  Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in
heterogeneous graph representation learning. The execution of HGNNs is usually
accelerated by GPUs. Therefore, characterizing and understanding the execution
pattern of HGNNs on GPUs is important for both software and hardware
optimizations. Unfortunately, there is no detailed characterization effort of
HGNN workloads on GPUs. In this paper, we characterize HGNN workloads at
inference phase and explore the execution of HGNNs on GPU, to disclose the
execution semantic and execution pattern of HGNNs. Given the characterization
and exploration, we propose several useful guidelines for both software and
hardware optimizations for the efficient execution of HGNNs on GPUs.
