Accelerating GNN training with locality-aware partial execution
Taehyun Kim, Chang Ho Hwang, KyoungSoo Park, Zhiqi Lin, Peng Cheng, Youshan Miao, Lingxiao Ma, Yongqiang Xiong · 2021
Graph Neural Networks (GNNs) are increasingly popular for various prediction and recommendation tasks. Unfortunately, the graph datasets for practical GNN applications are often too large to fit into the memory of a single GPU, leading to frequent data loading from host memory to GPU. This data transfer overhead is highly detrimental to the performance, severely limiting the training throughput.