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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)

Paper ID
14102
Venue
VLDB
Year
2025
Pagerank
5.093636e-05
Overall Rank
10,887 | 25.31%
DOI
10.14778/3742728.3742760

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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}
}

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