DGCL: An efficient communication library for distributed GNN training

Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics. Distributed GNN training is essential for handling large graphs and reducing the execution time. However, for distributed GNN training, a peer-to-peer communication strategy suffers from high communication overheads. Also, different GPUs require different remote vertex embeddings, which leads to an irregular communication pattern and renders existing communication planning solutions unsuitable. We propose the distributed graph communication library (DGCL) for efficient GNN training on multiple GPUs. At the heart of DGCL is a communication planning algorithm tailored for GNN training, which jointly considers fully utilizing fast links, fusing communication, avoiding contention and balancing loads on different links. DGCL can be easily adopted to extend existing single-GPU GNN systems to distributed training. We conducted extensive experiments on different datasets and network configurations to compare DGCL with alternative communication schemes. In our experiments, DGCL reduces the communication time of the peer-to-peer communication by 77.5% on average and the training time for an epoch by up to 47%.