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.
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 imple- ment cellular automata （CA） for land use change simulation based on graphics processing units （GPUs）. This method can be ap- plied 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 tradi- tional CA by 30 times. Such improvement is crucial for land-use change simulations in provincial regions and countries. The out- puts of the simulation can be further used to provide information to other global change models.
基金
supported by the Key National Natural Science Foundation of China (40830532)
the National Natural Science Foundation of China (40901187 and 41171308)
