DistTGL:基于分布式记忆的时间图神经网络训练

Hongkuan Zhou, Da Zheng, Xiang Song, G. Karypis, V. Prasanna
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引用次数: 0

摘要

基于记忆的时态图神经网络是动态图表示学习的强大工具,在许多实际应用中表现出优异的性能。然而,它们的节点内存倾向于较小的批处理大小,以捕获图事件中的更多依赖关系,并且需要在所有训练器之间同步维护。因此,现有框架在扩展到多个gpu时遭受精度损失。更糟糕的是,同步节点内存的巨大开销使得在GPU集群中部署解决方案变得不切实际。在这项工作中,我们提出了DistTGL——一种在分布式GPU集群上训练基于内存的tgnn的有效且可扩展的解决方案。DistTGL在现有解决方案上有三个改进:增强的TGNN模型,新的训练算法和优化的系统。在实验中,DistTGL实现了近线性收敛加速,准确率比单机方法高14.5%,训练吞吐量比单机方法高10.17倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smaller batch sizes to capture more dependencies in graph events and needs to be maintained synchronously across all trainers. As a result, existing frameworks suffer from accuracy loss when scaling to multiple GPUs. Even worse, the tremendous overhead of synchronizing the node memory makes it impractical to deploy the solution in GPU clusters. In this work, we propose DistTGL --- an efficient and scalable solution to train memory-based TGNNs on distributed GPU clusters. DistTGL has three improvements over existing solutions: an enhanced TGNN model, a novel training algorithm, and an optimized system. In experiments, DistTGL achieves near-linear convergence speedup, outperforming the state-of-the-art single-machine method by 14.5% in accuracy and 10.17× in training throughput.
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