Characterizing and Understanding HGNNs on GPUs

Mingyu Yan; Mo Zou; Xiaocheng Yang; Wenming Li; Xiaochun Ye; Dongrui Fan; Yuan Xie

Characterizing and Understanding HGNNs on GPUs

Hardware Architecture 2022-08-10 v1 Distributed, Parallel, and Cluster Computing

Authors: Mingyu Yan , Mo Zou , Xiaocheng Yang , Wenming Li , Xiaochun Ye , Dongrui Fan , Yuan Xie

Abstract

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.

Keywords

heterogeneous graph learning graph neural network gpu computing

Cite

@article{arxiv.2208.04758,
  title  = {Characterizing and Understanding HGNNs on GPUs},
  author = {Mingyu Yan and Mo Zou and Xiaocheng Yang and Wenming Li and Xiaochun Ye and Dongrui Fan and Yuan Xie},
  journal= {arXiv preprint arXiv:2208.04758},
  year   = {2022}
}

Comments

To Appear in IEEE Computer Architecture Letters

Characterizing and Understanding HGNNs on GPUs

Abstract

Keywords

Cite

Comments

Related papers