Accelerating Triangle Counting on GPU
Summary: Lightweight graph preprocessing boosts GPU triangle counting without changing code. Proposes analytic models for workload imbalance and pattern diversity; uses approx edge directions and vertex reordering to balance load and boost GPU parallelism. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
BibTeX Citation
@inproceedings{hu_sigmod21,
title = {{Accelerating Triangle Counting on GPU}},
author = {Hu, Lin and Zou, Lei and Liu, Yu},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3452815},
url = {https://dl.acm.org/doi/10.1145/3448016.3452815},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,695 | NeutronStar: Distributed GNN Training with Hybrid Dependency Management | 2022 | SIGMOD | 8.2468134e-05 |
| 5,317 | Efficient Load-Balanced Butterfly Counting on GPU | 2022 | VLDB | 6.2685847e-05 |
| 9,866 | Finding Logic Bugs in Graph-processing Systems via Graph-cutting | 2025 | SIGMOD | 5.2043672e-05 |
| 10,252 | GraphRTX: Lighting the Way to Scalable Graph Analytics | 2026 | SIGMOD | 5.093636e-05 |
| 10,370 | Fast Optimal Group Steiner Tree Search using GPUs | 2026 | SIGMOD | 5.093636e-05 |
| 10,376 | GraphTwin: Cache-Centric Bit-Level Graph Representation for Fast and Exact Graph Queries | 2026 | SIGMOD | 5.093636e-05 |
| 10,857 | Truss Decomposition in Hypergraphs | 2025 | VLDB | 5.093636e-05 |
| 11,087 | Towards Sufficient GPU-accelerated Dynamic Graph Management: Survey and Experiment | 2025 | VLDB | 5.093636e-05 |
| 11,094 | Efficient Computation of Hyper-triangles on Hypergraphs | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 102 | Truss Decomposition in Massive Networks | 2012 | VLDB | 0.00034255289 |
| 825 | K-Core Decomposition of Large Networks on a Single PC | 2016 | VLDB | 0.00013779895 |
| 1,655 | Efficient Parallel Lists Intersection and Index Compression Algorithms using Graphics Processing Units | 2011 | VLDB | 0.00010103504 |
| 2,112 | Speeding Up Set Intersections in Graph Algorithms using SIMD Instructions | 2018 | SIGMOD | 9.1514258e-05 |
| 3,503 | Accelerating Truss Decomposition on Heterogeneous Processors | 2020 | VLDB | 7.3592701e-05 |
| 4,088 | Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining | 2011 | VLDB | 6.912485e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,088 | Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining | 2011 | VLDB |
| 2 | 9,375 | Efficiently Counting Triangles in Large Temporal Graphs | 2025 | SIGMOD |
| 3 | 5,110 | Better Algorithms for Counting Triangles in Data Streams | 2016 | PODS |
| 4 | 594 | Massive Graph Triangulation | 2013 | SIGMOD |
| 5 | 2,607 | GPU-Accelerated Subgraph Enumeration on Partitioned Graphs | 2020 | SIGMOD |
| 6 | 1,103 | Counting and Sampling Triangles from a Graph Stream | 2013 | VLDB |
| 7 | 2,635 | Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges | 2021 | SIGMOD |
| 8 | 5,317 | Efficient Load-Balanced Butterfly Counting on GPU | 2022 | VLDB |
| 9 | 4,157 | GPU-based Graph Traversal on Compressed Graphs | 2019 | SIGMOD |
| 10 | 3,503 | Accelerating Truss Decomposition on Heterogeneous Processors | 2020 | VLDB |