论文标题
在GPU上表征和理解HGNN
Characterizing and Understanding HGNNs on GPUs
论文作者
论文摘要
异质图神经网络（HGNN）在异质图表示学习方面具有强大的能力。 GPU通常会加速HGNN的执行。因此，表征和了解GPU上HGNN的执行模式对于软件和硬件优化都很重要。不幸的是，HGNN工作负载在GPU上没有详细的表征工作。在本文中，我们在推理阶段表征了HGNN工作负载，并探索了HGNN在GPU上的执行，以披露HGNN的执行语义和执行模式。鉴于表征和探索，我们为在GPU上有效执行HGNN的软件和硬件优化提供了一些有用的指南。
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.
