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Recent studies have shown that Binary Graph Neural Networks (GNNs) arepromising for saving computations of GNNs through binarized tensors. Priorwork, however, mainly focused on algorithm designs or training techniques,leaving it open to how to materialize the performance potential on acceleratorhardware fully. This work redesigns the binary GNN inference backend from theefficiency perspective. It fills the gap by proposing a series of abstractionsand techniques to map binary GNNs and their computations best to fit the natureof bit manipulations on GPUs. Results on real-world graphs with GCNs,GraphSAGE, and GraphSAINT show that the proposed techniques outperformstate-of-the-art binary GNN implementations by 8-22X with the same accuracymaintained. BitGNN code is publicly available.

BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs