Moment: Co-optimizing Physical Communication Topology and Data Placement for Multi-GPU Out-of-core GNN Training

Graph Neural Networks (GNNs) are widely employed in applications like recommendation systems, social network analysis, and fraud detection, but training large-scale GNNs is challenging due to its memory limitations. Existing systems face a trade-off between throughput and monetary cost: Distributed systems require expensive memory scaling, while single-machine out-of-core systems are limited by GPU/PCIe throughput. To this end, we propose Moment, a physical communication topology and data placement co-optimizer to enable high-throughput and low-cost GNN training in a single multi-GPU machine. Moment addresses communication contention and GPU load imbalance issues by modeling the physical topology as capacity-constrained directed graphs and formulating communication scheduling as a max-flow problem. It also introduces a data-distribution-aware knapsack algorithm for optimized data placement. Experimental results show that Moment outperforms out-of-core systems by up to 6.51 × and distributed systems by up to 3.02 ×, with only 50% monetary cost.