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02-01-2019 | Original Article

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

Authors: Guangyuan Kan, Xiaoyan He, Liuqian Ding, Jiren Li, Yang Hong, Ke Liang

Published in: Engineering with Computers | Issue 1/2020

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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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Title: Heterogeneous parallel computing accelerated generalized likelihood uncertainty estimation (GLUE) method for fast hydrological model uncertainty analysis purpose
Authors: Guangyuan Kan
Xiaoyan He
Liuqian Ding
Jiren Li
Yang Hong
Ke Liang
Publication date: 02-01-2019
Publisher: Springer London
Published in: Engineering with Computers / Issue 1/2020
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI: https://doi.org/10.1007/s00366-018-0685-4

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