AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels
  on GPUs
Yangjie Zhou Yaoxu Song Jingwen Leng Zihan Liu Weihao Cui Zhendong Zhang Cong Guo Quan Chen Li Li Minyi Guo
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.