69
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A parallel strategy to accelerate neighborhood operation for raster data coordinating CPU and GPU

ORCID Icon, ORCID Icon & ORCID Icon
Received 15 Feb 2023, Accepted 16 Oct 2023, Published online: 07 Nov 2023

References

  • Aji, A., Wang, F. S., Vo, H., Lee, R., Liu, Q. L., Zhang, X. D., & Saltz, J. (2013). Hadoop-GIS: A high-performance spatial data warehousing system over MapReduce. In Proceedings of the VLDB endowment (pp. 1009–1020). https://doi.org/10.14778/2536222.2536227.
  • Becker, A., Zheng, G., & Kalé, L. V. (2011). Load balancing, distributed memory. In Encyclopedia of parallel computing (pp. 1043–1051). Springer. https://doi.org/10.1007/978-0-387-09766-4_504
  • Bohn, C. A., & Lamont, G. B. (2002). Load balancing for heterogeneous clusters of PCs. Future Generation Computer Systems, 18(3), 389–400. https://doi.org/10.1016/S0167-739X(01)00058-9
  • Chang, K. T. (2008). Introduction to geographic information systems. McGraw-Hill Education.
  • Chen, Y. L., Li, S. M., & Liu, D. Y. (2009). Analysis of slope and aspect based on regular grid DEM. Geomatics & Spatial Information Technology, 32(5), 36–39. https://doi.org/10.3969/j.issn.1672-5867.2009.05.010
  • Cramer, B. E., & Armstrong, M. P. (2010). An evaluation of domain decomposition strategies for parallel spatial interpolation of surfaces. Geographical Analysis, 31(1), 148–168. https://doi.org/10.1111/j.1538-4632.1999.tb00974.x
  • Do, H. T., Limet, S., & Melin, E. (2011). Parallel computing flow accumulation in large digital elevation models. Procedia Computer Science, 4, 2277–2286. https://doi.org/10.1016/j.procs.2011.04.248
  • Dong, Y. Z., Liu, Y. X., Hu, C. M., Macdonald, I. R., & Lu, Y. C. (2022). Chronic oiling in global oceans. Science, 376, 1300–1304. https://doi.org/10.1126/science.abm5940
  • Gong, J. Y., & Xie, J. B. (2009). Extraction of drainage networks from large terrain datasets using high throughput computing. Computers & Geosciences, 35(2), 337–346. https://doi.org/10.1016/j.cageo.2008.09.002
  • Guo, H. L., Xu, B. W., Yang, H., Li, B. Y., Yue, Y. Y., & Zhao, S. (2022). CUDA-based parallelization of time-weighted dynamic time warping algorithm for time series analysis of remote sensing data. Computers & Geosciences, 164, 105122. https://doi.org/10.1016/j.cageo.2022.105122
  • Hadian, A., & Shahrivari, S. (2014). High performance parallel $k$ k -means clustering for disk-resident datasets on multi-core CPUs. The Journal of Supercomputing, 69(2), 845–863. https://doi.org/10.1007/s11227-014-1185-y
  • Helming, K., Roth, C. H., Wolf, R., & Diestel, H. (1993). Characterization of rainfall-microrelief interactions with runoff using parameters derived from digital elevation models (DEMs). Soil Technology, 6(3), 273–286. https://doi.org/10.1016/0933-3630(93)90016-8
  • Huang, T. S. (1972). Stability of two-dimensional recursive filters. IEEE Transactions on Audio and Electroacoustics, 20(2), 158–163. https://doi.org/10.1109/TAU.1972.1162364
  • Huang, Y. Z., Yin, Y. L., Liu, Y., He, S. B., Bai, Y., & Li, R. F. (2021). A novel multi-CPU/GPU collaborative computing framework for SGD-based matrix factorization. In Proceedings of the 50th international conference on parallel processing (pp. 1–12). https://doi.org/10.1145/3472456.3472520.
  • Hu, C., Zhang, F., Ma, L., Li, G., Hu, W., & Li, W. (2015). Efficient SAR raw data parallel simulation based on multicore vector extension. In Proceedings of the IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 4719–4722). https://doi.org/10.1109/IGARSS.2015.7326883.
