gSWORD: GPU-accelerated Sampling for Subgraph Counting
Summary: gSWORD accelerates iterative subgraph-count sampling on GPUs, using sample inheritance and warp streaming to tame irregular workloads and scale to millions of samples per second. A CPU-GPU co-processing pipeline overlaps sampling and enumeration to mitigate underestimation. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chang Ye (Singapore Management University)
- 2. Yuchen Li (Singapore Management University)
- 3. Shixuan Sun (Shanghai Jiao Tong University)
- 4. Wentian Guo (Independent)
BibTeX Citation
@inproceedings{ye_sigmod24,
title = {{gSWORD: GPU-accelerated Sampling for Subgraph Counting}},
author = {Ye, Chang and Li, Yuchen and Sun, Shixuan and Guo, Wentian},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3639288},
url = {https://dl.acm.org/doi/10.1145/3639288},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,173 | uBlade: Efficient Batch Processing for Uncertain Graph Queries | 2024 | SIGMOD | 5.0559291e-05 |
| 10,952 | Efficient GPU-Accelerated Local Subgraph Counting | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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