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iBFS: Concurrent Breadth-First Search on GPUs

Summary: iBFS is a GPU-based framework for concurrent BFS from multiple sources, with a single joint-traversal kernel, outdegree-based GroupBy to maximize frontier sharing, and bitwise per-vertex checks across BFS instances. Evaluations show up to 30x single-GPU speedup and near-linear scaling to 112 GPUs, achieving peak TEPS in the tens of trillions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5212
Venue
SIGMOD
Year
2016
Pagerank
8.0094569e-05
Overall Rank
2,874 | 80.29%
DOI
10.1145/2882903.2882959

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liu_sigmod16,
        title = {{iBFS: Concurrent Breadth-First Search on GPUs}},
        author = {Liu, Hang and Huang, H. Howie and Hu, Yang},
        series = {{SIGMOD} '16},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2882903.2882959},
        url = {https://dl.acm.org/doi/10.1145/2882903.2882959},
        year = {2016}
}

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