  • Knuth, D. E. (1998). Art of computer programming (Vol. 3). Sorting and Searching. Addison-Wesley Professional.
  • Liang, L., Zhang, Q., Song, P. T., Zhang, Z. J., Zhao, Q., Wu, H. C., & Cao, L. Z. (2020). Overlapping communication and computation of GPU/CPU heterogeneous parallel spatial domain decomposition MOC method. Annals of Nuclear Energy, 135(10), 69–88. https://doi.org/10.1016/j.anucene.2019.106988
  • Liu, Y. X., Hu, C. M., Zhan, W. F., Sun, C., Murch, B., & Ma, L. (2018). Identifying industrial heat sources using time-series of the VIIRS nightfire product with an object-oriented approach. Remote Sensing of Environment, 204, 347–365. https://doi.org/10.1016/j.rse.2017.10.019
  • Liu, Y. X., Zhou, M., Zhao, S., Zhan, W., Yang, K., & Li, M. (2015). Automated extraction of tidal creeks from airborne laser altimetry data. Canadian Journal of Fisheries and Aquatic Sciences, 527, 1006–1020. https://doi.org/10.1016/j.jhydrol.2015.05.058
  • Li, G., Zhang, F., Ma, L., Hu, W., & Li, W. (2015) Accelerating SAR imaging using vector extension on multi-core SIMD CPU. In IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 537–540). https://doi.org/10.1109/IGARSS.2015.7325819.
  • Lu, F. S., Song, J. Q., Yin, F. K., & Zhang, L. L. (2011). Survey of CPU/GPU synergetic parallel computing. 38(3), 5–9. https://doi.org/10.3969/j.issn.1002-137X.2011.03.002
  • Lu, M., Wang, J. Y., Lu, G., Tao, W. D., & Wang, J. C. (2017). Research of raster data spatial analysis under CPU/GPU heterogeneous hybrid parallel environment: Take terrain factors analysis as an example. Computer Engineering and Application, 53(1), 172–177. https://doi.org/10.3778/j.issn.1002-8331.1503-0292
  • Lv, M. H., Wei, X., & Lei, C. (2012). A GPU-based parallel processing method for slope analysis in geographic computation. 2nd international conference on advanced engineering materials and technology (pp. 625–631). https://doi.org/10.4028/www.scientific.net/AMR.538-541.625.
  • McDonnell, R. A., Lloyd, C., & Burrough, P. (1998). Principles of geographical information systems. Oxford University Press.
  • Mineter, M. J., & Dowers, S. (1999). Parallel processing for geographical applications: A layered approach. Journal of Geographical Systems, 1(1), 61–74. https://doi.org/10.1007/s101090050005
  • Planchon, O., & Darboux, F. (2002). A fast, simple, and versatile algorithm to fill the depressions of digital elevation models. Catena, 46(2), 159–176. https://doi.org/10.1016/S0341-8162(01)00164-3
  • Rokos, D. K., & Armstrong, M. P. (1992). Parallel terrain feature extraction. In Proceedings of GIS/LIS (pp. 652–661). San Jose Convention Center, San Jose, CA.
  • Sanchez-Fernandez, A. J., Romero, L. F., Bandera, G., & Tabik, S. (2021). A data relocation approach for terrain surface analysis on multi-GPU systems: A case study on the total viewshed problem. International Journal of Geographical Information Science, 35(8), 1500–1520. https://doi.org/10.1080/13658816.2020.1844207
  • Sanders, J., & Kandrot, E. (2011). CUDA by example: An introduction to general-purpose GPU programming. Addison-Wesley.
  • Sano, Y., & Fukuta, N. (2013). A GPU-based framework for large-scale multi-agent traffic simulations. In IIAI international conference on advanced applied informatics (pp. 262–267). https://doi.org/10.1109/IIAI-AAI.2013.75.
