Embracing Uncertainty for Equity in Resource Allocation in ML Training-Reference-Cited by-同舟云学术

Embracing Uncertainty for Equity in Resource Allocation in ML Training

Published:2023-08-07 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 52nd International Conference on Parallel Processing
language:
Short-container-title:

Author:

Tairin Suraiya¹^ORCID,Shen Haiying²^ORCID,Zhang Zeyu¹^ORCID

Affiliation:

1. University of Virginia, USA

2. University of Virginia, United States of America

Funder

FHWA

Microsoft Research Faculty Fellowship

CCF

Commonwealth Cyber Initiative (CCI)

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3605573.3605583

Reference45 articles.

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2. 2023. Amazon EC2. https://aws.amazon.com/en/blogs/machine-learning/traindeep-learning-models-on-gpus-using-amazon-ec2-spot-instances/. . 2023. Amazon EC2. https://aws.amazon.com/en/blogs/machine-learning/traindeep-learning-models-on-gpus-using-amazon-ec2-spot-instances/. .

3. 2023. Cifar-10 dataset. https://www.cs.toronto.edu/kriz/cifar.html. 2023. Cifar-10 dataset. https://www.cs.toronto.edu/kriz/cifar.html.

4. 2023. cpu-load-generator. https://pypi.org/project/cpu-load-generator/. 2023. cpu-load-generator. https://pypi.org/project/cpu-load-generator/.

5. 2023. Microsoft trace. https://github.com/msr-fiddle/philly-traces. 2023. Microsoft trace. https://github.com/msr-fiddle/philly-traces.

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