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A Versatile and Efficient GPU Data Structure for Spatial Indexing.

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    We introduce a new GPU-accelerated spatial indexing data structure using Fenwick trees. This structure offers efficient construction, updates, and queries for large datasets, outperforming existing methods.

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    Area of Science:

    • Computer Science
    • Data Structures
    • GPU Computing

    Background:

    • Spatial indexing is crucial for managing large datasets in applications like block-storage.
    • Existing methods like summed-area tables and spatial hashing have limitations in terms of memory or query flexibility.
    • Efficient data structures are needed to handle the increasing scale of data in modern computing.

    Purpose of the Study:

    • To present a novel GPU-based data structure for efficient spatial indexing.
    • To demonstrate its advantages over existing spatial indexing techniques.
    • To analyze its performance and identify suitable applications.

    Main Methods:

    • Developed a spatial indexing data structure based on Fenwick trees (binary indexed trees).
    • Leveraged GPU acceleration for enhanced performance.
    • Analyzed time and memory complexity for construction, updates, and queries.

    Main Results:

    • Achieved linear time construction, logarithmic time updates and prefix computations, and average constant time point queries.
    • Requires only a constant amount of bits per data element.
    • Offers unconstrained point queries, surpassing limitations of summed-area tables and spatial hashing.
    • Provided asymptotic bounds for run-time and memory requirements.

    Conclusions:

    • The novel Fenwick tree-based GPU data structure provides efficient and flexible spatial indexing.
    • Its constant memory footprint and unconstrained queries make it ideal for large-scale data applications, including block-sparse volumes.
    • The structure offers significant performance improvements over competing methods for spatial indexing tasks.