PUSHGNN: A Low-communication Runtime System for GNN Acceleration on Multi-GPUs

The need for multi-GPU platforms in graph neural networks (GNNs) has been driven by the growing size of input graphs. However, although the existing multi-gpu GNN framework has been optimized from the perspective of optimizing computing and communication operations, communication competition still exists. To this end, we introduce PUSHGNN, a runtime system designed to reduce communication overhead across GPUs, boosting GNN performance. Therefore, we designed a push-based pipeline communication model and made custom tuning to significantly reduce pipeline contention. Comparative assessments demonstrate that PUSHGNN consistently outperforms leading full-graph GNN systems on average 1.97× and 7.57× faster than MGG and MGG-UVM, respectively.