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

Zhila-Nouri Lewis

doi:10.1109/TKDE.2020.2992440

2022-03-01

Zhila-Nouri Lewis *

Yi-Cheng Tu

DOI：10.1109/TKDE.2020.2992440

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摘要

En 中文

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).

Keyword:

Query processing

Graphics processing units

Spatial databases

Indexing

Parallel processing

Computational modeling

Spatial indexes

Spatial query processing

massively parallel algorithms

GPU computing