Next Article in Journal
Parallel Landscape Driven Data Reduction & Spatial Interpolation Algorithm for Big LiDAR Data
Previous Article in Journal
A Fractal Perspective on Scale in Geography
Article Menu

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2016, 5(6), 96; doi:10.3390/ijgi5060096

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

1,2,†,* , 1,†
,
3,†,* and 4,†
1
School of Resources & Environment, University of Electronic Science and Technology of China, 2006 Xiyuan Avenue, West Hi-Tech Zone, Chengdu 611731, China
2
Institute of Remote Sensing Big Data, Big Data Research Center, University of Electronic Science and Technology of China, 2006 Xiyuan Road, West Hi-Tech Zone, Chengdu 611731, China
3
Center for Computation & Technology, Louisiana State University, 2039 Digital Media Center, Baton Rouge, LA 70803, USA
4
International School of Software, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 23 April 2016 / Revised: 24 May 2016 / Accepted: 6 June 2016 / Published: 17 June 2016
View Full-Text   |   Download PDF [5689 KB, uploaded 17 June 2016]   |  

Abstract

In some digital Earth engineering applications, spatial interpolation algorithms are required to process and analyze large amounts of data. Due to its powerful computing capacity, heterogeneous computing has been used in many applications for data processing in various fields. In this study, we explore the design and implementation of a parallel universal kriging spatial interpolation algorithm using the OpenCL programming model on heterogeneous computing platforms for massive Geo-spatial data processing. This study focuses primarily on transforming the hotspots in serial algorithms, i.e., the universal kriging interpolation function, into the corresponding kernel function in OpenCL. We also employ parallelization and optimization techniques in our implementation to improve the code performance. Finally, based on the results of experiments performed on two different high performance heterogeneous platforms, i.e., an NVIDIA graphics processing unit system and an Intel Xeon Phi system (MIC), we show that the parallel universal kriging algorithm can achieve the highest speedup of up to 40× with a single computing device and the highest speedup of up to 80× with multiple devices. View Full-Text
Keywords: heterogeneous computing; OpenCL; universal kriging algorithm; Graphics Processing Unit (GPU); Intel Xeon Phi heterogeneous computing; OpenCL; universal kriging algorithm; Graphics Processing Unit (GPU); Intel Xeon Phi
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Huang, F.; Bu, S.; Tao, J.; Tan, X. OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems. ISPRS Int. J. Geo-Inf. 2016, 5, 96.

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