cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries
Summary: cuRPQ is the first GPU-oriented framework for RPQ and CRPQ evaluation, combining novel traversal, visited-set management, and concurrent exploration/materialization. It delivers orders-of-magnitude speedups over prior methods while avoiding GPU out-of-memory failures. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Sungwoo Park (Korea Advanced Institute of Science and Technology)
- 2. Seohyeon Kim (Korea Advanced Institute of Science and Technology)
- 3. Min-Soo Kim (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@inproceedings{park_sigmod26,
title = {{cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries}},
author = {Park, Sungwoo and Kim, Seohyeon and Kim, Min-Soo},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802033},
url = {https://dl.acm.org/doi/10.1145/3802033},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,993 | cuRPQ+: A System for Interactive Path-Aware Querying Beyond Plain CRPQs | 2026 | VLDB | 4.9793485e-05 |
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