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arXiv · 2510.12064

GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network

Jun Dai·Xiaorun Wang·Kexiong Fang·Zheng Yang·Yuefeng Ji·Jiawei Zhang
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Abstract

The proliferation of Large Language Models (LLMs) with exponentially growing parameters is making cross-data center (DC) training an inevitable trend. However, viable strategies for extending single-DC training frameworks to multi-DC environments remain underdeveloped. We experimentally demonstrate, for the first time, a high-performance geo-distributed LLMs training framework across multiple DCs interconnected by a lossless, remote direct memory access (RDMA) enabled Datacenter Optical Transport Network (DC-OTN). An enhanced pipeline parallelism scheme is implemented within the Ascend full-stack environment of Huawei, which effectively eliminates the impact of cross-DC communication overhead on training efficiency. The overlapped computation and cross-DC communication is achieved with constraint cross-DC bandwidth and High Bandwidth Memory (HBM), reducing computation bubble ratio by up to 78.91%.

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Jun Dai, Xiaorun Wang, Kexiong Fang, Zheng Yang, Yuefeng Ji, Jiawei Zhang. 2025-10-14. GeoPipe: a Geo-distributed LLM Training Framework with enhanced Pipeline Parallelism in a Lossless RDMA-enabled Datacenter Optical Transport Network. https://doi.org/10.1109/acp66871.2025.11350566

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