CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks

arXiv:2404.02300v1 Announce Type: new
Abstract: Graph neural networks have been shown successful in recent years. While different GNN architectures and training systems have been developed, GNN training on large-scale real-world graphs still remains challenging. Existing distributed systems load the entire graph in memory for graph partitioning, requiring a huge memory space to process large graphs and thus hindering GNN training on such large graphs using commodity workstations. In this paper, we propose CATGNN, a cost-efficient and scalable distributed GNN training …

abstract architectures arxiv cost cs.dc cs.lg distributed distributed systems gnn graph graph neural networks graphs memory networks neural networks partitioning scalable scale systems training type world