Full-graph training on graph neural networks (GNN) has emerged as a promising training method for its effectiveness. Full-graph training requires extensive memory and computation resources. To accelerate this training process, researchers have proposed employing multi-GPU processing. However the scalability of existing frameworks is limited as they necessitate maintaining the training data for every layer in GPU memory. To efficiently train on large graphs, we present HongTu, a scalable full-graph GNN training system running on GPU-accelerated platforms. HongTu stores vertex data in CPU memory and offloads training to GPUs. HongTu employs a memory-efficient full-graph training framework that reduces runtime memory consumption by using partition-based training and recomputation-caching-hybrid intermediate data management. To address the issue of increased host-GPU communication caused by duplicated neighbor access among partitions, HongTu employs a deduplicated communication framework that converts the redundant host-GPU communication to efficient inter/intra-GPU data access. Further, HongTu uses a cost model-guided graph reorganization method to minimize communication overhead. Experimental results on a 4XA100 GPU server show that HongTu effectively supports billion-scale full-graph GNN training while reducing host-GPU data communication by 25%-71%. Compared to the full-graph GNN system DistGNN running on 16 CPU nodes, HongTu achieves speedups ranging from 7.8X to 20.2X. For small graphs where the training data fits into the GPUs, HongTu achieves performance comparable to existing GPU-based GNN systems.

翻译：图神经网络（GNN）的全图训练因其有效性已成为一种有前景的训练方法。全图训练需要大量的内存和计算资源。为加速这一训练过程，研究人员提出了采用多GPU处理方案。然而，现有框架的可扩展性受限，因为它们需要在GPU内存中维护每一层的训练数据。为高效训练大规模图，我们提出HongTu——一种在GPU加速平台上运行的可扩展全图GNN训练系统。HongTu将顶点数据存储于CPU内存，并将训练任务卸载至GPU。该系统采用内存高效的全图训练框架，通过基于分区的训练和重计算-缓存混合的中间数据管理策略降低运行时内存消耗。针对分区间的重复邻居访问导致的主机-GPU通信增加问题，HongTu采用去重通信框架，将冗余的主机-GPU通信转换为高效的GPU内/间数据访问。此外，HongTu使用成本模型指导的图重组方法以最小化通信开销。在4×A100 GPU服务器上的实验结果表明，HongTu能够有效支持十亿级全图GNN训练，同时将主机-GPU数据通信量降低25%-71%。与在16个CPU节点上运行的全图GNN系统DistGNN相比，HongTu实现了7.8倍至20.2倍的加速比。对于训练数据可适配GPU的小规模图，HongTu的性能与现有基于GPU的GNN系统相当。