Dragonfly-Ultra: A Scalable, Low-Cost Network Architecture for High-Performance AI Clusters
TL;DR
Dragonfly-Ultra is presented, a scalable, low-cost network architecture for high-performance AI clusters that can scale to over 260k GPUs with only 82% cost and 81% power consumption of a 3-layer Clos architecture and incorporates three key mechanisms to further improve network performance and optimize collective communication.
Abstract
Large-scale AI clusters impose higher requirements on network scalability, cost, and communication efficiency. The traditional Clos topology suffers from superlinear cost growth when scaling to over 100k GPUs, while the more cost-effective Dragonfly+ introduces "down-up" detours, deadlock risks, and complex routing design. This paper presents Dragonfly-Ultra, a scalable, low-cost network architecture for high-performance AI clusters. Dragonfly-Ultra can scale to over 260k GPUs with only 82% cost and 81% power consumption of a 3-layer Clos architecture. Dragonfly-Ultra optimizes inter-group connectivity to eliminate intra-group detours entirely. Beyond the topological benefits, Dragonfly-Ultra incorporates three key mechanisms to further improve network performance and optimize collective communication, including lightweight dual-waterline adaptive routing for fast congestion mitigation, virtual-link-based deadlock avoidance with lower hardware overhead, and uniform affinity-aware rank placement for balanced inter-group traffic across all phases. Simulation results on a 4k-node cluster show that, compared to Clos, Dragonfly-Ultra achieves up to 18.8% and 39.2% lower completion time for AllReduce and AlltoAll, respectively. Compared to Dragonfly+, the reductions are up to 27.9% and 62.1%, outperforming current mainstream topologies.