Sampling-Based Multi-Job Placement for Heterogeneous Deep Learning Clusters-Reference-Cited by-同舟云学术

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Sampling-Based Multi-Job Placement for Heterogeneous Deep Learning Clusters

Published:2024-06 Issue:6 Volume:35 Page:1029-1043
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Liu Kaiyang¹^ORCID,Wang Jingrong²^ORCID,Huang Zhiming³^ORCID,Pan Jianping³^ORCID

Affiliation:

1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL, Canada

2. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON, Canada

3. Department of Computer Science, University of Victoria, Victoria, BC, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Canada Foundation for Innovation

British Columbia Knowledge Development Fund

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/71/10494199/10504599.pdf?arnumber=10504599

Reference37 articles.

1. Deep Residual Learning for Image Recognition

2. Learning-based Cooperative Sound Event Detection with Edge Computing

3. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin,2018

4. Heterogeneity-aware cluster scheduling policies for deep learning workloads;Narayanan

5. Accelerating Distributed Learning in Non-Dedicated Environments

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