cuMatch: A GPU-based Memory-Efficient Worst-case Optimal Join Processing Method for Subgraph Queries with Complex Patterns
Summary: cuMatch: GPU-based memory-efficient unified worst-case optimal join for subgraph queries with complex patterns (negative/optional edges). single multi-way join prevents intermediate data explosion; new partitioning, scheduling, and task-fusion techniques yield strong memory efficiency and speed, beating prior methods by orders of magnitude. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Sungwoo Park (Korea Advanced Institute of Science and Technology)
- 2. Seyeon Oh (GraphAI)
- 3. Min-Soo Kim (Korea Advanced Institute of Science and Technology)
BibTeX Citation
@inproceedings{park_sigmod25,
title = {{cuMatch: A GPU-based Memory-Efficient Worst-case Optimal Join Processing Method for Subgraph Queries with Complex Patterns}},
author = {Park, Sungwoo and Oh, Seyeon and Kim, Min-Soo},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725398},
url = {https://dl.acm.org/doi/10.1145/3725398},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,215 | cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries | 2026 | SIGMOD | 5.093636e-05 |
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