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）因从图数据中提取有用表示的强大能力而受到广泛关注。随着对高效GNN计算需求的日益增长，为优化GNN聚合而设计的多种编程抽象方法应运而生，以促进加速。然而，现有抽象方法尚未得到全面评估与分析，因此对于哪种方法更优尚无明确共识。本文按数据组织与传播方法的维度，对现有GNN聚合编程抽象方法进行了分类。通过在最先进的GNN库上构建这些抽象方法，我们开展了一项详尽的特征化研究，以比较其性能与效率，并基于分析为未来GNN加速提供了若干见解。