A data relocation approach for terrain surface analysis on multi-GPU systems: a case study on the total viewshed problem

References

• Atallah, M.J., 1983. Dynamic computational geometry. In: Foundations of Computer Science, 1983., 24th Annual Symposium on. Tucson, AZ, USA: IEEE, 92–99.
   Google Scholar
• Brughmans, T., van Garderen, M., and Gillings, M., 2018. Introducing visual neighbourhood configurations for total viewsheds. Journal of Archaeological Science, 96, 14–25. doi:10.1016/j.jas.2018.05.006
   Web of Science ®Google Scholar
• Cabral, B., Max, N., and Springmeyer, R., 1987. Bidirectional reflection functions from surface bump maps. In: ACM SIGGRAPH computer graphics. ACM Digital Library, vol. 21, 4, 273–281.
   Google Scholar
• Cauchi-Saunders, A.J. and Lewis, I.J., 2015. Gpu enabled xdraw viewshed analysis. Journal of Parallel and Distributed Computing, 84, 87–93. doi:10.1016/j.jpdc.2015.07.001
   Web of Science ®Google Scholar
• Cervilla, A., Tabik, S., and Romero, L., 2015. Siting multiple observers for maximum coverage: an accurate approach. In: Proceedings of the 2015 International Conference on Computational Science, ICCS2015. Reykjavík, Iceland.
   Google Scholar
• Choset, H.M., et al., 2005. Principles of robot motion: theory, algorithms, and implementation. Cambridge, Massachusetts: MIT press.
   Google Scholar
• Dou, W., Li, Y., and Wang, Y., 2018. A fine-granularity scheduling algorithm for parallel xdraw viewshed analysis. Earth Science Informatics, 11 (3), 433–447. doi:10.1007/s12145-018-0339-5
   Web of Science ®Google Scholar
• Dou, W., Li, Y., and Wang, Y., 2019. An equal-area triangulated partition method for parallel xdraw viewshed analysis. Concurrency and Computation: Practice and Experience, 31 (17), e5216. doi:10.1002/cpe.5216
   Web of Science ®Google Scholar
• Dungan, K.A., et al. 2018. A total viewshed approach to local visibility in the chaco world. antiquity, 92 (364), 905–921. doi:10.15184/aqy.2018.135
   Web of Science ®Google Scholar
• ESRI, 2010. Arcgis software. version 9.3. Redlands, CA: Environmental Systems Research Institute.
   Google Scholar
• Fisher, P.F., 1992. First experiments in viewshed uncertainty: simulating fuzzy viewsheds. Photogrammetric Engineering and Remote Sensing, 58, 345.
   Web of Science ®Google Scholar
• Floriani, L.D. and Magillo, P., 1994. Visibility algorithms on triangulated digital terrain models. International Journal of Geographical Information Systems, 8 (1), 13–41. doi:10.1080/02693799408901985
   Web of Science ®Google Scholar
• Franklin, W.R. and Ray, C., 1994. Geometric algorithms for siting of air defense missile batteries. In: Technical report DAAL03-86-D-0001. Columbus, OH: Battelle, Columbus Division, 199..
   Google Scholar
• Franklin, W.R., Ray, C.K., and Mehta, S., 1994. Geometric algorithms for siting of air defense missile batteries. Research Project for Battle, 2756.
   Google Scholar
• Gao, Y., et al., 2011. Optimization for viewshed analysis on gpu. In: 2011 19th International Conference on Geoinformatics. Shanghai, China: IEEE, 1–5.
   Google Scholar
• Kaučič, B. and Zalik, B., 2002. Comparison of viewshed algorithms on regular spaced points. In: Proceedings of the 18th spring conference on Computer graphics. New York, NY: ACM, 177–183.
   Google Scholar
• Li, J., Zheng, C., and Hu, X., 2010. An effective method for complete visual coverage path planning. In: 2010 Third International Joint Conference on Computational Science and Optimization. Huangshan (Yellow) Mountain, Anhui, China: IEEE, vol. 1, 497–500.
   Google Scholar
• Neteler, M., et al., 2012. GRASS GIS: a multi-purpose open source GIS. Environmental Modelling and Software, 31, 124–130. doi:10.1016/j.envsoft.2011.11.014
   Web of Science ®Google Scholar
• Osterman, A., Benedičič, L., and Ritoša, P., 2014. An io-efficient parallel implementation of an r2 viewshed algorithm for large terrain maps on a cuda gpu. International Journal of Geographical Information Science, 28 (11), 2304–2327. doi:10.1080/13658816.2014.918319
   Web of Science ®Google Scholar
• Qarah, F.F. and Tu, Y.C., 2019. A fast exact viewshed algorithm on gpu. In: 2019 IEEE International Conference on Big Data (Big Data). Los Angeles, CA, USA: IEEE, 3397–3405.
   Google Scholar
• Song, X.D., et al. 2016. Parallel viewshed analysis on a pc cluster system using triple-based irregular partition scheme. Earth Science Informatics, 9 (4), 511–523. doi:10.1007/s12145-016-0263-5
   Web of Science ®Google Scholar
• Stewart, A.J., 1998. Fast horizon computation at all points of a terrain with visibility and shading applications. Visualization and Computer Graphics, IEEE Transactions On, 4 (1), 82–93. doi:10.1109/2945.675656
   Web of Science ®Google Scholar
• Stojanović, N. and Stojanović, D., 2013. Performance improvement of viewshed analysis using gpu. In: 2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS). Nis, Serbia: IEEE, vol. 2, 397–400.
   Google Scholar
• Strnad, D., 2011. Parallel terrain visibility calculation on the graphics processing unit. Concurrency and Computation: Practice and Experience, 23 (18), 2452–2462. doi:10.1002/cpe.1808
   Web of Science ®Google Scholar
• Tabik, S., et al. 2014. Efficient data structure and highly scalable algorithm for total-viewshed computation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8 (1), 304–310. doi:10.1109/JSTARS.2014.2326252
   Web of Science ®Google Scholar
• Tabik, S., Zapata, E.L., and Romero, L.F., 2013. Simultaneous computation of total viewshed on large high resolution grids. International Journal of Geographical Information Science, 27 (4), 804–814. doi:10.1080/13658816.2012.677538
   Web of Science ®Google Scholar
• Wang, Y. and Dou, W., 2019. A fast candidate viewpoints filtering algorithm for multiple viewshed site planning. International Journal of Geographical Information Science, vol 34 (3), 1–16.
   Web of Science ®Google Scholar
• Zhao, Y., Padmanabhan, A., and Wang, S., 2013. A parallel computing approach to viewshed analysis of large terrain data using graphics processing units. International Journal of Geographical Information Science, 27 (2), 363–384. doi:10.1080/13658816.2012.692372
   Web of Science ®Google Scholar