Characterizing Training Performance and Energy for Foundation Models and Image Classifiers on Multi-Instance GPUs-Reference-Cited by-同舟云学术

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Characterizing Training Performance and Energy for Foundation Models and Image Classifiers on Multi-Instance GPUs

Published:2024-04-22 Issue: Volume:9 Page:47-55
ISSN:
Container-title:Proceedings of the 4th Workshop on Machine Learning and Systems
language:
Short-container-title:

Author:

Espenshade Connor¹^ORCID,Peng Rachel¹^ORCID,Hong Eumin¹^ORCID,Calman Max²^ORCID,Zhu Yue²^ORCID,Parida Pritish²^ORCID,Lee Eun Kyung²^ORCID,Kim Martha A.¹^ORCID

Affiliation:

1. Columbia University, New York, New York, USA

2. IBM Research Yorktown Heights, New York, USA

Funder

IBM

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3642970.3655830

Reference29 articles.

1. 2020. NVIDIA A100 Tensor Core GPU Architecture. https://images.nvidia.com/aem-dam/en-zz/Solutions/data-center/nvidia-ampere-architecture-whitepaper.pdf

2. 2021. NVIDIA A100 Tensor Core GPU | Data Sheet. https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf

3. 2022. NVIDIA H100 Tensor Core GPU. https://www.nvidia.com/en-us/data-center/h100/

4. 2022. NVIDIA Multi-Instance GPU User Guide. https://docs.nvidia.com/datacenter/tesla/mig-user-guide/index.html

5. 2023. Multi-Process Service. https://docs.nvidia.com/deploy/mps/index.html

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