  Graph convolutional neural networks (GCNs) have achieved state-of-the-art
performance on graph-structured data analysis. Like traditional neural
networks, training and inference of GCNs are accelerated with GPUs. Therefore,
characterizing and understanding the execution pattern of GCNs on GPU is
important for both software and hardware optimization. Unfortunately, to the
best of our knowledge, there is no detailed characterization effort of GCN
workloads on GPU. In this paper, we characterize GCN workloads at inference
stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and
exploration, we propose several useful guidelines for both software
optimization and hardware optimization for the efficient execution of GCNs on
GPU.
