AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads-Reference-Cited by-同舟云学术

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AutoSched: An Adaptive Self-configured Framework for Scheduling Deep Learning Training Workloads

Published:2024-05-30 Issue: Volume: Page:473-484
ISSN:
Container-title:Proceedings of the 38th ACM International Conference on Supercomputing
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Author:

Gao Wei¹^ORCID,Zhang Xu²^ORCID,Huang Shan³^ORCID,Guo Shangwei²^ORCID,Sun Peng⁴^ORCID,Wen Yonggang³^ORCID,Zhang Tianwei³^ORCID

Affiliation:

1. S-Lab, Nanyang Technological University, Singapore

2. Chongqing University, China

3. Nanyang Technological University, Singapore

4. SenseTime, China and Shanghai AI Lab, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650200.3656598

Reference46 articles.

1. 2024. KNative Issues. https://github.com/knative/serving/issues/8682.

2. 2024. Kubeflow Issues. https://github.com/kubeflow/kubeflow/issues/1219.

3. Md Abir Hossen, Sonam Kharade, Bradley Schmerl, Javier Cámara, Jason M O’Kane, Ellen C Czaplinski, Katherine A Dzurilla, David Garlan, and Pooyan Jamshidi. 2023. CaRE: Finding Root Causes of Configuration Issues in Highly-Configurable Robots. arXiv e-prints (2023), arXiv–2301.

4. Saurabh Agarwal, Amar Phanishayee, and Shivaram Venkataraman. 2023. Blox: A Modular Toolkit for Deep Learning Schedulers. arXiv preprint arXiv:2312.12621 (2023).

5. A Survey on Compiler Autotuning using Machine Learning

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