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Massively Parallel Monte Carlo Sampling for Xinanjiang Hydrological Model Parameter Optimization Using CPU-GPU Computer Cluster
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DOI：10.3390/w15152810.png)
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The Monte Carlo sampling (MCS) method is a simple and practical way for hydrological model parameter optimization. The MCS procedure is used to generate a large number of data points. Therefore, its computational efficiency is a key issue when applied to large-scale problems. The MCS method is an internally concurrent algorithm that can be parallelized. It has the potential to execute on massively parallel hardware systems such as multi-node computer clusters equipped with multiple CPUs and GPUs, which are known as heterogeneous hardware systems. To take advantage of this, we parallelize the algorithm and implement it on a multi-node computer cluster that hosts multiple INTEL multi-core CPUs and NVIDIA many-core GPUs by using C++ programming language combined with the MPI, OpenMP, and CUDA parallel programming libraries. The parallel parameter optimization method is coupled with the Xinanjiang hydrological model to test the acceleration efficiency when tackling real-world applications that have a very high computational burden. Numerical experiments indicate, on the one hand, that the computational efficiency of the massively parallel parameter optimization method is significantly improved compared to single-core CPU code, and the multi-GPU code achieves the fastest speed. On the other hand, the scalability property of the proposed method is also satisfactory. In addition, the correctness of the proposed method is also tested using sensitivity and uncertainty analysis of the model parameters. Study results indicate good acceleration efficiency and reliable correctness of the proposed parallel optimization methods, which demonstrates excellent prospects in practical applications.
Keyword:
hydrological model
parameter optimization
Monte Carlo
MPI
OpenMP
CUDA
期刊
W
IF:
3
论文数:
3.2W
被引数:
7.4W
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