Gpu-based Spatial Indexing

GPU-Based Spatial Indexing is a spatial indexing method that leverages the parallel processing capabilities of graphics processing units (GPUs) to accelerate the construction, querying, and maintenance of spatial data structures, distinguishing it from traditional CPU-based spatial indexing techniques by exploiting massive thread-level parallelism for high-throughput spatial data operations.

19

Publications

514

Citations

34

Authors

20

Institutions

Publications per year

2017–2026

4

Authors

34

  1. JZ
    Jianting Zhang

    City College of New York, United States

    7 publications · 196 citations

    7

    h-index

  2. SY
    Simin You

    City College of New York, United States

    6 publications · 175 citations

    6

    h-index

  3. LG
    Le Gruenwald

    University of Oklahoma, United States

    4 publications · 124 citations

    4

    h-index

  4. MG
    Michael Gowanlock

    Northern Arizona University, United States

    2 publications · 36 citations

    2

    h-index

  5. LG
    Luke Gosink

    Pacific Northwest National Laboratory, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 34

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 48 citations

    3

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 1 publications · 21 citations

    1

    h-index

  3. SIGSPATIAL Special

    1 publications · 26 citations

    1

    h-index

  4. 1

    h-index

  5. 1

    h-index

1–5