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Arachne: Learning to Plan Parallel Training on Dynamic Heterogeneous Clusters
Training large machine learning models on shared GPU infrastructures faces two challenges: (1) GPU availability shifts dynamically with varying resource demands from tenants, and (2) hardware heterogeneity accumulates as datacenters continuously adopt new GPU generations. Due to the vast search spac
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