, Hongbin Zhang, and Ke Liang

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, Guangyuan Kan, Hongquan Sun, Hongbin Zhang, and Ke Liang
Depeng Zuo, Guangyuan Kan, Hongquan Sun, Hongbin Zhang, and Ke Liang

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Latest update: 15 Jul 2024
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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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