RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing-Reference-Cited by-同舟云学术

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RAP: Resource-aware Automated GPU Sharing for Multi-GPU Recommendation Model Training and Input Preprocessing

Published:2024-04-27 Issue: Volume:19 Page:964-979
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
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Author:

Wang Zheng¹^ORCID,Wang Yuke²^ORCID,Deng Jiaqi²^ORCID,Zheng Da³^ORCID,Li Ang⁴^ORCID,Ding Yufei¹^ORCID

Affiliation:

1. University of California, San Diego, San Diego, United States of America

2. University of California, Santa Barbara, Santa Barbara, United States of America

3. Amazon, Santa Clara, USA

4. Pacific Northwest National Laboratory, Richland, United States of America

Funder

NSF (National Science Foundation)

Amazon Faculty Research Award 2021

DOE U.S. Office of Science

DOE U.S. Office of Advanced Scientific Computing Research

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3620665.3640406

Reference55 articles.

1. Criteo display ad challenge. https://www.kaggle.com/c/criteodisplay-ad-challenge.

2. Nvidia data loading library (dali). https://developer.nvidia.com/dali.

3. Nvidia merlin hugectr. https://developer.nvidia.com/nvidia-merlin/hugectr.

4. Terabyte click logs. https://labs.criteo.com/2013/12/downloadterabyte-click-logs.

5. Torchrec. github.com/pytorch/torchrec/, 2022.

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