Author:
Wang Sheng,Chen Shiping,Shi Yumei
Funder
University of Shanghai for Science and Technology
National Natural Science Foundation of China
Subject
Computer Networks and Communications,Hardware and Architecture,Software
Reference52 articles.
1. S. Chaudhary, et al., Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning, in: Proceedings of the Fifteenth European Conference on Computer Systems, 2020, pp. 1–16.
2. Q. Weng, et al., MLaaS in the wild: Workload analysis and scheduling in Large-Scale heterogeneous GPU clusters, in: 19th USENIX symposium on networked systems design and implementation, 2022, pp. 945–960.
3. AI and cloud computing-how the cloud is accelerating AI;Rajasekaran;Int. J. Intell. Syst. Appl. Eng.,2023
4. Horus: Interference-aware and prediction-based scheduling in deep learning systems;Yeung;IEEE Trans. Parallel Distrib. Syst.,2021
5. W. Xiao, et al., AntMan: Dynamic scaling on GPU clusters for deep learning, in: 14th USENIX Symposium on Operating Systems Design and Implementation, OSDI 20, 2020, pp. 533–548.