Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs
Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core settings. Existing distributed GNN systems incur high communication times and infrastructure costs, while disk-based GNN systems are primarily tailored to training and experience massive wasted reads during inference on the entire graph. We present Taurus, a single-machine system for GNN inference on graphs that do not fit in RAM, supporting both \textit{exact} full-graph inference and fanout-sampled inference. To avoid random and repeated feature gathers, Taurus reformulates layer-wise inference as source-centric b...
| Authors | Pranjal Naman, Yogesh Simmhan |
|---|---|
| Primary category | cs.DC |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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