  Many interesting datasets ubiquitous in machine learning and deep learning
can be described via graphs. As the scale and complexity of graph-structured
datasets increase, such as in expansive social networks, protein folding,
chemical interaction networks, and material phase transitions, improving the
efficiency of the machine learning techniques applied to these is crucial. In
this study, we focus on Graph Neural Networks (GNN) that have found great
success in tasks such as node or edge classification and link prediction.
However, standard GNN models have scaling limits due to necessary recursive
calculations performed through dense graph relationships that lead to memory
and runtime bottlenecks. While new approaches for processing larger networks
are needed to advance graph techniques, and several have been proposed, we
study how GNNs could be parallelized using existing tools and frameworks that
are known to be successful in the deep learning community. In particular, we
investigate applying pipeline parallelism to GNN models with GPipe, introduced
by Google in 2018.
