Abstract
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.
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Notes
- 1.
Open-sourced at https://github.com/danghr/GDL-GNN.
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Acknowledgement
The authors sincerely appreciates the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (Grant No. 62202451), CAS Project for Young Scientists in Basic Research (Grant No. YSBR-029), and CAS Project for Youth Innovation Promotion Association.
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Dang, H., Wu, M., Yan, M., Ye, X., Fan, D. (2024). GDL-GNN: Applying GPU Dataloading of Large Datasets for Graph Neural Network Inference. In: Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., Schreiber, M. (eds) Euro-Par 2024: Parallel Processing. Euro-Par 2024. Lecture Notes in Computer Science, vol 14802. Springer, Cham. https://doi.org/10.1007/978-3-031-69766-1_24
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