Emerging graph neural networks (GNNs) have extended the successes of deep learning techniques against datasets like images and texts to more complex graph-structured data. By leveraging GPU accelerators, existing frameworks combine mini-batch and sampling for effective and efficient model training on large graphs. However, this setup faces a scalability issue since loading rich vertex features from CPU to GPU through a limited bandwidth link usually dominates the training cycle. In this article, we propose PaGraph, a novel, efficient data loader that supports general and efficient sampling-based GNN training on single-server with multi-GPU. PaGraph significantly reduces the data loading time by exploiting available GPU resources to keep frequently-accessed graph data with a cache. It also embodies a lightweight yet effective caching policy that takes into account graph structural information and data access patterns of sampling-based GNN training simultaneously. Furthermore, to scale out on multiple GPUs, PaGraph develops a fast GNN-computation-aware partition algorithm to avoid cross-partition access during data-parallel training and achieves better cache efficiency. Finally, it overlaps data loading and GNN computation for further hiding loading costs. Evaluations on two representative GNN models, GCN and GraphSAGE, using two sampling methods, Neighbor and Layer-wise, show that PaGraph could eliminate the data loading time from the GNN training pipeline, and achieve up to 4.8x performance speedup over the state-of-the-art baselines. Together with preprocessing optimization, PaGraph further delivers up to 16.0x end-to-end speedup.
