SiHGNN：利用语义图特性实现高效的 HGNN 加速
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
AI总结：
针对异构图神经网络（HGNNs）在GPU上执行效率低的问题，提出轻量级硬件加速器前端SiHGNN，通过利用语义图特性优化生成与布局，使现有HGNN加速器平均性能提升2.95倍。
摘要：
异构图神经网络（HGNNs）已将图表示学习扩展到异构图领域。最近的研究证明了它们在医疗分析和推荐系统等各种应用中表现出卓越的性能，通常超越了现有方法。然而，由于其独特且复杂的执行模式，GPU 在执行 HGNNs 时经常面临效率低下的问题。与传统图神经网络相比，这些模式进一步加剧了内存访问的不规则性。为了应对这些挑战，最近的研究集中在为 HGNNs 开发领域专用加速器。尽管如此，这些努力大多集中在优化数据通路或调度数据访问，而在很大程度上忽略了利用语义图的固有特性（如拓扑、布局和生成）所能带来的潜在收益。在这项工作中，我们专注于利用语义图的特性来提升 HGNN 性能。首先，我们分析了语义图构建（SGB）阶段，并识别出语义图生成过程中显著的数据重用机会。接着，我们揭示了图特征处理（GFP）阶段的缓冲区颠簸现象，暴露出语义图布局中的潜在优化机会。此外，我们提出了一种名为 SiHGNN 的轻量级 HGNN 硬件加速器前端。该加速器前端集成了一个基于树的语义图构建器以高效生成语义图，并采用了一种新颖的图重构器来优化语义图布局。实验结果表明，SiHGNN 使最先进的 HGNN 加速器平均实现了 2.95 倍的性能提升。
英文摘要：
Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including medical analysis and recommendation systems, often surpassing existing methods. However, GPUs often experience inefficiencies when executing HGNNs due to their unique and complex execution patterns. Compared to traditional Graph Neural Networks, these patterns further exacerbate irregularities in memory access. To tackle these challenges, recent studies have focused on developing domain-specific accelerators for HGNNs. Nonetheless, most of these efforts have concentrated on optimizing the datapath or scheduling data accesses, while largely overlooking the potential benefits that could be gained from leveraging the inherent properties of the semantic graph, such as its topology, layout, and generation. In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance. First, we analyze the Semantic Graph Build (SGB) stage and identify significant opportunities for data reuse during semantic graph generation. Next, we uncover the phenomenon of buffer thrashing during the Graph Feature Processing (GFP) stage, revealing potential optimization opportunities in semantic graph layout. Furthermore, we propose a lightweight hardware accelerator frontend for HGNNs, called SiHGNN. This accelerator frontend incorporates a tree-based Semantic Graph Builder for efficient semantic graph generation and features a novel Graph Restructurer for optimizing semantic graph layouts. Experimental results show that SiHGNN enables the state-of-the-art HGNN accelerator to achieve an average performance improvement of 2.95$\times$.
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