Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling
Summary: MorphGL: GNN training for billion-edge graphs via collective batching that dynamically splits mini-batch prep across CPU/GPU instead of static binding. Dual-buffer scheduling co-optimizes CPU, PCIe, and GPU stages, boosting utilization and yielding up to 2.76x over SALIENT. (summarized by gpt-5.4-mini on Apr 12 2026)
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Authors
- 1. Xin Zhang (Hong Kong University of Science and Technology)
- 2. Yanyan Shen (Shanghai Jiao Tong University)
- 3. Yingxia Shao (Beijing Institute of Technology)
- 4. Haoyang Li (Hong Kong Polytechnic University)
- 5. Lei Chen (Hong Kong University of Science and Technology)
BibTeX Citation
@article{zhang_vldb26,
title = {{Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling}},
author = {Zhang, Xin and Shen, Yanyan and Shao, Yingxia and Li, Haoyang and Chen, Lei},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {6},
pages = {1184--1197},
doi = {10.14778/3797919.3797927},
url = {https://doi.org/10.14778/3797919.3797927},
year = {2026}
}
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