GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelism

arXiv:2303.13775v2 Announce Type: replace-cross
Abstract: Graph neural networks (GNNs), an emerging class of machine learning models for graphs, have gained popularity for their superior performance in various graph analytical tasks. Mini-batch training is commonly used to train GNNs on large graphs, and data parallelism is the standard approach to scale mini-batch training across multiple GPUs. One of the major performance costs in GNN training is the loading of input features, which prevents GPUs from being fully utilized. In this paper, …

abstract arxiv class cs.dc cs.lg data gnns graph graph neural network graph neural networks graphs machine machine learning machine learning models network networks network training neural network neural networks performance replace scaling split standard tasks train training type via