Next Article in Journal
A Formalized 3D Geovisualization Illustrated to Selectivity Purpose of Virtual 3D City Model
Previous Article in Journal
Evaluation of the Cartographical Quality of Urban Plans by Eye-Tracking
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(5), 193; https://doi.org/10.3390/ijgi7050193

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

1
School of Information Engineering, China University of Geosciences, Beijing 100083, China
2
Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China
*
Author to whom correspondence should be addressed.
Received: 22 March 2018 / Revised: 9 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
View Full-Text   |   Download PDF [2839 KB, uploaded 19 May 2018]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top