GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
This work addresses the problem of inefficient and non-adaptive training for GNN users, offering a practical optimization solution that is incremental in improving existing methodologies.
The paper tackles the challenge of balancing runtime, memory, and accuracy in Graph Neural Network (GNN) training by proposing GNNavigator, an adaptive configuration optimization framework, which achieves up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art methods.
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.