GNNavigator: Towards Adaptive Training of Graph Neural Networks via
  Automatic Guideline Exploration
Tong Qiao Jianlei Yang Yingjie Qi Ao Zhou Chen Bai Bei Yu Weisheng Zhao Chunming Hu
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.