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English(EN) MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs

新的MeanField模型改进了GPU上的AI推理调度

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种MeanField代理模型，以解决在共享GPU上调度并发AI推理工作负载的挑战。这种新方法基于局部配置和聚合GPU状态来预测模型性能，避免了传统代理模型的组合复杂性。实验证明了其高预测准确性和可扩展性，MeanField代理使遗传算法调度器能够有效地处理大量配置并避免服务级别协议违规。 AI

影响 这种MeanField代理模型为优化共享GPU上的AI推理调度提供了一个可扩展的解决方案，有望提高资源利用率并降低复杂AI部署中的延迟。

排序理由 该集群包含一篇详细介绍新技术方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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新的MeanField模型改进了GPU上的AI推理调度

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该集群包含一篇详细介绍新技术方法的学术论文。[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) · Youssef Ennouri, Soonhoi Ha ·

    面向共享GPU上并发异构AI推理可扩展运行时调度的均场代理建模

    arXiv:2609.02109v1 Announce Type: cross Abstract: Deploying heterogeneous AI models concurrently on a shared GPU introduces resource contention that complicates runtime scheduling. While surrogate models avoid costly online benchmarking, their profiling requirements typically gro…

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