Graph Neural Networks (GNNs) are becoming increasingly popular due to their
superior performance in critical graph-related tasks. While quantization is
widely used to accelerate GNN computation, quantized training faces
unprecedented challenges. Current quantized GNN training systems often have
longer training times than their full-precision counterparts for two reasons:
(i) addressing the accuracy challenge leads to excessive overhead, and (ii) the
optimization potential exposed by quantization is not adequately leveraged.
This paper introduces Tango which re-thinks quantization challenges and
opportunities for graph neural network training on GPUs with three
contributions: Firstly, we introduce efficient rules to maintain accuracy
during quantized GNN training. Secondly, we design and implement
quantization-aware primitives and inter-primitive optimizations that can speed
up GNN training. Finally, we integrate Tango with the popular Deep Graph
Library (DGL) system and demonstrate its superior performance over
state-of-the-art approaches on various GNN models and datasets