Parallel Compression and Indexing of Large-Scale Geospatial Raster Data with GPGPUs

Nathalie Kaligirwa, Eleazar Leal, Le Gruenwald, Jianting Zhang, Simin You

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Global remote sensing and large-scale environment modeling have generated vast amounts of raster geospatial data. Performing spatial queries over such data has applications in many domains, such as climate impact studies, water and wildlife management, and urban planning. Processing those queries is greatly facilitated by the existence of spatial indices. However, I/O transfer is still a major bottleneck in the overall system performance. One of the solutions to this issue is to compress data before sending it over the I/O channel. Therefore, a lossless compression technique that also supports spatial indexing to improve query response time is highly desirable. To fill this gap, in this paper we propose two parallel GPGPU algorithms, called Multi-Block per Tile (MBPT) and One-Block per Tile (OBPT), to compress and index large-scale geospatial raster data using BQ-Trees. Experiments comparing our best performing proposed algorithm, OBPT, against HFPaC, a state-of-the-art geospatial parallel GPGPU compression algorithm, using three real datasets of satellite images, show that our algorithm achieves a compression time speedup of up to 2X, and a 2.5X increment in compression ratio. OBPT also yields a comparable average spatial query response time to HFPaC.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017
EditorsGeorge Karypis, Jia Zhang
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages137-144
Number of pages8
ISBN (Electronic)9781538619964
DOIs
StatePublished - Sep 7 2017
Event6th IEEE International Congress on Big Data, BigData Congress 2017 - Honolulu, United States
Duration: Jun 25 2017Jun 30 2017

Publication series

NameProceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017

Other

Other6th IEEE International Congress on Big Data, BigData Congress 2017
CountryUnited States
CityHonolulu
Period6/25/176/30/17

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Keywords

  • query processing on GPGPUs
  • query processing on raster data
  • raster data compression
  • raster data indexing

Cite this

Kaligirwa, N., Leal, E., Gruenwald, L., Zhang, J., & You, S. (2017). Parallel Compression and Indexing of Large-Scale Geospatial Raster Data with GPGPUs. In G. Karypis, & J. Zhang (Eds.), Proceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017 (pp. 137-144). [8029319] (Proceedings - 2017 IEEE 6th International Congress on Big Data, BigData Congress 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataCongress.2017.27