Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for various applications is non-trivial. Previous training methodologies suffer from inferior adaptability and lack a unified training optimization solution. To address the problem, this work proposes GNNavigator, an adaptive GNN training configuration optimization framework. GNNavigator meets diverse GNN application requirements due to our unified software-hardware co-abstraction, proposed GNNs training performance model, and practical design space exploration solution. Experimental results show that GNNavigator can achieve up to 3.1x speedup and 44.9% peak memory reduction with comparable accuracy to state-of-the-art approaches.

翻译：图神经网络（GNNs）近年来在许多应用中取得了显著成功。然而，平衡GNNs训练的运行时间成本、内存消耗以及针对不同应用的可实现精度并非易事。以往的训练方法存在适应性差、缺乏统一训练优化方案的问题。为解决这一问题，本文提出GNNavigator——一种自适应GNN训练配置优化框架。通过我们提出的统一软硬件协同抽象、GNNs训练性能模型以及实际的设计空间探索方案，GNNavigator能够满足多样化的GNN应用需求。实验结果表明，与当前最先进方法相比，GNNavigator在保持相当精度的前提下，可实现高达3.1倍的加速比和44.9%的峰值内存减少。