Grappa: Gradient-Only Communication for Scalable Graph Neural Network Training
This addresses scalability issues in distributed GNN training for large graphs, enabling trillion-edge training on commodity hardware.
The paper tackles the high communication cost of distributed graph neural network training by introducing Grappa, a framework that uses gradient-only communication and periodic repartitioning, achieving up to 13 times faster training and better accuracy for deeper models.
Cross-partition edges dominate the cost of distributed GNN training: fetching remote features and activations per iteration overwhelms the network as graphs deepen and partition counts grow. Grappa is a distributed GNN training framework that enforces gradient-only communication: during each iteration, partitions train in isolation and exchange only gradients for the global update. To recover accuracy lost to isolation, Grappa (i) periodically repartitions to expose new neighborhoods and (ii) applies a lightweight coverage-corrected gradient aggregation inspired by importance sampling. We prove the corrected estimator is asymptotically unbiased under standard support and boundedness assumptions, and we derive a batch-level variant for compatibility with common deep-learning packages that minimizes mean-squared deviation from the ideal node-level correction. We also introduce a shrinkage version that improves stability in practice. Empirical results on real and synthetic graphs show that Grappa trains GNNs 4 times faster on average (up to 13 times) than state-of-the-art systems, achieves better accuracy especially for deeper models, and sustains training at the trillion-edge scale on commodity hardware. Grappa is model-agnostic, supports full-graph and mini-batch training, and does not rely on high-bandwidth interconnects or caching.