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In the field of hydrological modelling, the global and automatic parameter 1 calibration has been a hot issue for many years. Among automatic parameter 2 optimization algorithms, the shuffled complex evolution developed at the University 3 of Arizona (SCE-UA) is the most successful method for stably and robustly locating 4 the global "best" parameter values. Ever since the invention of the SCE-UA, the 5 profession suddenly has a consistent way to calibrate watershed models. However, the 6 computational efficiency of the SCE-UA significantly deteriorates when coping with 7 big data and complex models. For the purpose of solving the efficiency problem, the 8 recently emerging heterogeneous parallel computing (parallel computing by using the 9 multi-core CPU and many-core GPU) was applied in the parallelization and 10 acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were 11 compared to test the performance based on the Griewank benchmark function. The 12 comparison results indicated that the parallel SCE-UA converged much faster than the 13 serial version and its optimization accuracy was the same as the serial version. It has a 14 promising application prospect in the field of fast hydrological model parameter 15 optimization. 16

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The famous global optimization SCE-UA method, which has been widely used in the field of environmental model parameter calibration, is an effective and robust method. However, the SCE-UA method has a high computational load which prohibits the application of SCE-UA to high dimensional and complex problems. In recent years, the hardware of computer, such as multi-core CPUs and many-core GPUs, improves significantly. These much more powerful new hardware and their software ecosystems provide an opportunity to accelerate the SCE-UA method. In this paper, we proposed two parallel SCE-UA methods and implemented them on Intel multi-core CPU and NVIDIA many-core GPU by OpenMP and CUDA Fortran, respectively. The Griewank benchmark function was adopted in this paper to test and compare the performances of the serial and parallel SCE-UA methods. According to the results of the comparison, some useful advises were given to direct how to properly use the parallel SCE-UA methods.