Photonic Rails in ML Datacenters
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911050176135168 |
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| author | Ding, Eric Ouyang, Chuhan Singh, Rachee |
| author_facet | Ding, Eric Ouyang, Chuhan Singh, Rachee |
| contents | Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-to-all connectivity in rails imposes massive power, cost, and complexity overheads. We propose a rethinking of the rail abstraction by retaining its communication semantics, but realizing it using optical circuit switches. The key challenge is that optical switches support only one-to-one connectivity at a time, limiting the fan-out of traffic in ML workloads using hybrid parallelisms. We introduce parallelism-driven rail reconfiguration as a solution that leverages the sequential ordering between traffic from different parallelisms. We design a control plane, Opus, to enable time-multiplexed emulation of electrical rail switches using optical switches. More broadly, our work discusses a new research agenda: datacenter fabrics that co-evolve with the model parallelism dimensions within each job, as opposed to the prevailing mindset of reconfiguring networks before a job begins. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Photonic Rails in ML Datacenters Ding, Eric Ouyang, Chuhan Singh, Rachee Networking and Internet Architecture Rail-optimized network fabrics have become the de facto datacenter scale-out fabric for large-scale ML training. However, the use of high-radix electrical switches to provide all-to-all connectivity in rails imposes massive power, cost, and complexity overheads. We propose a rethinking of the rail abstraction by retaining its communication semantics, but realizing it using optical circuit switches. The key challenge is that optical switches support only one-to-one connectivity at a time, limiting the fan-out of traffic in ML workloads using hybrid parallelisms. We introduce parallelism-driven rail reconfiguration as a solution that leverages the sequential ordering between traffic from different parallelisms. We design a control plane, Opus, to enable time-multiplexed emulation of electrical rail switches using optical switches. More broadly, our work discusses a new research agenda: datacenter fabrics that co-evolve with the model parallelism dimensions within each job, as opposed to the prevailing mindset of reconfiguring networks before a job begins. |
| title | Photonic Rails in ML Datacenters |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.08119 |