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.

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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

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article

Accelerating DML Training in OCS-Based DCNs: (Invited Paper)

Zuqing Zhu, Xiaoliang Chen, Xuexia Xie, B L Tang
Experimental Learning in Engineering
article

Accelerating DML Training in OCS-Based DCNs: (Invited Paper)

Zuqing Zhu, Xiaoliang Chen, Xuexia Xie, B L Tang
article en

Abstract

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.

University of Science and Technology of China (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 81%
Experimental Learning in Engineering
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