用智能网卡预测远程嵌入，大幅减少图神经网络训练通信量。
SNI-GNN: SmartNIC-Assisted Full-Graph GNN Training with In-Network Embedding Prediction
- 在智能网卡上部署轻量预测器，实时修正缓存的嵌入向量。
- 通信量降低21%~45%，训练速度提升1.3~3.6倍，精度损失小于0.01。
- 适合大规模图神经网络训练，尤其适用于多服务器集群场景。
全图图神经网络训练虽精度高，但在多服务器集群中因节点间嵌入数据交换频繁且不规则，导致扩展性差。本文提出SNI-GNN，一种基于智能网卡的全图训练系统，通过在网内预测远程嵌入来减少通信开销，同时保持高精度。SNI-GNN在NVIDIA BlueField-3智能网卡上部署轻量级线性趋势预测器，用于优化缓存的历史嵌入；结合重要性驱动的边界节点采样策略和异步DPU-GPU数据流水线，并支持中间结果复用。理论分析表明，在二阶动态有界条件下，预测偏差可控，仍可实现标准非凸收敛。实测结果显示，相较于BNS-GCN，SNI-GNN通信量减少21%~45%，端到端加速比达1.3~3.6倍；相比基线SANCUS，最高提速1.29倍，精度损失不超过0.01，在含数千万边的图上可高效扩展至16个GPU。
原文摘要 · Abstract (English)
Full-graph GNN training delivers high accuracy but scales poorly on multi-server clusters due to heavy, irregular inter-node embedding exchanges. We present SNI-GNN, a SmartNIC-assisted full-graph training system that reduces communication while preserving accuracy by predicting remote embeddings in-network. SNI-GNN deploys a lightweight linear-trend predictor on SmartNICs to refine cached historical embeddings, coupled with an importance-based boundary-node sampling policy and an asynchronous DPU--GPU data pipeline with intermediate-result reuse. We provide error and convergence bounds showing that predictor bias remains controlled under bounded second-order dynamics and yields standard non-convex convergence with inexact gradients. Implemented on NVIDIA BlueField-3, SNI-GNN integrates with state-of-the-art full-graph systems, cuts communication by 21--45\%, achieves 1.3--3.6$\times$ end-to-end speedups over BNS-GCN and up to 1.29$\times$ over baseline SANCUS, with accuracy loss $\leq 0.01$, and scales efficiently to 16 GPUs on graphs with up to tens of millions of edges. These results indicate SmartNIC-based in-network prediction is a practical complement to partitioning and compression techniques for communication-efficient full-graph GNN training at scale.
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