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.

翻译：图神经网络（GNN）因其在关键图相关任务中的卓越性能而日益普及。尽管量化被广泛用于加速GNN计算，但量化训练面临前所未有的挑战。当前量化GNN训练系统的训练时间往往长于其全精度对应系统，原因有二：（i）解决精度挑战导致过高开销，（ii）量化带来的优化潜力未被充分利用。本文提出Tango，通过三项贡献重新思考GPU上图神经网络训练中的量化挑战与机遇：首先，我们引入高效规则以在量化GNN训练中保持精度；其次，我们设计并实现面向量化的原语及原语间优化，可加速GNN训练；最后，我们将Tango与流行的Deep Graph Library（DGL）系统集成，并在多种GNN模型和数据集上展示其相较于现有最优方法的优越性能。