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arXiv · 2605.26960

Extreme-Scale Interconnection Networks

Alejandro Cano·Cristina Brinza·Cristóbal Camarero·Carmen Martínez·Ramón Beivide
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Abstract

Extreme-scale data centers are the backbone of next-generation computing, enabling breakthroughs in science, artificial intelligence, and global innovation through unprecedented processing power and scalability. This work examines leaf-spine network topologies that offer extreme scalability--connecting a vast number of endpoints--while delivering strong performance at low cost. It takes as a starting point two alternatives to the widely used Fat-Tree topology: the Orthogonal Fat-Tree and the Random Folded Clos. The resulting Multipass Random Leaf-Spine (MRLS) networks inherit their advantages and surpass Fat-Trees in both throughput and flexibility. To fully leverage the topological properties of these networks, various non-minimal routing strategies are considered. An exhaustive evaluation using an interconnection network simulator provides insight into the trade-offs and scalability of these topologies under realistic conditions, positioning them as a promising solution for extreme-scale systems. The MRLS achieves a 50% speedup against a Fat-Tree for an All2All collective comprising 100k endpoints, and 100% against Dragonfly networks for the same collective.

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Alejandro Cano, Cristina Brinza, Cristóbal Camarero, Carmen Martínez, Ramón Beivide. 2026-05-26. Extreme-Scale Interconnection Networks. https://arxiv.org/abs/2605.26960

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