Interference-aware Multiplexing for Deep Learning in GPU Clusters: A Middleware Approach-Reference-Cited by-同舟云学术

Interference-aware Multiplexing for Deep Learning in GPU Clusters: A Middleware Approach

Published:2023-11-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
language:
Short-container-title:

Author:

Chen Wenyan¹²^ORCID,Mo Zizhao¹^ORCID,Xu Huanle¹^ORCID,Ye Kejiang²^ORCID,Xu Chengzhong¹^ORCID

Affiliation:

1. University of Macau, Macau, China

2. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Funder

National Key Research and Development Program of China

the Science and Technology Development Fund of Macau

the Multi-Year Research Grant of University of Macau

the Start-up Research Grant of University of Macau

National Natural Science Foundation of China

Guangdong Special Support Plan

Shenzhen Basic Research Program

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581784.3607060

Reference78 articles.

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2. 2020. Antman. https://github.com/alibaba/GPU-scheduler-for-deep-learning 2020. Antman. https://github.com/alibaba/GPU-scheduler-for-deep-learning

3. 2020. REDDIT-MULTI-12K. https://chrsmrrs.github.io/datasets/docs/datasets/ 2020. REDDIT-MULTI-12K. https://chrsmrrs.github.io/datasets/docs/datasets/

4. 2021. Helios Traces. https://github.com/S-Lab-System-Group/HeliosData 2021. Helios Traces. https://github.com/S-Lab-System-Group/HeliosData

5. 2022. NVIDIA MPS. https://docs.nvidia.com/deploy/mps/index.html 2022. NVIDIA MPS. https://docs.nvidia.com/deploy/mps/index.html

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