  • Schulz, C. (2013). Efficient local search on the GPU—investigations on the vehicle routing problem. Journal of Parallel and Distributed Computing, 73(1), 14–31. https://doi.org/10.1016/j.jpdc.2012.02.020
  • Shirazaki, M., & Yagawa, G. (1999). Large-scale parallel flow analysis based on free mesh method: A virtually meshless method. Computer Methods in Applied Mechanics and Engineering, 174(3–4), 419–431. https://doi.org/10.1016/S0045-7825(98)00307-7
  • Si, S., & Zheng, H. (2010). High-performance remote sensing image processing using CUDA. In 2010 third international symposium on electronic commerce and security (pp. 121–125). https://doi.org/10.1109/ISECS.2010.35.
  • Smith, M. J., Goodchild, M. F., & Longley, P. (2007). Geospatial analysis: A comprehensive guide to principles, techniques and software tools. Matador.
  • Song, P. T., Zhang, Z. J., Zhang, Q., Liang, L., & Zhao, Q. (2020). Study on heterogeneous computing for MOC neutron transport calculation with CPU-GPU concurrent calculation. Nuclear Power Engineering, 41(4), 17–21. https://doi.org/10.13832/j.jnpe.2020.04.0017
  • Tang, W. (2013). Parallel construction of large circular cartograms using graphics processing units. International Journal of Geographical Information Science, 27(11), 2182–2206. https://doi.org/10.1080/13658816.2013.778413
  • Tang, G. A., Jiang, L., & Liu, K. (2015). Research on parallel technology for digital terrain analysis. Geomatics World, 22(6), 7–15. https://doi.org/10.3969/j.issn.1672-1586.2015.06.002
  • Tomlin, D. (1990). Geographic information systems and cartographic modelling. Prentice Hall.
  • Wang, H. Y. (2019). Hybrid parallel neighbor-related computing for vector point and raster big data based on heterogeneous CPU-GPU systems [ Doctoral dissertation, Wuhan University]. Wuhan University Theses and Dissertations Archive.
  • Wang, F. S., Aji, A., & Vo, H. (2015). High-performance spatial queries for spatial big data: From medical imaging to GIS. SIGSpatial Special, 6(3), 11–18. https://doi.org/10.1145/2766196.2766199
  • Wang, Y. F., Chen, Z. J., Cheng, L., Li, M. C., & Wang, J. C. (2013). Parallel scanline algorithm for rapid rasterization of vector geographic data. Computers & Geosciences, 59, 31–40. https://doi.org/10.1016/j.cageo.2013.05.005
  • Wang, L., Huang, Y. Z., Xin, C., & Zhang, C. Y. (2008). Task scheduling of parallel processing in CPU-GPU collaborative environment. In 2008 international conference on computer science and information technology (pp. 228–232). https://doi.org/10.1109/ICCSIT.2008.27.
  • Wu, Z., Liu, Y., Zhang, L., Li, N., Du, K., & Balz, T. (2015). Highly efficient synthetic aperture radar processing system for airborne sensors using CPU+GPU architecture. Journal of Applied Remote Sensing, 9(1), 097293. https://doi.org/10.1117/1.JRS.9.097293
  • Xia, L. G., Zhang, X. B., Zhang, J. X., Yang, H. P., & Chen, T. T. (2021). Building extraction from very-high-resolution remote sensing images using semi-supervised semantic edge detection. Remote Sensing, 13(11), 2187. https://doi.org/10.3390/rs13112187
  • Xie, C. L., Zhao, R., & Kang, X. C. (2016). Application of multi-core parallel computing in the computation of terrain factors. Science of Surveying & Mapping, 41(3), 40–43. https://doi.org/10.16251/j.cnki.1009-2307.2016.03.008
  • Zhang, F., Li, G., Li, W., Hu, W., & Hu, Y. (2016). Accelerating spaceborne SAR imaging using multiple CPU/GPU deep collaborative computing. Sensors, 16(4), 494. https://doi.org/10.3390/s16040494
  • Zhou, C. (2018). Load-balanced parallel strategies for geospatial analysis over hybrid CPU and GPU architecture [ Doctoral dissertation, Nanjing University]. Nanjing University Theses and Dissertations Archive.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.