28940

Deep Learning Workload Scheduling in GPU Datacenters: A Survey

Zhisheng Ye, Wei Gao, Qinghao Hu, Peng Sun, Xiaolin Wang, Yingwei Luo, Tianwei Zhang, Yonggang Wen
Peking University, China
ACM Computing Surveys, 2023

@article{ye2023deep,

   title={Deep Learning Workload Scheduling in GPU Datacenters: A Survey},

   author={Ye, Zhisheng and Gao, Wei and Hu, Qinghao and Sun, Peng and Wang, Xiaolin and Luo, Yingwei and Zhang, Tianwei and Wen, Yonggang},

   journal={ACM Computing Surveys},

   year={2023},

   publisher={ACM New York, NY}

}

Download Download (PDF)   View View   Source Source   

770

views

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The development of a DL model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU accelerators have been collectively constructed into a GPU datacenter. An efficient scheduler design for a GPU datacenter is crucially important to reduce operational cost and improve resource utilization. However, traditional approaches designed for big data or high performance computing workloads can not support DL workloads to fully utilize the GPU resources. Recently, many schedulers are proposed to tailor for DL workloads in GPU datacenters. This paper surveys existing research efforts for both training and inference workloads. We primarily present how existing schedulers facilitate the respective workloads from the scheduling objectives and resource utilization manner. Finally, we discuss several promising future research directions including emerging DL workloads, advanced scheduling decision making and underlying hardware resources. A more detailed summary of the surveyed paper and code links can be found at our project website.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Contact us: