English(EN) Two-Layer Evolutionary Scheduling for Machine Learning Jobs in Multi-Cluster Environments

新的EVO-GP调度器优化了跨GPU集群的机器学习作业分配

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 07:22

研究人员开发了一种新颖的两层进化调度方法EVO-GP，旨在优化多集群GPU环境中机器学习作业的分配。该方法通过最小化平均和最大加权排队时间并最大化GPU利用率，解决了当前启发式调度器的局限性。该系统将作业到集群的分配与集群内的资源分配分离开来，采用进化算法进行负载均衡，并使用遗传编程来指导集群内的GPU分配。实验表明，EVO-GP在适应性和可扩展性方面优于现有方法。 AI

影响这种新的调度方法可以提高分布式GPU系统上大规模机器学习任务的效率并减少等待时间。

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在 arXiv cs.NE (Neural & Evolutionary) 阅读 →

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arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Xin Yao · 2026-11-06 07:22

面向多集群环境机器学习作业的双层进化调度

The rapid growth of large-scale machine learning (ML) workloads has led to unprecedented demands on GPU cluster scheduling systems. However, existing heuristic schedulers struggle to handle the complexity and dynamics of multi-cluster GPU environments, often leading to load imbal…

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面向多集群环境机器学习作业的双层进化调度

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