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：图神经网络因其在关键图相关任务中的卓越性能而日益流行。尽管量化被广泛用于加速图神经网络计算，但量化训练面临前所未有的挑战。当前的量化图神经网络训练系统通常比全精度对等系统需要更长的训练时间，原因有二：（i）解决精度挑战导致额外开销过高，（ii）量化所暴露的优化潜力未被充分挖掘。本文提出Tango，重新思考GPU上图神经网络训练中的量化挑战与机遇，包含三项贡献：首先，我们引入高效规则以在量化GNN训练中保持精度；其次，我们设计并实现量化感知原语及原语间优化，可加速GNN训练；最后，我们将Tango与流行的Deep Graph Library系统集成，并在多种GNN模型和数据集上展示了其相对于现有最优方法的卓越性能。