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English(EN) Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems

Multi-Plane HyperX网络为AI和HPC系统提供更低延迟和成本

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出Multi-Plane HyperX网络架构，旨在提高大规模人工智能（AI）和高性能计算（HPC）系统的效率。该新架构将先前用于Fat-Tree设计的多平面网络概念扩展到HyperX等直接网络。研究表明，与现有的先进拓扑结构（如多平面Fat-Tree、Dragonfly和Dragonfly+）相比，Multi-Plane HyperX具有更小的网络直径和更高的成本效益。 AI

影响 提出了一种更具成本效益、低延迟的网络拓扑结构，适用于大规模AI训练基础设施。

排序理由 这是一篇介绍面向AI和HPC系统的新网络架构的研究论文。

在 arXiv cs.LG 阅读 →

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

Multi-Plane HyperX网络为AI和HPC系统提供更低延迟和成本

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ziyu Wang, Fei Lei, Dezun Dong ·

    Multi-Plane HyperX：面向大规模人工智能和高性能计算系统的低延迟、高性价比网络

    arXiv:2604.23519v1 Announce Type: cross Abstract: Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single network interface card (NIC) or multiple NICs within a scale-up domain, each port o…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Multi-Plane HyperX：面向大规模人工智能和高性能计算系统的低延迟、高性价比网络

    Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single network interface card (NIC) or multiple NICs within a scale-up domain, each port or NIC is allocated to an independent network plane…

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