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Fast Optimal Group Steiner Tree Search using GPUs

Summary: First GPU-tailored optimal GST solver: breaks traditional bottom-up DP into a parallel-friendly order and adds GST-specific load balancing (kernel fusion, global-memory coalescing) plus parallel-safe pruning to achieve 48–2390× speedups. Also introduces a GPU DP for diameter-bounded GSTs. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7578
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,370 | 28.86%
DOI
10.1145/3769792

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Authors

BibTeX Citation

@inproceedings{li_sigmod26,
        title = {{Fast Optimal Group Steiner Tree Search using GPUs}},
        author = {Li, Jiayu and Sun, Yahui and Ma, Bojing and Chen, Libang and Hu, Mengxi and Zhang, Feng and Li, Rong-Hua},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769792},
        url = {https://dl.acm.org/doi/10.1145/3769792},
        year = {2026}
}

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Showing 8 of 8 cited papers.

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

Rank Cited Paper Year Venue Pagerank
2,607 GPU-Accelerated Subgraph Enumeration on Partitioned Graphs 2020 SIGMOD 8.3489531e-05
3,686 Accelerating Triangle Counting on GPU 2021 SIGMOD 7.2029305e-05
4,157 GPU-based Graph Traversal on Compressed Graphs 2019 SIGMOD 6.8629037e-05
5,317 Efficient Load-Balanced Butterfly Counting on GPU 2022 VLDB 6.2685847e-05
6,469 Efficient and Progressive Group Steiner Tree Search 2016 SIGMOD 5.8710857e-05
7,491 Self-adaptive Graph Traversal on GPUs 2021 SIGMOD 5.6059269e-05
11,463 Approximating Probabilistic Group Steiner Trees in Graphs 2023 VLDB 5.093636e-05
11,691 Finding Group Steiner Trees in Graphs with both Vertex and Edge Weights 2021 VLDB 5.093636e-05
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