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Article

A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data

1
School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222005, China
2
Research Center of Government GIS, Chinese Academy of Surveying and Mapping, Beijing 100039, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 653; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110653
Received: 3 September 2020 / Revised: 23 October 2020 / Accepted: 26 October 2020 / Published: 30 October 2020
Geographically weighted regression (GWR) introduces the distance weighted kernel function to examine the non-stationarity of geographical phenomena and improve the performance of global regression. However, GWR calibration becomes critical when using a serial computing mode to process large volumes of data. To address this problem, an improved approach based on the compute unified device architecture (CUDA) parallel architecture fast-parallel-GWR (FPGWR) is proposed in this paper to efficiently handle the computational demands of performing GWR over millions of data points. FPGWR is capable of decomposing the serial process into parallel atomic modules and optimizing the memory usage. To verify the computing capability of FPGWR, we designed simulation datasets and performed corresponding testing experiments. We also compared the performance of FPGWR and other GWR software packages using open datasets. The results show that the runtime of FPGWR is negatively correlated with the CUDA core number, and the calculation efficiency of FPGWR achieves a rate of thousands or even tens of thousands times faster than the traditional GWR algorithms. FPGWR provides an effective tool for exploring spatial heterogeneity for large-scale geographic data (geodata). View Full-Text
Keywords: CUDA; GWR; parallel computation; large-scale geodata CUDA; GWR; parallel computation; large-scale geodata
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MDPI and ACS Style

Wang, D.; Yang, Y.; Qiu, A.; Kang, X.; Han, J.; Chai, Z. A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data. ISPRS Int. J. Geo-Inf. 2020, 9, 653. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110653

AMA Style

Wang D, Yang Y, Qiu A, Kang X, Han J, Chai Z. A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data. ISPRS International Journal of Geo-Information. 2020; 9(11):653. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110653

Chicago/Turabian Style

Wang, Dongchao, Yi Yang, Agen Qiu, Xiaochen Kang, Jiakuan Han, and Zhengyuan Chai. 2020. "A CUDA-Based Parallel Geographically Weighted Regression for Large-Scale Geographic Data" ISPRS International Journal of Geo-Information 9, no. 11: 653. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9110653

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