GNNavigator accepted by DAC 2024: automatic guideline exploration for adaptive GNN training

👏 Paper title: GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration.

GNNavigator addresses the difficulty of optimizing GNN training across diverse graphs, models, and hardware platforms. GNN training performance depends on sampling, aggregation, memory behavior, and device characteristics, making fixed optimization rules brittle.

The framework introduces software-hardware co-abstraction and performance modeling to automatically explore training guidelines. It helps select configurations that balance runtime, memory usage, and accuracy, enabling more adaptive GNN training optimization across graph learning workloads.

The work is motivated by the fact that GNN training behavior varies dramatically across datasets and platforms. Sampling choices, graph topology, feature dimensions, and accelerator characteristics can all change the best training strategy.

GNNavigator turns this tuning process into an automated exploration problem. Instead of relying on fixed heuristics, it searches for guidelines that match the current workload and hardware, making GNN training more robust across diverse scenarios.