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.
@article{arxiv.2404.09544,
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}
}