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GPU-based cloud performance for LiDAR data processing

Authors:

Ramanathan Sugumaran,

Dossay Oryspayev,

Paul GrayAuthors Info & Claims

COM.Geo '11: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications

Article No.: 48, Pages 1 - 2

https://doi.org/10.1145/1999320.1999369

Published: 23 May 2011 Publication History

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Abstract

Goal of this paper is to compare the timing/performance results of CPU and GPU on local and Cloud platform for processing massive Light Detecting and Ranging (LiDAR) topographic data. We have used locally various multi-core CPU technologies as well as GPU implementations on various graphics cards of nVidia which support CUDA, where as a cloud computing infrastructure we utilized various components of the Amazon Web Services (AWS). In order to study the performance, we have developed and implemented vertex decimation algorithm for data reduction of LiDAR point cloud. Our presentation will demonstrate the preliminary results by comparing the multi-core CPU and GPU based implementations of the code, as well as the comparison with cloud performance.

References

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Han, S. H., Heo, J., Sohn, H. G., and Yu, K. 2009. Parallel Processing Method for Airborne Laser Scanning Data Using a PC Cluster and a Virtual Grid. Sensors 2009, 9 (4): 2555--2573;

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Krishnan, S., Bary, C., Crosby, C., 2010. Evaluation of MapReduce for Gridding LIDAR Data, CloudCom, pp. 33--40, 2010 IEEE Second International Conference on Cloud Computing Technology and Science.

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Iowa Lidar Mapping Project (ILMP), GeoInformatics Training, Research, Education, and Extension (GeoTRE) Center of University of Northern Iowa, http://geotree2.geog.uni.edu/lidar/ Accessed on February 11, 2011.

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LAStools. http://www.cs.unc.edu/~isenburg/lastools/ Accessed on February 11, 2011.

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Cited By

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Kaynak SKaynak BOzmen A(2020)GPU-Based Dynamic Solar Potential Estimation Tool Using 3D PlansIEEE Access10.1109/ACCESS.2020.29785908(45432-45442)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2978590
Hu XGong JRenard EParashar M(2019)Two-Stage Framework for Big Spatial Data Analytics to Support Disaster Response2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005613(5409-5418)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9005613
Rizki PEum JLee HOh S(2017)Spark-based in-memory DEM creation from 3D LiDAR point cloudsRemote Sensing Letters10.1080/2150704X.2016.12750538:4(360-369)Online publication date: 3-Jan-2017
https://doi.org/10.1080/2150704X.2016.1275053
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Index Terms

GPU-based cloud performance for LiDAR data processing
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications

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Published In

COM.Geo '11: Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications

May 2011

292 pages

ISBN:9781450306812

DOI:10.1145/1999320

Conference Chair:
Lindi Liao

Publisher

Association for Computing Machinery

New York, NY, United States

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Published: 23 May 2011

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COM.Geo '11

COM.Geo '11: 2nd International Conference on Computing for Geospatial Research & Application

May 23 - 25, 2011

DC, Washington, USA

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Kaynak SKaynak BOzmen A(2020)GPU-Based Dynamic Solar Potential Estimation Tool Using 3D PlansIEEE Access10.1109/ACCESS.2020.29785908(45432-45442)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2978590
Hu XGong JRenard EParashar M(2019)Two-Stage Framework for Big Spatial Data Analytics to Support Disaster Response2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005613(5409-5418)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9005613
Rizki PEum JLee HOh S(2017)Spark-based in-memory DEM creation from 3D LiDAR point cloudsRemote Sensing Letters10.1080/2150704X.2016.12750538:4(360-369)Online publication date: 3-Jan-2017
https://doi.org/10.1080/2150704X.2016.1275053
Xiangyun Hu Xiaokai Li Yongjun Zhang (2013)Fast Filtering of LiDAR Point Cloud in Urban Areas Based on Scan Line Segmentation and GPU AccelerationIEEE Geoscience and Remote Sensing Letters10.1109/LGRS.2012.220513010:2(308-312)Online publication date: Mar-2013
https://doi.org/10.1109/LGRS.2012.2205130
Li JJiang YYang CHuang QRice M(2013)Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs)Computers & Geosciences10.1016/j.cageo.2013.04.02959(78-89)Online publication date: Sep-2013
https://doi.org/10.1016/j.cageo.2013.04.029
Huang QLi ZXia JJiang YXu CLiu KYu MYang C(2013)Accelerating Geocomputation with Cloud ComputingModern Accelerator Technologies for Geographic Information Science10.1007/978-1-4614-8745-6_4(41-51)Online publication date: 25-Sep-2013
https://doi.org/10.1007/978-1-4614-8745-6_4
Sugumaran RBurnett JBlinkmann AChandola VVatsavai RGupta C(2012)Big 3D spatial data processing using cloud computing environmentProceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data10.1145/2447481.2447484(20-22)Online publication date: 6-Nov-2012
https://dl.acm.org/doi/10.1145/2447481.2447484

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