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

Abstract

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.

HydroCompute: An Open-Source Web-Based Computational Library for Hydrology and Environmental Sciences

“…Large-scale parallelization of hydrological models has been studied using standards such as OpenMP and Open ACC for graphical processing units (GPU) simulations in HPC systems to create sequential and parallel executions that can potentially optimize the runtime, speedup, efficiency, and balance of different hydrologic models (Freitas & Mendes, 2018). Moreover, uncertainty and parameter stipulation through CPU/GPU hybrid performance clusters has been done to create a generalized likelihood uncertainty estimation (GLUE) method with applications running on CUDA devices and multi-core computer clusters (Zuo et al, 2021). Applications leveraging GPU engines using different approaches in hydrological sciences have also been the focus of various studies, specifically with the release of low-level applications for C based code for flow routing algorithms (Rueda et al, 2016).…”

Precomputation of Critical State Soil Plastic Models

“…Even considering the development experienced in recent years by microelectronic technology, which has allowed the multi-core and many-core hybrid heterogeneous parallel computing platform to facilitate a very important advance in computing power, the efficiency of the calculation algorithm continues to be a key issue in the application of massive calculation processes. This is shown, for example, in [4] where the application of the Generalized Likelihood Uncertainty Estimation method in the probabilistic estimation of parameters in hydrology is analysed. In the field of soil mechanics, the improvement of computational performance is especially important in design or parameter identification processes, where the computational time can determine the viability of the study [5].…”