Heterogeneous graph neural networks (HGNNs) have emerged as powerful algorithms for processing heterogeneous graphs (HetGs), widely used in many critical fields. To capture both structural and semantic information in HetGs, HGNNs first aggregate the neighboring feature vectors for each vertex in each semantic graph and then fuse the aggregated results across all semantic graphs for each vertex. Unfortunately, existing graph neural network accelerators are ill-suited to accelerate HGNNs. This is because they fail to efficiently tackle the specific execution patterns and exploit the high-degree parallelism as well as data reusability inside and across the processing of semantic graphs in HGNNs. In this work, we first quantitatively characterize a set of representative HGNN models on GPU to disclose the execution bound of each stage, inter-semantic-graph parallelism, and inter-semantic-graph data reusability in HGNNs. Guided by our findings, we propose a high-performance HGNN accelerator, HiHGNN, to alleviate the execution bound and exploit the newfound parallelism and data reusability in HGNNs. Specifically, we first propose a bound-aware stage-fusion methodology that tailors to HGNN acceleration, to fuse and pipeline the execution stages being aware of their execution bounds. Second, we design an independency-aware parallel execution design to exploit the inter-semantic-graph parallelism. Finally, we present a similarity-aware execution scheduling to exploit the inter-semantic-graph data reusability. Compared to the state-of-the-art software framework running on NVIDIA GPU T4 and GPU A100, HiHGNN respectively achieves an average 41.5$\times$ and 8.6$\times$ speedup as well as 106$\times$ and 73$\times$ energy efficiency with quarter the memory bandwidth of GPU A100.

翻译：异构图神经网络（HGNNs）已成为处理异构图的强大算法，广泛应用于多个关键领域。为捕获异构图中的结构信息与语义信息，HGNNs首先对每个语义图中各顶点的邻居特征向量进行聚合，再将所有语义图的聚合结果按顶点进行融合。然而，现有图神经网络加速器难以高效加速HGNNs，原因在于其未能有效处理HGNNs特有的执行模式，也无法充分利用语义图处理内部及处理间的高度并行性与数据重用性。本研究首先在GPU上对一系列代表性HGNN模型进行量化特征分析，揭示了HGNNs中各阶段的执行瓶颈、语义图间并行性以及语义图间数据重用性。基于发现，我们提出高性能HGNN加速器HiHGNN，以缓解执行瓶颈并利用HGNNs中新发现的并行性与数据重用性。具体而言，我们首先提出一种面向HGNN加速的边界感知阶段融合方法，通过融合与流水线化执行阶段并兼顾其执行边界。其次，设计基于独立性的并行执行方案以利用语义图间并行性。最后，提出相似性感知的执行调度策略以利用语义图间数据重用性。与在NVIDIA GPU T4和GPU A100上运行的最先进软件框架相比，HiHGNN在仅使用GPU A100四分之一内存带宽的情况下，分别实现了平均41.5倍和8.6倍的加速比，以及106倍和73倍的能效提升。