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.

本文提出AdaptGear系统，旨在通过在子图层面上利用与密度特征相匹配的核心来优化GNN的性能，同时提出一种方法动态选择给定输入图形的最佳核心集，评估表明AdaptGear可以在各种数据集上实现显着的性能改进，最高可达6.49倍（平均1.87倍）

通过GPU上的自适应子图级核加速GNN训练的AdaptGear