Accelerating DML Training in OCS-Based DCNs: (Invited Paper)
This paper explores the benefits of innetwork computing (INC) empowered all-optical interconnects (AOI) in accelerating distributed machine learning (DML) jobs. We describe the network architecture and service model, and present a large-job-first and a grouping-based interleaved scheduling policy for minimizing job completion time. The results verify the superiority of our scheduling policies and show the effectiveness of INC and AOI in mitigating bandwidth bottlenecking during DML training.
Authors
- Zuqing Zhu (ORCID: https://orcid.org/0000-0002-4251-788X)
- Xiaoliang Chen (ORCID: https://orcid.org/0000-0002-7805-6237)
- Xuexia Xie
- B L Tang
Institutions
- University of Science and Technology of China (CN)
Publication Details
- Published
- 2025-05-06
- DOI
- https://doi.org/10.23919/ondm65745.2025.11029359
- Primary Topic
- Experimental Learning in Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Key Research and Development Program of China