Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evaluation and analysis upon existing abstractions, thus no clear consensus on which approach is better. In this letter, we classify existing programming abstractions for GNN Aggregation by the dimension of data organization and propagation method. By constructing these abstractions on a state-of-the-art GNN library, we perform a thorough and detailed characterization study to compare their performance and efficiency, and provide several insights on future GNN acceleration based on our analysis.

翻译：图神经网络（GNN）凭借从图数据中提取有效表示的强大能力，已获得广泛关注。随着对高效GNN计算需求的增长，为优化GNN聚合而设计的多种编程抽象应运而生，以促进加速进程。然而，现有抽象方法缺乏全面评估与分析，因此尚未明确哪种方法更为优越。本文按数据组织维度和传播方法对现有GNN聚合编程抽象进行分类。通过将上述抽象方法部署于最新GNN库，我们开展了详尽的特征研究以对比其性能与效率，并基于分析结果为未来GNN加速提供了若干见解。