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NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism

Summary: NeutronTP replaces graph/data parallelism with feature-sliced tensor parallelism, eliminating cross-worker vertex dependencies and balancing GNN training loads. Decoupled aggregation/NN computation and memory-aware, overlap-oriented scheduling enable large-graph training, yielding 1.29–8.72× speedups. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
14020
Venue
VLDB
Year
2025
Pagerank
5.2528121e-05
Overall Rank
9,546 | 34.51%
DOI
10.14778/3705829.3705837

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{ai_vldb25,
        title = {{NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism}},
        author = {Ai, Xin and Yuan, Hao and Ling, Zeyu and Wang, Qiange and Zhang, Yanfeng and Fu, Zhenbo and Chen, Chaoyi and Gu, Yu and Yu, Ge},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {2},
        pages = {173--186},
        doi = {10.14778/3705829.3705837},
        url = {https://doi.org/10.14778/3705829.3705837},
        year = {2025}
}

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