[Submitted on 29 Jul 2026]

Authors:Tiancheng Hu, Jin Qin, Yuzheng Wang, Ke Liu, TangShengsheng Li, Sheng Wang, Zhongzhe Hu, Tianlun Hu, Wei Wang, Lijun Li, Jingbin Zhou, Xiaoming Bao, Hongwei Sun, Jieru Zhao, Huimin Cui, Tao Xie, Chenxi Wang

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Abstract:Modern distributed AI workloads run across hundreds of accelerators, making communication a major bottleneck. Existing communication libraries remain largely buffer-centric because user and communication buffers are managed separately, causing redundant data copies or costly user-buffer registration. This paper presents StrataCL, a zero-redundancy and fabric-native communication library for production supernodes. StrataCL introduces registration-on-allocation to realize user-buffer direct communication, and designs communication operators with workload-balanced NPU-core partitioning and NPU-driven SDMA offloading to exploit supernode architecture features. On the Huawei CloudMatrix384, StrataCL improves collective bus bandwidth by up to 1.6x and improves MoE dispatch/combine bus bandwidth by up to 1.4x. Across three production workloads, StrataCL improves LLM inference throughput by 1.9x, reduces P99 TTFT by 2.2x, and reduces LLM and Recsys training iteration time by 1.4x and 1.3x, respectively.

Submission history

From: Tiancheng Hu [view email]
[v1] Wed, 29 Jul 2026 03:45:05 UTC (641 KB)