# Characterizing and Understanding HGNNs on GPUs

arXiv: https://arxiv.org/abs/2208.04758 · HTML: https://mlsanity.com/paper/2208.04758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Survey on Distributed Training of Graph Neural Networks](https://mlsanity.com/paper/2211.05368/) (2022)
- [Characterizing and Understanding HGNN Training on GPUs](https://mlsanity.com/paper/2407.11790/) (2024)
- [SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration](https://mlsanity.com/paper/2408.15089/) (2024)

## Abstract (arXiv)

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.