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	Parallelization and optimization of spatial analysis for large scale environmental model data assembly
	Yu Q.
	2012
关键词	Climate data High-performance computing Zonal statistics Array-based algorithm GIS climate-change simulation systems carbon
英文摘要	Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resources requires high resolution input data. Assembling and summarizing this data in the appropriate format for model input often requires a series of spatial analyses which can be extremely time-consuming, especially when many large data sets are involved. In this paper we investigated the ability of high-performance computing techniques to improve the efficiency of spatial analysis for model data assembly. We implemented an array-based algorithm to calculate summary statistics for long time-series daily grid climate data sets for 11,575 climate-soil zones across the Australian wheat-growing regions for input into a crop simulation model. We developed a zonal statistics algorithm using Python's Numpy module then parallelized it and processed it using a shared memory, multi-processor system. We assessed algorithm performance with a varying number of CPU cores, and assessed the influence of load balancing on the efficiency of parallel processing. Compared with traditional desktop GIS software, the serial and parallel (32 cores) implementation achieved about 180 and 1440 times speed-up, respectively. We also found that the most efficient computation occurred when not all of the available CPU cores were used, and the chunk size of jobs also had an important influence on computing efficiency. The algorithm and the parallel processing scheme provides a useful approach to address computing challenges posed by spatial analysis of numerous large data sets for large scale environmental modelling. (C) 2012 Elsevier B.V. All rights reserved.
出处	Computers and Electronics in Agriculture
卷	89
页	94-99
收录类别	SCI
语种	英语
ISSN号	0168-1699
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/30679]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Yu Q.. Parallelization and optimization of spatial analysis for large scale environmental model data assembly. 2012.

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