Relational data present in real world graph representations demands for tools capable to study it accurately. In this regard Graph Neural Network (GNN) is a powerful tool, wherein various models for it have also been developed over the past decade. Recently, there has been a significant push towards creating accelerators that speed up the inference and training process of GNNs. These accelerators, however, do not delve into the impact of their dataflows on the overall data movement and, hence, on the communication requirements. In this paper, we formulate analytical models that capture the amount of data movement in the most recent GNN accelerator frameworks. Specifically, the proposed models capture the dataflows and hardware setup of these accelerator designs and expose their scalability characteristics for a set of hardware, GNN model and input graph parameters. Additionally, the proposed approach provides means for the comparative analysis of the vastly different GNN accelerators.

翻译：真实世界图示对能够准确研究该图的工具的需求中包含的关系数据。 在这方面,图形神经网络(GNN)是一个强有力的工具,过去十年间也为它开发了各种模型。最近,在创建加速器以加快GNNs的推论和培训过程方面出现了巨大的推动。然而,这些加速器并没有深入探究其数据流对整个数据流动的影响,因此也没有深入到通信要求。在本文中,我们制定了分析模型,在最新的GNN加速器框架中捕捉了数据流动量。具体地说,拟议的模型捕捉了这些加速器设计的数据流和硬件设置,暴露了一套硬件、GNNN模型和输入图参数的可缩放性。此外,拟议的方法提供了对大不相同的GNN加速器进行比较分析的手段。