QQ客服 官方微博 反馈留言

	Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley's K function
	Zhang G. M.; Huang, Q. Y.; Zhu, A. X.; Keel, J. H.
	2016
关键词	Point pattern analysis Ripley's K function optimization MPI/OpenMP geospatial cloud computing cyberinfrastructure
英文摘要	Performing point pattern analysis using Ripley's K function on point events of large size is computationally intensive as it involves massive point-wise comparisons, time-consuming edge effect correction weights calculation, and a large number of simulations. This article presented two strategies to optimize the algorithm for point pattern analysis using Ripley's K function and utilized cloud computing to further accelerate the optimized algorithm. The first optimization sorted the points on their x and y coordinates and thus narrowed the scope of searching for neighboring points down to a rectangular area around each point in estimating K function. Using the actual study area in computing edge effect correction weights is essential to estimate an unbiased K function, but is very computationally intensive if the study area is of complex shape. The second optimization reused the previously computed weights to avoid repeating expensive weights calculation. The optimized algorithm was then parallelized using Open Multi-Processing (OpenMP) and hybrid Message Passing Interface (MPI)/OpenMP on the cloud computing platform. Performance testing showed that the optimizations effectively accelerated point pattern analysis using K function by a factor of 8 using both the sequential version and the OpenMP-parallel version of the optimized algorithm. While the OpenMP-based parallelization achieved good scalability with respect to the number of CPU cores utilized and the problem size, the hybrid MPI/OpenMP-based parallelization significantly shortened the time for estimating K function and performing simulations by utilizing computing resources on multiple computing nodes. Computational challenge imposed by point pattern analysis tasks on point events of large size involving a large number of simulations can be addressed by utilizing elastic, distributed cloud resources.
出处	International Journal of Geographical Information Science
卷	30
期	11
页	2230-2252
语种	英语
ISSN号	1365-8816
DOI标识	10.1080/13658816.2016.1170836
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/42834]
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	Zhang G. M.,Huang, Q. Y.,Zhu, A. X.,et al. Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley's K function. 2016.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。