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Implementation of a Parallel GPU-Based Space-Time Kriging Framework

School of Information Engineering, China University of Geosciences, Beijing 100083, China
Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(5), 193;
Received: 22 March 2018 / Revised: 9 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
In the study of spatiotemporal geographical phenomena, the space–time interpolation method is widely applied, and the demands for computing speed and accuracy are increasing. For nonprofessional modelers, utilizing the space–time interpolation method quickly is a challenge. To solve this problem, the classical ordinary kriging algorithm was selected and expanded to a spatiotemporal kriging algorithm. Using the OpenCL framework to integrate central processing unit (CPU) and graphic processing unit (GPU) computing resources, a parallel spatiotemporal kriging algorithm was implemented, and three experiments were conducted in this work to verify the results. The results indicated the following: (1) when the size of the prediction point dataset is consistent, the performance of the method is robust with the increasing size of the observation point dataset; (2) the acceleration effect of the parallel method increases with an increased number of predicted points. Compared with the original sequential program, the implementation of the improved parallel framework showed a 3.23 speedup, which obviously shortens the interpolation time; (3) when cross-validating the temperature data in the Beijing Tianjin Hebei region, the space–time acceleration model provides a better fit than traditional pure space interpolation. View Full-Text
Keywords: spatiotemporal kriging; OpenCL; graphics processing unit; ordinary kriging spatiotemporal kriging; OpenCL; graphics processing unit; ordinary kriging
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Zhang, Y.; Zheng, X.; Wang, Z.; Ai, G.; Huang, Q. Implementation of a Parallel GPU-Based Space-Time Kriging Framework. ISPRS Int. J. Geo-Inf. 2018, 7, 193.

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