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English(EN) ETH: Data-Balanced Heterogeneous Pipeline Parallelism for Training Large Models

ETH系统通过数据平衡并行改进大模型训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出ETH，一种旨在通过采用面向数据的异构流水线并行来提高大模型训练效率的新训练系统。与之前不均匀分配模型层的旧方法不同，ETH专注于异构流水线之间细粒度的数据平衡。该系统通过分层工作负载平衡框架和块状梯度聚合来解决高搜索开销和通信成本等挑战。评估表明，ETH的训练速度最高可提高2倍，同时显著减少了生成配置所需的时间。 AI

影响 引入了一种新颖的并行技术，可以加速未来大型AI模型的训练。

排序理由 这是一篇详细介绍新大模型训练系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

ETH系统通过数据平衡并行改进大模型训练

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这是一篇详细介绍新大模型训练系统的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mianjie Yu, Zhongmin Zhao, Zizhao Mo, Jianxiong Liao, Wenxuan Li, Yulin Qiao, Huanle Xu, Wenquan Yang, Yongqiang Yang, Zeren Li, Ruifeng Tang, Chengzhong Xu ·

    ETH：用于训练大模型的数据平衡异构流水线并行

    arXiv:2610.22035v1 Announce Type: cross Abstract: Current heterogeneity-aware training systems for large models balance workloads by assigning unequal numbers of layers across pipeline stages, a strategy aimed at equalizing execution time through a unique pipeline configuration. …

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