Abstract

Jobs in large-scale machine learning platforms are expressed using a computational graph of tasks with precedence constraints. To handle such precedence-constrained tasks that have machine-dependent communication demands in settings with heterogeneous service rates and communication times, we propose a new scheduling framework, Generalized Earliest Time First (GETF), that improves upon stateof- the-art results in the area. Specifically, we provide the first provable, worst-case approximation guarantee for the goal of minimizing the makespan of tasks with precedence constraints on related machines with machine-dependent communication times.

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