A Highly Scalable LLM Clusters with Optical Interconnect

Abstract: The rapid development of large-scale GPU clusters for LLM training has driven enterprises to replace core-layer electrical switches with optical circuit switches (OCS) to meet escalating bandwidth demands. However, current physical topology design of OCS-based clusters faces two critical challenges. First, there exist unrealizable logical topologies, leading to underutilization of bandwidth resource. Second, calculating OCS reconfiguration constitutes an NP-Complete problem and is time-consuming for multi-tenant GPU clusters which need real-time scheduling. In this paper, we propose \emph{Cross Wiring}, a new physical topology design that resolves both limitations. Our physical topology guarantees full compatibility with all logical topologies under L2-compatibility constraints. Through a proposed \emph{Symmetric Integer Matrix Decomposition Theorem}, we design a polynomial-time OCS reconfiguration algorithm that satisfies arbitrary logical topology requirements. Evaluations show a up to 39.5\% higher training throughput versus prior architectures such as \emph{Gemini} in 128-NPU testbed and a 12.6\% reduction in average job completion time through real-workload based multi-tenant large-scale simulations.