Before revision:  This distributed framework improves model performance and significantly decreases wall-clock training time . GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning .
After revision:  This distributed framework improves model performance and significantly decreases wall-clock training time , and enables the training of markedly overparameterized GCN models. Remarkably, with GIST, we train an astonishgly-wide 32,768-dimensional GraphSAGE model, which exceeds the capacity of a single GPU by a factor of 8X, to SOTA performance on the Amazon2M dataset .