Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

Graph neural networks (GNNs) have emerged due to their success at modeling graph data.
Yet, it is challenging for GNNs to efficiently scale to large graphs. Thus, distributed GNNs …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

GNN's training needs to resolve issues of vertex dependencies, ie, each vertex
representation's update depends on its neighbors. Existing distributed GNN systems adopt …

We study training of Graph Neural Networks (GNNs) for large-scale graphs. We revisit the
premise of using distributed training for billion-scale graphs and show that for graphs that fit …

Recently Graph Neural Networks (GNNs) have achieved great success in many
applications. The mini-batch training has become the de-facto way to train GNNs on giant …

Graph neural networks (GNNs) are prevalent to deal with graph-structured datasets,
encoding graph data into low dimensional vectors. In this paper, we present a fast training …

Advances in graph neural network (GNN)-based algorithms enable machine learning on
relational data. GNNs are computationally demanding since they rely upon backpropagation …

Graph neural network (GNN) is a promising emerging application for link prediction,
recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM …

As deep learning (DL) inference has been widely adopted for building user-facing
applications in many domains, it is increasingly important for DL inference servers to …