Hardware Acceleration for Spatial Selections and Joins

Summary: GPU-accelerated refinement for spatial queries uses graphics hardware to speed up polygon/geometry comparisons, reducing CPU load in the refinement step. No pre-processing or index changes needed; works with both intersection and distance predicates, delivering notable end-to-end speedups by combining hardware and software. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3516
Venue: SIGMOD
Year: 2003
Pagerank: 8.8099424e-05
Overall Rank: 2,283 | 84.34%
DOI: 10.1145/872757.872813

Incoming Non-self Citations Over Time

Authors

1. Chengyu Sun (University of California Santa Barbara)
2. Divyakant Agrawal (University of California Santa Barbara)
3. Amr El Abbadi (University of California Santa Barbara)

BibTeX Citation

@inproceedings{sun_sigmod03,
        title = {{Hardware Acceleration for Spatial Selections and Joins}},
        author = {Sun, Chengyu and Agrawal, Divyakant and Abbadi, Amr El},
        series = {{SIGMOD} '03},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/872757.872813},
        url = {https://dl.acm.org/doi/10.1145/872757.872813},
        year = {2003}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
305	GPUTeraSort: High Performance Graphics Co-processor Sorting for Large Database Management	2006	SIGMOD	0.00021872796
631	Relational Joins on Graphics Processors	2008	SIGMOD	0.00015591241
1,452	Fast Computation of Database Operations using Graphics Processors	2004	SIGMOD	0.00010745803
2,655	Executing Stream Joins on the Cell Processor	2007	VLDB	8.2888851e-05
2,741	Hardware Acceleration in Commercial Databases: A Case Study of Spatial Operations	2004	VLDB	8.1851944e-05
5,216	Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors	2005	SIGMOD	6.3121293e-05
5,709	GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons	2018	VLDB	6.1142154e-05
5,871	Cheetah: Accelerating Database Queries with Switch Pruning	2020	SIGMOD	6.0604381e-05
7,113	GPUQP: Query Co-Processing Using Graphics Processors	2007	SIGMOD	5.6985502e-05
8,487	A Case for Graphics-driven Query Processing	2023	VLDB	5.4153858e-05
8,580	Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems	2012	VLDB	5.4082911e-05
9,544	SwiftSpatial: Spatial Joins on Modern Hardware	2025	SIGMOD	5.2528121e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2	R-Trees: A Dynamic Index Structure For Spatial Searching	1984	SIGMOD	0.0020210012
522	Multi-Step Processing of Spatial Joins	1994	SIGMOD	0.00017133516
3,170	A Raster Approximation for the Processing of Spatial Joins	1998	VLDB	7.6703896e-05
3,217	Quadtree and R-tree Indexes in Oracle Spatial: A Comparison using GIS Data	2002	SIGMOD	7.6314544e-05

Semantically Similar Papers

#	Overall Rank	Paper	Year	Venue
1	3,703	Approximation Techniques for Spatial Data	2004	SIGMOD
2	8,614	The Case for Distance-Bounded Spatial Approximations	2021	CIDR
3	145	Efficient Processing of Spatial Joins Using R-trees	1993	SIGMOD
4	2,310	Spatial Joins Using Seeded Trees	1994	SIGMOD
5	3,170	A Raster Approximation for the Processing of Spatial Joins	1998	VLDB
6	9,544	SwiftSpatial: Spatial Joins on Modern Hardware	2025	SIGMOD
7	5,709	GPU Rasterization for Real-Time Spatial Aggregation over Arbitrary Polygons	2018	VLDB
8	6,629	A GPU-friendly Geometric Data Model and Algebra for Spatial Queries	2020	SIGMOD
9	522	Multi-Step Processing of Spatial Joins	1994	SIGMOD
10	2,741	Hardware Acceleration in Commercial Databases: A Case Study of Spatial Operations	2004	VLDB