Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU
Accelerating the SCE-UA Global Optimization Method Based on Multi-Core CPU and Many-Core GPU
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基于多核CPU和众核GPU的加速SCE-UA全局优化方法
DOI：
10.1155/2016/8483728
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发表时间：
2016-04
影响因子：
2.9
通讯作者：
Youbin Hu
中科院分区：
文献类型：
--
作者：
Guangyuan Kan;Ke Liang;Jiren Li;Liuqian Ding;Xiaoyan He;Youbin Hu
著名的全局优化SCE-UA方法是一种有效的、鲁棒的方法，在环境模型参数率定领域得到了广泛的应用。然而，SCE-UA方法具有较高的计算负荷，这限制了SCE-UA方法在高维和复杂问题中的应用。近年来，计算机硬件，如多核CPU和众核GPU，有了很大的发展。这些更强大的新硬件及其软件生态系统为加速SCE-UA方法提供了机会。本文提出了两种并行SCE-UA方法，并分别在Intel多核CPU和NVIDIA众核GPU上通过OpenMP和CUDA Fortran实现。采用Griewank基准函数对串行和并行SCE-UA方法的性能进行了测试和比较。根据比较结果，给出了一些有益的建议，以指导如何正确使用并行SCE-UA方法。
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.
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DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
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DOI：
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期刊：
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影响因子：
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作者：
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影响因子：
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作者：
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DOI：
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发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
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作者：
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通讯作者：
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DOI：
10.1201/9780429422614-20
发表时间：
2018-10
期刊：
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影响因子：
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作者：
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通讯作者：
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