Publication
Graph Neural Networks (GNNs) with learnable vertex embeddings enable models to infer rich, task-specific representations even when vertex features are sparse, noisy, or missing. GLPilot introduces a staleness-bounded embedding buffering mechanism to reduce remote fetches and a local gradient aggregation technique to minimize redundant communications during distributed training.
Chengru Yang, Chaoyi Ruan, Chengjie Tang, Ping Gong, Shiyi Wang, Xiang Song, Cheng Li
GLPilot introduces a staleness-bounded embedding buffering mechanism to reduce remote fetches and a local gradient aggregation technique to minimize redundant communications, enabling efficient distributed GNN training with learnable vertex embeddings.