PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization
Summary: PipeTGL: pipeline-parallel training for memory-based temporal GNNs that models inter-minibatch memory dependencies via a runtime DAG and uses fine-grained scheduling, operation reordering, and targeted communication to honor chronological memory constraints. Achieves near-zero pipeline bubbles, reduces GPU idle/communication overhead, and delivers 1.27–4.74× speedups with improved multi-GPU training accuracy. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Jun Liu
- 2. Bingqian Du
- 3. Ziyue Luo
- 4. Sitian Lu
- 5. Qiankun Zhang
- 6. Hai Jin
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 636 | APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding | 2021 | SIGMOD | 0.00018846494 |
| 1,387 | TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs | 2022 | VLDB | 0.00012261568 |
| 3,709 | Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank | 2023 | VLDB | 6.8242482e-05 |
| 4,047 | Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees | 2023 | SIGMOD | 6.4972105e-05 |
| 5,345 | NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams | 2024 | VLDB | 5.5567697e-05 |
| 5,475 | ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs | 2024 | VLDB | 5.4869706e-05 |
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