G-PICS: A Framework for GPU-Based Spatial Indexing and Query Processing

• Published in: IEEE transactions on knowledge and data engineering Vol. 34; no. 3; pp. 1243 - 1257
• Main Authors: Lewis, Zhila-Nouri, Tu, Yi-Cheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computational modeling; Data processing; Data storage; GPU computing; Graphics processing units; Indexing; massively parallel algorithms; Parallel processing; Queries; Query processing; Spatial data; Spatial databases; Spatial indexes; Spatial query processing
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2020.2992440

Support for efficient spatial data storage and retrieval has become a vital component in almost all spatial database systems. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database systems could receive a large number of queries simultaneously. In this paper, we present a comprehensive framework named G-PICS for parallel processing of concurrent spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in a Multi-GPU environment to support datasets beyond the size of a single GPU's global memory. Empirical evaluation of G-PICS shows significant performance improvement over the state-of-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X) and query processing (up to 80X).