Parallelization of an optimization algorithm for beneficial watershed management practices

被引：0

作者：

Wu H. ^{[1
,2
]}

Liu Y. ^{[3
]}

Qin C. ^{[1
]}

Liu J. ^{[4
]}

Jiang J. ^{[1
,2
]}

Zhu A.-X. ^{[1
,5
]}

机构：

[1] Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing

[2] University of Chinese Academy of Sciences, Beijing

[3] Department of Geography, University of Guelph, Guelph, N1G 2W1, ON

[4] Nanjing Normal University, Nanjing

[5] Department of Geography, University of Wisconsin-Madison, Madison, 53706, WI

来源：

Zhu, A-Xing (axing@lreis.ac.cn) | 1600年 / Editorial Board of Medical Journal of Wuhan University卷 / 41期

关键词：

Beneficial management practices (BMPs); Cluster; MPI; Optimization algorithm; Parallel computation; Watershed model;

D O I：

10.13203/j.whugis20140048

中图分类号：

学科分类号：

摘要：

The optimization of beneficial management practices (or beneficial management practices, BMPs) is a typical case of complex geo-computation; a computation-intensive search for optimal solutions of watershed BMPs through many iterative watershed model simulations. This paper presents a parallelization of the epsilon non-dominated sorted genetic algorithm (ε-NSGA-II), an increasingly widely-used algorithm for BMPs optimization. The proposed parallel optimization algorithm was designed based on a master-slave parallelization strategy and implemented using the message passing interface (MPI). A case study executed on an IBM cluster for the Meichuan Jiang watershed (about 6366 km2) in the Lake Poyang basin shows that the proposed parallel BMPs optimization algorithm performs well. When the count of cores used in the case study increased (8~512 cores), the proposed parallel optimization algorithm delivered a higher speedup ratio. The speedup ratio reached 310 when 512 cores were used. In this case study, the parallel efficiency of the proposed parallel BMPs optimization algorithm decreased with an increase of the count of cores. The parallel efficiency ranged from 0.61 to 0.91, demonstrating that the proposed algorithm achieves good parallel performance. © 2016, Wuhan University. All right reserved.

引用

页码：202 / 207

页数：5

共 15 条

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