Hydra: Deadline-Aware and Efficiency-Oriented Scheduling for Deep Learning Jobs on Heterogeneous GPUs-Reference-Cited by-同舟云学术

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Hydra: Deadline-Aware and Efficiency-Oriented Scheduling for Deep Learning Jobs on Heterogeneous GPUs

Published:2023-08-01 Issue:8 Volume:72 Page:2224-2236
ISSN:0018-9340
Container-title:IEEE Transactions on Computers
language:
Short-container-title:IEEE Trans. Comput.

Author:

Yang Zichao¹^ORCID,Wu Heng²^ORCID,Xu Yuanjia¹,Wu Yuewen³,Zhong Hua³,Zhang Wenbo³^ORCID

Affiliation:

1. University of Chinese Academy of Sciences, Beijing, China

2. Chongqing School, University of Chinese Academy of Sciences, Beijing, China

3. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

Funder

Provincial Key Research and Development Program of Shandong

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software

Link

http://xplorestaging.ieee.org/ielx7/12/10177816/10036352.pdf?arnumber=10036352

Reference45 articles.

1. Efficient Real-time Earliest Deadline First based scheduling for Apache Spark

2. Balancing efficiency and fairness in heterogeneous GPU clusters for deep learning

4. Themis: Fair and efficient {GPU} cluster scheduling;mahajan;Proc 17th USENIX Symp Netw Syst Des Implementation,2020

5. Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. UniSched: A Unified Scheduler for Deep Learning Training Jobs With Different User Demands;IEEE Transactions on Computers;2024-06

2. Hadar: Heterogeneity-Aware Optimization-Based Online Scheduling for Deep Learning Cluster;2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2024-05-27

3. PED: Probabilistic Energy-efficient Deadline-aware scheduler for heterogeneous SoCs;Journal of Systems Architecture;2024-02

4. A Stochastic Approach for Scheduling AI Training Jobs in GPU-based Systems;IEEE Transactions on Cloud Computing;2023

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