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English(EN) Power- and Fragmentation-aware Online Scheduling for GPU Datacenters

新策略解决GPU数据中心的功耗和碎片问题

一篇新论文提出了一种名为PWR的调度策略,以减少GPU数据中心的功耗,特别是针对AI和LLM任务。该策略与碎片梯度下降(FGD)结合使用,以平衡功耗效率和最小化GPU碎片。在模拟集群中的实验表明,PWR能有效降低功耗,同时缓解碎片问题。 AI

影响 优化GPU数据中心的调度可以降低AI和LLM训练的运营成本和能源消耗。

排序理由 该集群包含一篇详细介绍GPU数据中心新调度策略的研究论文。

在 arXiv cs.AI 阅读 →

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新策略解决GPU数据中心的功耗和碎片问题

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Francesco Lettich, Emanuele Carlini, Franco Maria Nardini, Raffaele Perego, Salvatore Trani ·

    面向GPU数据中心的功耗和碎片感知在线调度

    arXiv:2412.17484v2 Announce Type: replace-cross Abstract: The rise of Artificial Intelligence and Large Language Models is driving increased GPU usage in data centers for complex training and inference tasks, impacting operational costs, energy demands, and the environmental foot…

  2. LessWrong (AI tag) TIER_1 English(EN) · SJ_Beard ·

    Agency的不同视角及其对齐的启示

    <p><b><span style="white-space: pre-wrap;">TL;DR; </span></b><span style="white-space: pre-wrap;">These nine lenses on agency give you a wide range of tools to model and interpret agents. Each gives you lens-specific alignment methods and lens-specific risks</span><b><span style=…

  3. LessWrong (AI tag) TIER_1 English(EN) · David Africa ·

    短短数月,从过度诱导到诱导不足:LLM 倾向性的转变

    <p><span style="white-space: pre-wrap;">For a while, we felt we had a pretty good grasp over LLM behaviour because we had to exert significant effort to elicit misaligned behaviour. Then, fairly suddenly, we moved to undereliciting misaligned behaviour. Let us recount: </span></p…