English(EN) On Efficient Scaling of GNNs via IO-Aware Layers Implementations

新的GPU内核通过优化内存访问提升GNN性能

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-29 16:22

研究人员开发了新的GPU内核，通过解决内存访问瓶颈来优化图神经网络（GNN）。这些内核旨在减少数据移动并提高三种主要GNN层族的局部性：基于SpMM的卷积、基于归约的聚合以及基于注意力的层。这些实现提供了显著的加速，其中一些注意力内核的性能提高了8.5倍，并大幅减少了内存占用。 AI

影响优化的内核可以加速GNN在各种AI应用中的研究和部署。

排序理由该集群包含一篇学术论文，详细介绍了用于提高GNN性能的新技术实现。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

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arXiv cs.AI TIER_1 English(EN) · Daria Fomina, Daniil Krasylnikov, Alexey Boykov, Andrey Dolgovyazov, Vyacheslav Zhdanovskiy, Fedor Velikonivtsev · 2026-06-01 04:00

关于通过 IO 感知层实现 GNN 的高效扩展

arXiv:2605.31500v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general message passing, but complex layers often materialize edge-wise intermediates, …
arXiv cs.AI TIER_1 English(EN) · Fedor Velikonivtsev · 2026-05-29 16:22

关于通过 IO 感知层实现 GNN 高效扩展

Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general message passing, but complex layers often materialize edge-wise intermediates, increasing memory traffic and limiting scalability…