GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
<|reference_start|>GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration: Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design space exploration solution. Experimental results show that GNNavigator can achieve up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art approaches.<|reference_end|>
@article{qiao2024gnnavigator:,
    title={GNNavigator: Towards Adaptive Training of Graph Neural Networks via
  Automatic Guideline Exploration},
    author={Tong Qiao and Jianlei Yang and Yingjie Qi and Ao Zhou and Chen Bai and
  Bei Yu and Weisheng Zhao and Chunming Hu},
    journal={arXiv preprint arXiv:2404.09544},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09544},
    primaryClass={cs.LG cs.AI}
}