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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jun Liu (Huazhong University of Science and Technology)
- 2. Bingqian Du (Huazhong University of Science and Technology)
- 3. Ziyue Luo (Ohio State University)
- 4. Sitian Lu (Huazhong University of Science and Technology)
- 5. Qiankun Zhang (Huazhong University of Science and Technology)
- 6. Hai Jin (Huazhong University of Science and Technology)
BibTeX Citation
@article{liu_vldb25,
title = {{PipeTGL: (Near) Zero Bubble Memory-based Temporal Graph Neural Network Training via Pipeline Optimization}},
author = {Liu, Jun and Du, Bingqian and Luo, Ziyue and Lu, Sitian and Zhang, Qiankun and Jin, Hai},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {8},
pages = {2722--2734},
doi = {10.14778/3742728.3742760},
url = {https://doi.org/10.14778/3742728.3742760},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 920 | APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding | 2021 | SIGMOD | 0.00013209734 |
| 1,409 | TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs | 2022 | VLDB | 0.00010854808 |
| 3,210 | Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank | 2023 | VLDB | 7.6352864e-05 |
| 3,634 | Orca: Scalable Temporal Graph Neural Network Training with Theoretical Guarantees | 2023 | SIGMOD | 7.2358691e-05 |
| 4,878 | NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams | 2024 | VLDB | 6.4684388e-05 |
| 5,205 | ETC: Efficient Training of Temporal Graph Neural Networks over Large-scale Dynamic Graphs | 2024 | VLDB | 6.318626e-05 |
Previous
Page 1 / 1
Next