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Scalable and Efficient Full-Graph GNN Training for Large Graphs
Summary: G3 enables scalable full-graph GNN training on billions of edges with hybrid parallelism across three dimensions and p2p intermediate sharing. Partitioning and pipelining overlap compute and comms, yielding up to 2.24× speedup on 16 nodes.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6646
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 7.5939896e-05
- Overall Rank
- 3,087 | 78.53%
- DOI
-
10.1145/3589288
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 13 of 13 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 5,710 |
DynaHB: A Communication-Avoiding Asynchronous Distributed Framework with Hybrid Batches for Dynamic GNN Training |
2024 |
VLDB |
5.3590055e-05 |
| 5,737 |
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
2024 |
VLDB |
5.3480667e-05 |
| 7,289 |
DAHA: Accelerating GNN Training with Data and Hardware Aware Execution Planning |
2024 |
VLDB |
4.7747168e-05 |
| 7,566 |
ADGNN: Towards Scalable GNN Training with Aggregation-Difference Aware Sampling |
2023 |
SIGMOD |
4.7089968e-05 |
| 9,395 |
NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism |
2025 |
VLDB |
4.3441378e-05 |
| 9,804 |
Capsule*: An Out-of-Core Training Mechanism for Colossal GNNs |
2025 |
SIGMOD |
4.2805224e-05 |
| 9,806 |
The Image Calculator: 10x Faster Image-AI Inference by Replacing JPEG with Self-designing Storage Format |
2024 |
SIGMOD |
4.2805224e-05 |
| 10,027 |
NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,298 |
NeutronCloud: Resource-Aware Distributed GNN Training in Fluctuating Cloud Environments |
2026 |
VLDB |
4.1945683e-05 |
| 10,539 |
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch. |
2025 |
VLDB |
4.1945683e-05 |
| 10,570 |
NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism |
2025 |
VLDB |
4.1945683e-05 |
| 10,735 |
Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation |
2025 |
VLDB |
4.1945683e-05 |
| 11,026 |
Improving Graph Compression for Efficient Resource-Constrained Graph Analytics |
2024 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective |
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G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs |
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6.5611714e-05 |
| 2,400 |
ByteGNN: Efficient Graph Neural Network Training at Large Scale |
2022 |
VLDB |
8.8955105e-05 |