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<p>This paper introduces an innovative GPU sharing and scheduling method to tackle resource wastage and underutilization in deep learning training jobs. Existing methods rely on execution time estimation models and rarely explore fine-grained GPU sharing. Our approach leverages a suspend and resume mechanism to save and migrate model training states. With a lightweight sampling analysis to predict job completion times, the proposed method tackles large job starvation and reuses fragmented resources. By efficiently utilizing fragmented resources, the scheduler reduces job completion and waiting times. Performances are evaluated using Microsoft Philly data and TF-Slim benchmarks on four image classification models to demonstrate significant improvements. Compared to traditional methods, our approach increases resource utilization by 4.1 times and reduces completion time by 3.6 times. The proposed method significantly enhances deep learning training efficiency and optimizes idle GPU resource usage, providing a flexible and efficient solution for future training needs.</p>

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A fine-grained GPU sharing and job scheduling for deep learning jobs on the cloud

  • 期刊论文

  • 发表日期：

    2025年1月3日

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  • Wu-Chun Chung,
  • Jyun-Sen Tong,
  • Zhi-Hao Chen

摘要

This paper introduces an innovative GPU sharing and scheduling method to tackle resource wastage and underutilization in deep learning training jobs. Existing methods rely on execution time estimation models and rarely explore fine-grained GPU sharing. Our approach leverages a suspend and resume mechanism to save and migrate model training states. With a lightweight sampling analysis to predict job completion times, the proposed method tackles large job starvation and reuses fragmented resources. By efficiently utilizing fragmented resources, the scheduler reduces job completion and waiting times. Performances are evaluated using Microsoft Philly data and TF-Slim benchmarks on four image classification models to demonstrate significant improvements. Compared to traditional methods, our approach increases resource utilization by 4.1 times and reduces completion time by 3.6 times. The proposed method significantly enhances deep learning training efficiency and optimizes idle GPU resource usage, providing a flexible and efficient solution for future training needs.