Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination

[2608.15118] Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2608.15118 (cs) [Submitted on 15 Aug 2026] Title: Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination Authors: Xuebin Song (1, 2), Menghao Zhang (1, 2), Yuezheng Liu (1), Jinyi Xia (1), Shucan Yang (1), Xiaohe Hu (3), Chunming Hu (1), Mingwei Xu (2) ((1) School of Software, Beihang University, Beijing, China, (2) State Key Laboratory of Internet Architecture, Tsinghua University, Beijing, China, (3) Infrawaves, Beijing, China) View a PDF of the paper titled Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination, by Xuebin Song (1 and 19 other authors View PDF HTML (experimental) Abstract: Distributed large language model (LLM) systems increasingly rely on collective communication primitives such as AllReduce (AR), ReduceScatter (RS), AllGather (AG), and AlltoAll (A2A). In modern LLM training and serving clusters, heterogeneous GPU interconnects, multi-NIC networking, mixed parallelism strategies, low-latency inference requests, and high-throughput training pipelines have motivated increasingly diverse ways to plan, execute, and overlap collective communication. This paper presents a tutorial-style, collective-centric taxonomy for collective communication. We organize recent advances into three layers: communication planning, which generates topology-aware collective schedules; communication execution and adaptation, which maps these schedules onto GPU runtimes and hardware in real clusters; and computation-communication coordination, which turns collective optimization into end-to-end training and inference benefits. We further discuss open challenges and future opportunities for collective communication in distributed LLM systems. Subjects: Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2608.15118 [cs.DC] (or arXiv:2608.15118v1 [cs.DC] for this version) https://doi.org/10.48550/arXiv.2608.15118 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Menghao Zhang [ view email ] [v1] Sat, 15 Aug 2026 08:40:01 UTC (485 KB) Full-text links: Access Paper: View a PDF of the paper titled Collective Communication for Distributed LLM Systems: Planning, Runtime Adaptation, and Computation Coordination, by Xuebin Song (1 and 19 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.DC < prev | next > new | recent | 2026-08 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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