多平面HyperX网络降低延迟并提升成本效益
Multi-Plane HyperX: A Low-Latency and Cost-Effective Network for Large-Scale AI and HPC Systems
- 将多平面技术引入HyperX拓扑，实现多路径通信
- 相比现有架构，网络直径更小，成本更低
- 适合大规模AI与高性能计算系统部署
多平面架构在AI数据中心的胖树网络中日益普及。通过利用单个网卡的多个端口或同一扩展域内多个网卡，每个端口或网卡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，此前尚无研究探讨多平面技术在直接网络（如HyperX）中的应用。本文研究了多平面HyperX网络，并证明其相比最先进的胖树、Dragonfly及Dragonfly+等拓扑，具有显著更小的网络直径和更高的成本效益。
原文摘要 · Abstract (English)
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.
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