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, thereby provisioning the overall system with multiple network planes. However, no prior literature has explored the application of multi-plane technologies to direct networks such as HyperX. This paper investigates the multi-plane HyperX network and demonstrates that, compared to state-of-the-art network topologies like multi-plane Fat-Tree, Dragonfly, and Dragonfly+, the multi-plane HyperX architecture achieves a significantly smaller network diameter and superior cost-effectiveness.

翻译：多平面架构在AI数据中心的Fat-Tree网络中日益普及。通过利用单个网络接口卡上的多端口或纵向扩展域内的多个NIC，每个端口或NIC被分配至独立的网络平面，从而为整个系统提供多个网络平面。然而，现有文献尚未探索多平面技术在HyperX等直接网络中的应用。本文研究多平面HyperX网络，并证明：与多平面Fat-Tree、Dragonfly及Dragonfly+等前沿网络拓扑相比，多平面HyperX架构实现了显著更小的网络直径与更优的成本效益。