  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.
