HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric Model Migration
<|reference_start|>HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric Model Migration: Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that trains the model using a refined structure with superior locality to reduce remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2x compared to the state-of-the-art method, namely P3.<|reference_end|>
@article{chen2024hopgnn:,
    title={HopGNN: Boosting Distributed GNN Training Efficiency via Feature-Centric
  Model Migration},
    author={Weijian Chen, Shuibing He, Haoyang Qu, and Xuechen Zhang},
    journal={arXiv preprint arXiv:2409.00657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00657},
    primaryClass={cs.DC}
}