打破双工壁垒：面向大语言模型训练的通道粒度 OCS 调度

Computer Science > Networking and Internet Architecture arXiv:2609.14253 (cs) [Submitted on 13 Sep 2026] Title:Breaking the Duplex Barrier: Lane-Granularity OCS Scheduling for LLM Training Authors:Bangbo Liang, Yupeng Chen, Sicheng Zhao, Peihao Huang, Di Yang, Bohua Xu, Bin Yang, Shizhen Zhao, Guo Chen View a PDF of the paper titled Breaking the Duplex Barrier: Lane-Granularity OCS Scheduling for LLM Training, by Bangbo Liang and 8 other authors View PDF HTML (experimental) Abstract:Optical circuit switch (OCS) can reconfigure physical connectivity to match the predictable communication schedules of large language model (LLM) training. Although each OCS light path is physically simplex, existing demand-aware OCS schedulers allocate capacity in duplex-port pairs, forcing equal bandwidth in both directions and stranding capacity under asymmetric node-pair traffic. This paper present LACE, the first offline OCS schedule compiler that independently allocates transmit (TX) and receive (RX) lanes for LLM training. Without changing the selected collective algorithms, operation order, or rank placement, LACE reconstructs directed node-level demand, jointly determines which consecutive operations share a configuration and how many simplex circuits serve each direction, and realizes these allocations as physical lane bindings and optical paths under per-node lane-inventory and multi-OCS fabric constraints. Software acknowledgments carry feedback over independently provisioned return paths, while coordinated link configuration and recovery verify each configuration before communication resumes. On a separate three-server testbed using fixed topologies and matched per-port rate limits, LACE's asymmetric connectivity achieves $1.80\times$ speedup for communication replay and $1.27\times$ for GPT-2 training over a symmetric-topology baseline. At larger scale, simulations of LLaMA-3.1 70B and 405B schedules with sixteen 400-Gb/s ports per server show that LACE achieves $1.21$--$2.04\times$ communication speedup over the latest duplex OCS scheduler. Comments: Subjects: Networking and Internet Architecture (cs.NI) Cite as: arXiv:2609.14253 [cs.NI] (or arXiv:2609.14253v1 [cs.NI] for this version) https://doi.org/10.48550/arXiv.2609.14253 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Bangbo Liang [view email] [v1] Sun, 13 Sep 2026 03:08:45 UTC (1,412 KB) Full-text links: Access Paper: View a PDF of the paper titled Breaking the Duplex Barrier: Lane-Granularity OCS Scheduling for LLM Training, by Bangbo Liang and 8 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.NI new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)