  Graph neural networks (GNNs) are powerful tools for exploring and learning
from graph structures and features. As such, achieving high-performance
execution for GNNs becomes crucially important. Prior works have proposed to
explore the sparsity (i.e., low density) in the input graph to accelerate GNNs,
which uses the full-graph-level or block-level sparsity format. We show that
they fail to balance the sparsity benefit and kernel execution efficiency. In
this paper, we propose a novel system, referred to as AdaptGear, that addresses
the challenge of optimizing GNNs performance by leveraging kernels tailored to
the density characteristics at the subgraph level. Meanwhile, we also propose a
method that dynamically chooses the optimal set of kernels for a given input
graph. Our evaluation shows that AdaptGear can achieve a significant
performance improvement, up to $6.49 \times$ ($1.87 \times$ on average), over
the state-of-the-art works on two mainstream NVIDIA GPUs across various
datasets.
