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English(EN) A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

新AI框架优化计算系统的DAG调度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了WeCAN，一个新颖的强化学习框架，旨在优化大规模计算系统中定向无环图（DAG）的调度。该框架解决了生成过程带来的任务池兼容性和最优性差距等挑战。WeCAN采用两阶段单次设计，生成任务池分数和全局参数，然后进行调度构建映射。在TPC-H查询DAG和ML编译器计算图上的实验表明，WeCAN在完成时间方面优于现有基线，同时保持了具有竞争力的推理时间。 AI

排序理由 该集群包含一篇关于DAG调度新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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

新AI框架优化计算系统的DAG调度

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该集群包含一篇关于DAG调度新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen ·

    面向异构DAG调度的具有间隙感知生成的学习方法

    arXiv:2603.23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks comp…

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