论文标题
在GPU上表征和理解GCN
Characterizing and Understanding GCNs on GPU
论文作者
论文摘要
图卷积神经网络（GCN）在图形结构化数据分析方面已实现了最先进的性能。像传统的神经网络一样，GCN的培训和推断也随着GPU的形式加速。因此，表征和了解GCN在GPU上的执行模式对于软件和硬件优化都很重要。不幸的是，据我们所知，GCN工作量在GPU上没有详细的表征。在本文中，我们表征了推理阶段的GCN工作负载，并在NVIDIA V100 GPU上探索GCN模型。鉴于表征和探索，我们为软件优化和硬件优化提供了几个有用的指南，以有效地执行GCN在GPU上。
Graph convolutional neural networks (GCNs) have achieved state-of-the-art performance on graph-structured data analysis. Like traditional neural networks, training and inference of GCNs are accelerated with GPUs. Therefore, characterizing and understanding the execution pattern of GCNs on GPU is important for both software and hardware optimization. Unfortunately, to the best of our knowledge, there is no detailed characterization effort of GCN workloads on GPU. In this paper, we characterize GCN workloads at inference stage and explore GCN models on NVIDIA V100 GPU. Given the characterization and exploration, we propose several useful guidelines for both software optimization and hardware optimization for the efficient execution of GCNs on GPU.
