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Xronos：面向边缘 CPU 上协作 LLM 微调的异构感知张量并行
该论文针对 CPU 边缘设备（IoT 网关、智能家居中枢、车载计算机）的协作式 LLM 微调，指出主流方案依赖的流水线并行（PP）因同一 CPU 同时承担计算与通信而产生严重争抢，平均计算停顿比 GPU 设备高 5.75 倍；而现有张量并行（TP）假设设备同构，在异构 CPU 上会导致快节点在同步点空闲最高 34%。作者提出 Xronos，以 TP 为执行骨架，结合轻量 profiling 与异构感知张量划分缓解长尾瓶颈。在多种设备、模型与基准任务上，微调时间较 TP 减少 18%、较 PP 减少 56%，设备空闲时间比 SOTA 降低约 5.9 倍，同时保持精度。
AI推荐理由 arXiv 论文，问题定义清晰：指出 CPU 边缘设备上流水线并行（PP）因计算与通信争抢 CPU 导致平均 5.75 倍计算停顿，异构设备下快节点同步等待空闲最高达 34%；提出 Xronos 以张量并行（TP）为执行骨架，结合轻量 profiling 与异构感知张量划分。摘要给出可验证量化结果：微调时间较 TP 减少 18%、较 PP 减少 56%，设备空闲时间比 SOTA 降低约 5.9 倍且保持精度。局限是仅基于摘要，未提供数据集、设备型号与复现细节，效果为作者自报。
78
正文
Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2609.19909 (cs)
[Submitted on 17 Sep 2026]
Title:Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs
Authors:Wonmi Choi, Sunjae Park, Dohyeok Kwon, Zhixiong Niu, Yeonho Yoo, Chuck Yoo, Gyeongsik Yang
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Abstract:Collaborative fine-tuning on edge devices adapts large language models to domain-specific data while keeping each device's data local. State-of-the-art (SOTA) collaborative fine-tuning techniques are largely designed for GPU-based edge devices and rely on pipeline parallelism (PP). However, many edge platforms, including IoT gateways, smart-home hubs, and in-vehicle computers, are primarily CPU-based. This paper reports that PP is ineffective on CPU-based edge devices because the same CPU handles both model computation and communication, which causes severe CPU contention. Our analysis shows that this leads to 5.75$\times$ higher computation stall ratios than on GPU devices on average. Tensor parallelism (TP) can alleviate this contention by separating computation and communication, but existing TP techniques assume homogeneous devices. On heterogeneous CPU edge devices, we find that this assumption causes faster workers to remain idle for up to 34% while waiting for slower devices at synchronization points. To address the limitations, we propose Xronos, a collaborative fine-tuning framework for heterogeneous CPU edge devices. Xronos uses TP as its execution backbone and combines lightweight profiling with heterogeneity-aware tensor partitioning to reduce the straggler bottleneck. Across diverse devices, models, and benchmark tasks, Xronos reduces fine-tuning time by 18% (TP) to 56% (PP) and the ratio of device idle time by $\sim$5.9$\times$ over SOTA techniques, while maintaining the accuracy.
Subjects:
Distributed, Parallel, and Cluster Computing (cs.DC)
Cite as:
arXiv:2609.19909 [cs.DC]
(or
arXiv:2609.19909v1 [cs.DC] for this version)
https://doi.org/10.48550/arXiv.2609.19909
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arXiv-issued DOI via DataCite (pending registration)
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From: Wonmi Choi [view email]
[v1]
Thu, 17 Sep 2026 08:52:18 UTC (505 KB)
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