An Empirical Study on Low GPU Utilization of Deep Learning Jobs

Author:

Gao Yanjie1ORCID,He Yichen1ORCID,Li Xinze2ORCID,Zhao Bo1ORCID,Lin Haoxiang1ORCID,Liang Yoyo3ORCID,Zhong Jing3ORCID,Zhang Hongyu4ORCID,Wang Jingzhou5ORCID,Zeng Yonghua3ORCID,Gui Keli3ORCID,Tong Jie3ORCID,Yang Mao1ORCID

Affiliation:

1. Microsoft Research, Beijing, China

2. Peking University, Beijing, China

3. Microsoft, Beijing, China

4. Chongqing University, Chongqing, China

5. Tsinghua University, Beijing, China

Publisher

ACM

Reference84 articles.

1. Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 265--283.

2. Amazon. 2023. Amazon SageMaker. https://aws.amazon.com/sagemaker.

3. Weights & Biases. 2023. Current Best Practices for Training LLMs from Scratch. https://wandb.ai/site/llm-whitepaper.

4. Scott Boag, Parijat Dube, Benjamin Herta, Waldemar Hummer, Vatche Ishakian, K JAYARAM, Michael Kalantar, Vinod Muthusamy, Priya NAG-PURKAR, and Florian Rosenberg. 2017. Scalable multi-framework multi-tenant lifecycle management of deep learning training jobs. In Workshop on ML Systems, NIPS.

5. Samira Briongos, Pedro Malagón, José L. Risco, and José M. Moya. 2017. Building Accurate Models to Determine the Current CPU Utilization of a Host within a Virtual Machine Allocated on It. In Proceedings of the Summer Simulation Multi-Conference (Bellevue, Washington) (SummerSim '17). Society for Computer Simulation International, San Diego, CA, USA, Article 33, 12 pages.

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