用大模型理解代码日志，让GPU调度更聪明
Semantic-Aware Scheduling for GPU Clusters with Large Language Models
- 通过大模型分析代码和日志，挖掘任务语义信息
- 实测平均任务完成时间提升1.91倍
- 适合需要高效调度的深度学习研发团队
深度学习调度器在优化GPU集群资源分配中至关重要，但存在关键缺陷：对任务的语义上下文几乎无感知，只能依赖有限元数据，导致需大量预热、完成时间预测不准、故障处理能力弱、可观测性差。为此，我们提出SchedMate框架，通过系统性提取被忽视的非结构化数据源——源代码、运行日志和历史任务——中的深层洞察，以三个基于大语言模型的组件非侵入式增强现有调度器。该框架可无缝集成至现有DL调度系统。在128个GPU的真实集群及生产级数据轨迹上的评估显示，SchedMate将平均任务完成时间缩短达1.91倍，显著提升调度性能，证明语义感知在现代深度学习调度中的关键作用。
原文摘要 · Abstract (English)
Deep learning (DL) schedulers are pivotal in optimizing resource allocation in GPU clusters, but operate with a critical limitation: they are largely blind to the semantic context of the jobs they manage. This forces them to rely on limited metadata, leading to high profiling overhead, unreliable duration estimation, inadequate failure handling, and poor observability. To this end, we propose SchedMate, a framework that bridges this semantic gap by systematically extracting deep insights from overlooked, unstructured data sources: source code, runtime logs, and historical jobs. SchedMate enhances existing schedulers non-intrusively through three LLM-based components. Our implementation integrates seamlessly with existing deep learning schedulers. Evaluations on a 128-GPU physical cluster and extensive simulations on production traces show SchedMate reduces average job completion times by up to 1.91x, substantially enhancing the scheduling performance, demonstrating the critical role of semantic-awareness in modern DL scheduling.
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