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Graph Neural Networks (GNNs) are a class of neural networks designed toextract information from the graphical structure of data. Graph ConvolutionalNetworks (GCNs) are a widely used type of GNN for transductive graph learningproblems which apply convolution to learn information from graphs. GCN is achallenging algorithm from an architecture perspective due to inherentsparsity, low data reuse, and massive memory capacity requirements. Traditionalneural algorithms exploit the high compute capacity of GPUs to achieve highperformance for both inference and training. The architectural decision to usea GPU for GCN inference is a question explored in this work. GCN on both CPUand GPU was characterized in order to better understand the implications ofgraph size, embedding dimension, and sampling on performance.

Architectural Implications of Embedding Dimension during GCN on CPU and GPU