带有数据分层的图神经网络训练

图谱神经网络（GNNs）在从图结构数据中学习方面已经显示出成功，并应用于欺诈检测、推荐和知识图谱推理。然而，有效地训练GNN是一个挑战，因为。1）GPU内存容量有限，对于大型数据集来说可能是不够的；2）基于图形的数据结构导致不规则的数据访问模式。在这项工作中，我们提供了一种方法来统计分析和识别在GNN训练前更频繁访问的数据。我们的数据分层方法不仅利用了输入图的结构，而且还利用了从实际的GNN训练过程中获得的洞察力来实现更高的预测结果。通过我们的数据分层方法，我们还提供了一个新的数据放置和访问策略，以进一步减少CPU-GPU的通信开销。我们还考虑到了多GPU的GNN训练，并在多GPU系统中证明了我们的策略的有效性。评估结果显示，我们的工作将CPU-GPU的流量减少了87-95%，在具有数亿个节点和数十亿条边的图上，GNN的训练速度比现有的解决方案提高了1.6-2.1倍。

原文题目：Graph Neural Network Training with Data Tiering

原文：Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However, training GNN efficiently is challenging because: 1) GPU memory capacity is limited and can be insufficient for large datasets, and 2) the graph-based data structure causes irregular data access patterns. In this work, we provide a method to statistical analyze and identify more frequently accessed data ahead of GNN training. Our data tiering method not only utilizes the structure of input graph, but also an insight gained from actual GNN training process to achieve a higher prediction result. With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead. We also take into account of multi-GPU GNN training as well and we demonstrate the effectiveness of our strategy in a multi-GPU system. The evaluation results show that our work reduces CPU-GPU traffic by 87-95% and improves the training speed of GNN over the existing solutions by 1.6-2.1x on graphs with hundreds of millions of nodes and billions of edges.