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	A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data
	Wei H. T.; Du, Y. Y.; Liang, F. Y.; Zhou, C. H.; Liu, Z.; Yi, J. W.; Xu, K. H.; Wu, D.
	2015
关键词	k-d tree unevenly distributed data balanced workloads parallel computing binary search-trees quadtrees geography gpu
英文摘要	Parallel computing provides a promising solution to accelerate complicated spatial data processing, which has recently become increasingly computationally intense. Partitioning a big dataset into workload-balanced child data groups remains a challenge, particularly for unevenly distributed spatial data. This study proposed an algorithm based on the k-d tree method to tackle this challenge. The algorithm constructed trees based on the distribution variance of spatial data. The number of final sub-trees, unlike the conventional k-d tree method, is not always a power of two. Furthermore, the number of nodes on the left and right sub-trees is always no more than one to ensure a balanced workload. Experiments show that our algorithm is able to partition big datasets efficiently and evenly into equally sized child data groups. Speed-up ratios show that parallel interpolation can save up to 70% of the execution time of the consequential interpolation. A high efficiency of parallel computing was achieved when the datasets were divided into an optimal number of child data groups.
出处	Giscience & Remote Sensing
卷	52
期	1
页	40-57
收录类别	SCI
语种	英语
ISSN号	1548-1603
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/38501]
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	Wei H. T.,Du, Y. Y.,Liang, F. Y.,et al. A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data. 2015.

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