GDL-GNN: Applying GPU Dataloading of Large Datasets for Graph Neural Network Inference

Graph neural networks (GNNs) have emerged as a popular choice for analyzing structured data organized as graphs. Nevertheless, GNN models tend to be shallow, failing to fully exploit the capabilities of modern GPUs. Our motivational tests reveal that GPU dataloading for GNN inference yields remarkable performance enhancements when both the graph topology and features reside in GPU memory. Unfortunately, the use of this approach is hindered by the large size of real-world graph datasets. To address this limitation, we introduce GDL-GNN, a partition-based method that incorporates all essential information for inference within each subgraph. It thus combines the efficiency of GPU dataloading with layerwise inference, while maintaining the accuracy of full-neighbor inference. Additional optimization enables GDL-GNN to avoid unnecessary representation computation on halo nodes and to conceal file loading time. Evaluation shows the effectiveness of GDL-GNN in both single- and multi-GPU scenarios, revealing a reduction in inference time of up to 59.9% without compromising accuracy.