Routing for Large ML Models
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916646365429760 |
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| author | Cohen, Ofir Schapira, Jose Yallouz Michael Belkar, Shahar Mizrahi, Tal |
| author_facet | Cohen, Ofir Schapira, Jose Yallouz Michael Belkar, Shahar Mizrahi, Tal |
| contents | Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for \textit{quantifying} network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically \textit{optimizing} routing with respect to this global metric. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_05324 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Routing for Large ML Models Cohen, Ofir Schapira, Jose Yallouz Michael Belkar, Shahar Mizrahi, Tal Networking and Internet Architecture Machine Learning Training large language models (LLMs), and other large machine learning models, involves repeated communication of large volumes of data across a data center network. The communication patterns induced by these training process exhibit high regularity and persistence, giving rise to significant opportunities for optimizing the manner in which flows are routed across the network. We present an algorithmic framework for \textit{quantifying} network-wide efficiency in the context of training LLMs (and other large-scale ML models), and for periodically \textit{optimizing} routing with respect to this global metric. |
| title | Routing for Large ML Models |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2503.05324 |