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English(EN) Demystifying Pipeline Parallelism: First Theory for PipeDream

新理论揭秘大型模型流水线并行

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个用于机器学习流水线并行的理论框架，引入了随机流水线并行（RPD）。这一新抽象为PipeDream风格的方法提供了首个非凸收敛保证。该研究还分析了稳态PipeDream的扩展行为，表明延迟随着阶段数的增加而增加，影响收敛。将PipeDream与LocalSGD进行比较的实验表明，最优方法取决于具体目标和阶段数。 AI

影响 为扩展大型模型训练提供了理论基础，有望提高分布式机器学习系统的效率。

排序理由 这是一篇发表在arXiv上的研究论文，详细介绍了机器学习并行方面的理论进展。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新理论揭秘大型模型流水线并行

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这是一篇发表在arXiv上的研究论文，详细介绍了机器学习并行方面的理论进展。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ivan Ilin, Peter Richt\'arik ·

    揭秘流水线并行：PipeDream的首个理论

    arXiv:2606.03498v1 Announce Type: new Abstract: Training modern machine learning models increasingly requires computation to be distributed across many accelerators. Data parallelism remains the default choice and is often paired with tensor-parallel sharding, but model paralleli…

  2. arXiv cs.LG TIER_1 English(EN) · Peter Richtárik ·

    揭秘流水线并行：PipeDream的首个理论

    Training modern machine learning models increasingly requires computation to be distributed across many accelerators. Data parallelism remains the default choice and is often paired with tensor-parallel sharding, but model parallelism becomes unavoidable once parameters, activati…

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