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Self-adaptive Graph Traversal on GPUs

Summary: Self-adaptive graph traversal on GPUs; no preprocessing; operates on universal graphs. Proposes Tiled Partition and Resident Tile Stealing for runtime GPU use, plus Sampling-based Reordering for memory efficiency; beats preprocessing-based methods across GPU deployments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6232
Venue
SIGMOD
Year
2021
Pagerank
5.6059269e-05
Overall Rank
7,491 | 48.61%
DOI
10.1145/3448016.3457279

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{sha_sigmod21,
        title = {{Self-adaptive Graph Traversal on GPUs}},
        author = {Sha, Mo and Li, Yuchen and Tan, Kian-Lee},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457279},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457279},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
10,370 Fast Optimal Group Steiner Tree Search using GPUs 2026 SIGMOD 5.093636e-05
10,606 Efficient Partition-based Approaches for Diversified Top-k Subgraph Matching 2026 VLDB 5.093636e-05
10,609 Efficient Temporal Edge-Core Maintenance in Streaming Graphs 2026 VLDB 5.093636e-05
11,165 gSWORD: GPU-accelerated Sampling for Subgraph Counting 2024 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

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