An Extensible Software Transport Layer for GPU Networking

Yang Zhou; Zhongjie Chen; Ziming Mao; ChonLam Lao; Shuo Yang; Pravein Govindan Kannan; Jiaqi Gao; Yilong Zhao; Yongji Wu; Kaichao You; Fengyuan Ren; Zhiying Xu; Costin Raiciu; Ion Stoica

arXiv · 2504.17307

An Extensible Software Transport Layer for GPU Networking

Yang Zhou·Zhongjie Chen·Ziming Mao·ChonLam Lao·Shuo Yang·Pravein Govindan Kannan·Jiaqi Gao·Yilong Zhao·Yongji Wu·Kaichao You·Fengyuan Ren·Zhiying Xu·Costin Raiciu·Ion Stoica

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Abstract

Fast-evolving machine learning (ML) workloads have increasing requirements for networking. However, host network transport on RDMA NICs is hard to evolve, causing problems for ML workloads. For example, single-path RDMA traffic is prone to flow collisions that severely degrade collective communication performance. We present UCCL, an extensible software transport layer to evolve GPU networking. UCCL decouples the data path and control path of existing RDMA NICs and efficiently runs the control-path transport on host CPUs. This software extensibility brings in transport innovations that cannot be achieved in hardware for ML workloads, e.g., a multipath transport to resolve flow collisions. ML collectives atop UCCL achieve up to 4.5x higher performance compared to existing RDMA NICs.

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BibTeX RIS

Yang Zhou, Zhongjie Chen, Ziming Mao, ChonLam Lao, Shuo Yang, Pravein Govindan Kannan, Jiaqi Gao, Yilong Zhao, Yongji Wu, Kaichao You, Fengyuan Ren, Zhiying Xu, Costin Raiciu, Ion Stoica. 2025-04-24. An Extensible Software Transport Layer for GPU Networking. https://arxiv.org/abs/2504.17307

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