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arXiv · 2305.17408

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
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Abstract

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.

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Yangjie Zhou, Yaoxu Song, Jingwen Leng, Zihan Liu, Weihao Cui, Zhendong Zhang, Cong Guo, Quan Chen, Li Li, Minyi Guo. 2023-05-27. AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs. https://arxiv.org/abs/2305.17408

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