GPU-CA model for large-scale land-use change simulation

被引:20
作者
Li Dan [1 ]
Li Xia [1 ]
Liu XiaoPing [1 ]
Chen YiMin [1 ]
Li ShaoYing [1 ]
Liu Kai [2 ]
Qiao JiGang [3 ]
Zheng YiZhong [1 ]
Zhang YiHan [1 ]
Lao ChunHua [1 ]
机构
[1] Sun Yat Sen Univ, Guangdong Key Lab Urbanizat & Geosimulat, Sch Geog & Planning, Guangzhou 510275, Guangdong, Peoples R China
[2] Guangzhou Inst Geog, Guangzhou 510070, Guangdong, Peoples R China
[3] Guangdong Univ Business Studies, Resource & Environm Sch, Guangzhou 501320, Guangdong, Peoples R China
来源
CHINESE SCIENCE BULLETIN | 2012年 / 57卷 / 19期
基金
中国国家自然科学基金;
关键词
graphics processing unit (GPU); cellular automata (CA); land-use change simulation; large-scale; global change; CELLULAR-AUTOMATA;
D O I
10.1007/s11434-012-5085-3
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Land-use change simulation for large-scale regions (i.e. provincial regions or countries) is very useful for many global studies. Such simulation, however, is affected by computational capability of general computers. This paper proposes a method to implement cellular automata (CA) for land use change simulation based on graphics processing units (GPUs). This method can be applied to large-scale land-use change simulations by combining the latest GPU high-performance computing technology and CA. We carried out the experiments by simulating land-use change processes at a provincial scale. This involves a lot of sophisticated techniques, such as model mapping, and computational procedure of GPU-CA model. This proposed model has been validated by land-use change simulation in Guangdong Province, China. The comparison indicates that the GPU-CA model is faster than traditional CA by 30 times. Such improvement is crucial for land-use change simulations in provincial regions and countries. The outputs of the simulation can be further used to provide information to other global change models.
引用
收藏
页码:2442 / 2452
页数:11
相关论文
共 21 条
[1]  
[Anonymous], 1994, Environment and Planning B, DOI DOI 10.1068/B21S031
[2]  
[Anonymous], 2008, NVIDIA CUDA COMP UN
[3]   Molecular dynamics simulation of complex multiphase flow on a computer cluster with GPUs [J].
Chen FeiGuo ;
Ge Wei ;
Li JingHai .
SCIENCE IN CHINA SERIES B-CHEMISTRY, 2009, 52 (03) :372-380
[4]   A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area [J].
Clarke, KC ;
Hoppen, S ;
Gaydos, L .
ENVIRONMENT AND PLANNING B-PLANNING & DESIGN, 1997, 24 (02) :247-261
[5]   Octree-based, GPU implementation of a continuous cellular automaton for the simulation of complex, evolving surfaces [J].
Ferrando, N. ;
Gosalvez, M. A. ;
Cerda, J. ;
Gadea, R. ;
Sato, K. .
COMPUTER PHYSICS COMMUNICATIONS, 2011, 182 (03) :628-640
[6]   Retina simulation using cellular automata and GPU programming [J].
Gobron, Stephane ;
Devillard, Francois ;
Heit, Bernard .
MACHINE VISION AND APPLICATIONS, 2007, 18 (06) :331-342
[7]   Data mining of cellular automata's transition rules [J].
Li, X ;
Yeh, AGO .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2004, 18 (08) :723-744
[8]   Neural-network-based cellular automata for simulating multiple land use changes using GIS [J].
Li, X ;
Yeh, AG .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2002, 16 (04) :323-343
[9]  
Li X., 2007, GEOGRAPHICAL SIMULAT, V1st, P250
[10]  
Li Xia, 2010, Acta Scientiarum Naturalium Universitatis Sunyatseni, V49, P1