Graph neural networks (GNNs) have exhibited exceptional efficacy in a diverse
array of applications. However, the sheer size of large-scale graphs presents a
significant challenge to real-time inference with GNNs. Although existing
Scalable GNNs leverage linear propagation to preprocess the features and
accelerate the training and inference procedure, these methods still suffer
from scalability issues when making inferences on unseen nodes, as the feature
preprocessing requires the graph to be known and fixed. To further accelerate
Scalable GNNs inference in this inductive setting, we propose an online
propagation framework and two novel node-adaptive propagation methods that can
customize the optimal propagation depth for each node based on its topological
information and thereby avoid redundant feature propagation. The trade-off
between accuracy and latency can be flexibly managed through simple
hyper-parameters to accommodate various latency constraints. Moreover, to
compensate for the inference accuracy loss caused by the potential early
termination of propagation, we further propose Inception Distillation to
exploit the multi-scale receptive field information within graphs. The rigorous
and comprehensive experimental study on public datasets with varying scales and
characteristics demonstrates that the proposed inference acceleration framework
outperforms existing state-of-the-art graph inference acceleration methods in
terms of accuracy and efficiency. Particularly, the superiority of our approach
is notable on datasets with larger scales, yielding a 75x inference speedup on
the largest Ogbn-products dataset.

大规模图的实时推断对于图神经网络 (GNNs) 提出了一项重大挑战，但我们提出了一种在线传播框架和两种新的节点自适应传播方法来加速可扩展 GNNs 的推断过程，并通过简单的超参数灵活管理准确性和时延之间的平衡。此外，我们进一步提出了 Inception Distillation 来弥补推断准确性损失，实验结果显示我们的方法在精确性和效率上优于现有的图推断加速方法。

基于节点自适应传播的加速可扩展的图神经网络推理

Accelerating Scalable Graph Neural Network Inference with Node-Adaptive  Propagation

Graph neural networks (GNNs) are a type of deep learning models that learning
over graphs, and have been successfully applied in many domains. Despite the
effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to
large graphs. As a remedy, distributed computing becomes a promising solution
of training large-scale GNNs, since it is able to provide abundant computing
resources. However, the dependency of graph structure increases the difficulty
of achieving high-efficiency distributed GNN training, which suffers from the
massive communication and workload imbalance. In recent years, many efforts
have been made on distributed GNN training, and an array of training algorithms
and systems have been proposed. Yet, there is a lack of systematic review on
the optimization techniques from graph processing to distributed execution. In
this survey, we analyze three major challenges in distributed GNN training that
are massive feature communication, the loss of model accuracy and workload
imbalance. Then we introduce a new taxonomy for the optimization techniques in
distributed GNN training that address the above challenges. The new taxonomy
classifies existing techniques into four categories that are GNN data
partition, GNN batch generation, GNN execution model, and GNN communication
protocol.We carefully discuss the techniques in each category. In the end, we
summarize existing distributed GNN systems for multi-GPUs, GPU-clusters and
CPU-clusters, respectively, and give a discussion about the future direction on
scalable GNNs.

这篇综述论文分析了分布式图神经网络训练的三个挑战以及通过四类优化技术解决这些挑战的方法：GNN 数据分区、GNN 批量生成、GNN 执行模式和 GNN 通信协议。最后，本文总结了现有的多 GPU、GPU 集群和 CPU 集群的分布式 GNN 系统，并就可伸缩 GNNs 的未来方向进行了讨论。