Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters

• URL: http://arxiv.org/abs/2501.05563v1
• Date: Thu, 09 Jan 2025 20:19:01 GMT
• Title: Prediction-Assisted Online Distributed Deep Learning Workload Scheduling in GPU Clusters
• Authors: Ziyue Luo, Jia Liu, Myungjin Lee, Ness B. Shroff,
• Abstract summary: This paper proposes an adaptive shortest-remaining-processing-time-first (A-SRPT) scheduling algorithm. By modeling each job as a graph corresponding to heterogeneous Deep Neural Network (DNN) models, A-SRPT strategically assigns jobs to the available GPU. A-SRPT maps the complex scheduling problem into a single-machine instance, which is addressed optimally by a preemptive "shortest-remaining-processing-time-first" strategy.
• Score: 24.845122459974466
• License:
• Abstract: The recent explosive growth of deep learning (DL) models has necessitated a compelling need for efficient job scheduling for distributed deep learning training with mixed parallelisms (DDLwMP) in GPU clusters. This paper proposes an adaptive shortest-remaining-processing-time-first (A-SRPT) scheduling algorithm, a novel prediction-assisted online scheduling approach designed to mitigate the challenges associated with DL cluster scheduling. By modeling each job as a graph corresponding to heterogeneous Deep Neural Network (DNN) models and their associated distributed training configurations, A-SRPT strategically assigns jobs to the available GPUs, thereby minimizing inter-server communication overhead. Observing that most DDLwMP jobs recur, A-SRPT incorporates a random forest regression model to predict training iterations. Crucially, A-SRPT maps the complex scheduling problem into a single-machine instance, which is addressed optimally by a preemptive "shortest-remaining-processing-time-first" strategy. This optimized solution serves as a guide for actual job scheduling within the GPU clusters, leading to a theoretically provable competitive scheduling efficiency. We conduct extensive real-world testbed and simulation experiments to verify our proposed algorithms.

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