Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis. (arXiv:2205.09702v1 [cs.LG])

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, graph classification, or link prediction, with high
accuracy. However, both inference and training of GNNs are complex, and they
uniquely combine the features of irregular graph processing with dense and
regular computations. This complexity makes it very challenging to execute GNNs
efficiently on modern massively parallel architectures. To alleviate this, we
first design a taxonomy …

analysis arxiv concurrency distributed graph graph neural networks networks neural networks