DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines-Reference-Cited by-同舟云学术

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DynaPipe: Optimizing Multi-task Training through Dynamic Pipelines

Published:2024-04-22 Issue: Volume:33 Page:542-559
ISSN:
Container-title:Proceedings of the Nineteenth European Conference on Computer Systems
language:
Short-container-title:

Author:

Jiang Chenyu¹^ORCID,Jia Zhen²^ORCID,Zheng Shuai³^ORCID,Wang Yida²^ORCID,Wu Chuan⁴^ORCID

Affiliation:

1. The University of Hong Kong and AWS

2. Amazon Web Services

3. Boson AI and AWS

4. The University of Hong Kong

Funder

Research Grants Council, University Grants Committee

Amazon Web Services

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3627703.3629585

Reference40 articles.

1. Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta. 2021. Muppet: Massive Multi-task Representations with Pre-Finetuning. arXiv:2101.11038 [cs.CL]

2. Amazon Web Services Inc. 2023. Amazon EC2 P4d Instances. https://aws.amazon.com/ec2/instance-types/p4/.

3. Amazon Web Services Inc. 2023. Elastic Fabric Adapter. https://aws.amazon.com/hpc/efa/.

4. Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The Long-Document Transformer. (2020). arXiv:2004.05150 [cs.CL]

5. Scheduling jobshops with some identical or similar jobs

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