Accelerating GNN training with locality-aware partial execution

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. In this paper, we propose locality-aware, partial code execution that significantly cuts down the data copy overhead for GNN training. The key idea is to exploit the "near-data" processors for the first few operations in each iteration, which reduces the data size for DMA operations. In addition, we employ task scheduling tailored to GNN training and apply load balancing between CPU and GPU. We find that our approach substantially improves the performance, achieving up to 6.6x speedup in training throughput over the state-of-the-art system design.