  Recently, graph neural networks (GNNs), as the backbone of graph-based
machine learning, demonstrate great success in various domains (e.g.,
e-commerce). However, the performance of GNNs is usually unsatisfactory due to
the highly sparse and irregular graph-based operations. To this end, we propose
TC-GNN, the first GNN acceleration framework based on GPU Tensor Core Units
(TCUs). The core idea is to reconcile the "Sparse" GNN computation with the
high-performance "Dense" TCUs. Specifically, we conduct an in-depth analysis of
the sparse operations in mainstream GNN computing frameworks. We introduce a
novel sparse graph translation technique to facilitate TCU processing of the
sparse GNN workload. We implement an effective CUDA core and TCU collaboration
design to fully utilize GPU resources. We integrate TC-GNN with the PyTorch
framework for high programmability. Rigorous experiments show an average of
1.70X speedup over the state-of-the-art DGL framework across various models and
datasets.
