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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Feb 2018
Next issue: May 2018
Avg review time: 107 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

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  4/2014 - 17
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High Performance Processing and Analysis of Geospatial Data Using CUDA on GPU

STOJANOVIC, N. See more information about STOJANOVIC, N. on SCOPUS See more information about STOJANOVIC, N. on IEEExplore See more information about STOJANOVIC, N. on Web of Science, STOJANOVIC, D. See more information about STOJANOVIC, D. on SCOPUS See more information about STOJANOVIC, D. on SCOPUS See more information about STOJANOVIC, D. on Web of Science
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (810 KB) | Citation | Downloads: 350 | Views: 1,494

Author keywords
high performance computing, geographic information systems, multiprocessing systems, parallel programming, performance analysis

References keywords
parallel(6), data(6), graphics(5), cuda(5), analysis(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 109 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04017
Web of Science Accession Number: 000348772500017
SCOPUS ID: 84921662038

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In this paper, the high-performance processing of massive geospatial data on many-core GPU (Graphic Processing Unit) is presented. We use CUDA (Compute Unified Device Architecture) programming framework to implement parallel processing of common Geographic Information Systems (GIS) algorithms, such as viewshed analysis and map-matching. Experimental evaluation indicates the improvement in performance with respect to CPU-based solutions and shows feasibility of using GPU and CUDA for parallel implementation of GIS algorithms over large-scale geospatial datasets.

References | Cited By  «-- Click to see who has cited this paper

[1] S. Patel, W. W. Hwu, "Accelerator Architectures," IEEE Micro, vol. 28, no. 4, pp. 4-12, 2008.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 23]

[2] D. Kirk, W. M. Hwu, "Programming Massively Parallel Processors: A Hands-on Approach," Elsevier, 2010.

[3] B. He, K. Yang, R. Fang, M. Lu, N. K. Govindaraju, Q. Luo, P. Sander, "Relational joins on graphics processors," Proceedings of the ACM SIGMOD Int. Conf.on Management of data, 2008, pp. 511-524.

[4] W. Fang, M. Lu, X. Xiao, B. He, Q. Luo, "Frequent Itemset Mining on Graphics Processors," Proceedings of the 5th International Workshop on Data Management on New Hardware, 2009, pp. 34-42.

[5] P. Bakkum, K. Skadron, "Accelerating SQL database operations on a GPU with CUDA," Proceedings of the 3rd Workshop on General-Purpose Computation on GPUs, 2010, pp. 94-103.

[6] B. Oh, "A Parallel Access Method for Spatial Data Using GPU," International Journal on Computer Science and Engineering, vol. 4 no. 03, pp. 492-500, 2012.

[7] J. Zhang, "Towards Personal High-Performance Geospatial Computing (HPC-G): Perspectives and a Case Study," ACM SIGSPATIAL - HPDGIS 2010 workshop, pp. 3-10, 2010.

[8] D. van der Merwe, J. Meyer, "Towards Automatic Digital Surface Model Generation Using a Graphics Processing Unit," Proceedings of the AFRICON, 2009, pp. 1-6.

[9] A. Beutel, T. Molhave, P. K. Agarwal, "Natural neighbour interpolation based grid DEM construction using a GPU," Proceeding of the 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2010, pp. 172-181.

[10] Y. Xia, Y. Li, X. Shi, "Parallel viewshed analysis on GPU using CUDA," Proceedings of the 3rd International Joint Conference on Computational Science and Optimization, vol. 01, pp. 373-374, 2010

[11] Y. Xia, L. Kuang, X. Li, "Accelerating geospatial analysis on GPUs using CUDA," Journal of Zhejiang University - Science C 12(12), pp. 990-999, 2011.

[12] D. Strnad, "Parallel terrain visibility calculation on the graphics processing unit," Concurrency and Computation: Practice & Experience, vol.23, no.18, pp. 2452-2462, 2011.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 10]

[13] C. Fang, C. Yang, Z. Chen, X. Yao, H. Guo, "Parallel algorithm for viewshed analysis on a modern GPU," International Journal of Digital Earth, pp.471-486, 2011.

[14] G. B. Vitor , A. Körbes, R. Lotufo, J. Ferreira, "Analysis of a Step-Based Watershed Algorithm Using CUDA", International Journal of Natural Computing Research, vol 1, no. 4, pp. 16-28, 2010.

[15] J. Kolomazník, J. Horácek, V. Krajícek, J. Pelikán, "Implementing Interactive 3D Segmentation on CUDA Using Graph-Cuts and Watershed Transformation," Proceedings of the 20th International Conference on Computer Graphics, Visualization and Computer Vision, pp. 35-38, 2012.

[16] J. Zhang, S. You, "CudaGIS: report on the design and realization of a massive data parallel GIS on GPUs," Proceedings of the 3rd ACM SIGSPATIAL Int. Workshop on GeoStreaming, pp.101-108, 2012.

[17] A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, J. Saltz, "Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce," Proceedings of VLDB, vol.6, no.11, pp.1009-1020, 2013.
[CrossRef] [SCOPUS Times Cited 202]

[18] A. Eldawy, M.Mokbel, "A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data," Proceedings of VLDB, vol. 6, no. 12, pp.1230-1233, 2013.
[CrossRef] [SCOPUS Times Cited 100]

[19] I. Heywood, S. Cornelius S. Carver, "An Introduction to Geographical Information Systems," 4th Edition, Prentice Hall, 2012.

[20] T. Brinkhof, "A framework for generating network-based moving objects," GeoInformatica, vol. 6, no. 2, pp. 153-180, 2002.
[CrossRef] [Web of Science Times Cited 366] [SCOPUS Times Cited 577]

References Weight

Web of Science® Citations for all references: 388 TCR
SCOPUS® Citations for all references: 912 TCR

Web of Science® Average Citations per reference: 18 ACR
SCOPUS® Average Citations per reference: 43 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-03-19 19:20 in 48 seconds.

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Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

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