RayBooster: A Ray Tracing Engine to Accelerate SedonaDB
Summary: RayBooster integrates GPU RT cores into production SedonaDB for spatial joins. A GPU-friendly layout, monolithic ID-encoded BVH, unified DE-9IM RelateEngine, and memory-aware execution yield up to 5.93× speedup and 59% lower cost. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Liang Geng (Ohio State University)
- 2. Rubao Lee (Ohio State University)
- 3. Dewey Dunnington (Wherobots, Inc.)
- 4. Feng Zhang (Wherobots, Inc.)
- 5. Jia Yu (Wherobots, Inc.)
- 6. Xiaodong Zhang (Ohio State University)
BibTeX Citation
@article{geng_vldb26,
title = {{RayBooster: A Ray Tracing Engine to Accelerate SedonaDB}},
author = {Geng, Liang and Lee, Rubao and Dunnington, Dewey and Zhang, Feng and Yu, Jia and Zhang, Xiaodong},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {3902--3915},
doi = {10.14778/3827998.3828004},
url = {https://doi.org/10.14778/3827998.3828004},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,023 | Hardware Acceleration for Spatial Databases: Building Next-Generation Query Engines Using Ray Tracing Cores | 2026 | VLDB | 4.9793485e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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