  Graph Neural Networks (GNNs) is a promising approach for applications with
nonEuclidean data. However, training GNNs on large scale graphs with hundreds
of millions nodes is both resource and time consuming. Different from DNNs,
GNNs usually have larger memory footprints, and thus the GPU memory capacity
and PCIe bandwidth are the main resource bottlenecks in GNN training. To
address this problem, we present BiFeat: a graph feature quantization
methodology to accelerate GNN training by significantly reducing the memory
footprint and PCIe bandwidth requirement so that GNNs can take full advantage
of GPU computing capabilities. Our key insight is that unlike DNN, GNN is less
prone to the information loss of input features caused by quantization. We
identify the main accuracy impact factors in graph feature quantization and
theoretically prove that BiFeat training converges to a network where the loss
is within $\epsilon$ of the optimal loss of uncompressed network. We perform
extensive evaluation of BiFeat using several popular GNN models and datasets,
including GraphSAGE on MAG240M, the largest public graph dataset. The results
demonstrate that BiFeat achieves a compression ratio of more than 30 and
improves GNN training speed by 200%-320% with marginal accuracy loss. In
particular, BiFeat achieves a record by training GraphSAGE on MAG240M within
one hour using only four GPUs.
