Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern of HGNNs leads to the buffer thrashing issue in HGNN accelerators. In this work, we identify an opportunity to address buffer thrashing in HGNN acceleration through an analysis of the topology of heterogeneous graphs. To harvest this opportunity, we propose a graph restructuring method and map it into a hardware frontend named GDR-HGNN. GDR-HGNN dynamically restructures the graph on the fly to enhance data locality for HGNN accelerators. Experimental results demonstrate that, with the assistance of GDR-HGNN, a leading HGNN accelerator achieves an average speedup of 14.6 times and 1.78 times compared to the state-of-the-art software framework running on A100 GPU and itself, respectively.

翻译：异构图神经网络（HGNN）将图表示学习的应用范围拓展至异构图中。然而，HGNN的不规则内存访问模式会导致HGNN加速器中出现缓冲区抖动问题。本研究通过对异构图拓扑结构的分析，找到了解决HGNN加速中缓冲区抖动问题的契机。为利用这一契机，我们提出了一种图重构方法，并将其映射至名为GDR-HGNN的硬件前端。GDR-HGNN能够动态地即时重构图结构，从而提升HGNN加速器的数据局部性。实验结果表明，在GDR-HGNN的辅助下，与运行在A100 GPU上的最先进软件框架相比，一种领先的HGNN加速器平均实现了14.6倍的加速比；而与该加速器自身相比，平均加速比达到1.78倍。