Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913144192892928 |
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| author | Papavasileiou, Ioannis Prabhakar, Sairam Deo, Indu Kant Makovejs, Sergejs |
| author_facet | Papavasileiou, Ioannis Prabhakar, Sairam Deo, Indu Kant Makovejs, Sergejs |
| contents | We use discrete-event simulation to quantify the impact of fiber latency on the efficacy of geo-distributed AI model training with data parallelism. We conclude that the optimum distances between two AI clusters is 10-100km, over which hollow-core fiber enables 25% higher compute-communication overlap. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19169 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training Papavasileiou, Ioannis Prabhakar, Sairam Deo, Indu Kant Makovejs, Sergejs Performance Distributed, Parallel, and Cluster Computing We use discrete-event simulation to quantify the impact of fiber latency on the efficacy of geo-distributed AI model training with data parallelism. We conclude that the optimum distances between two AI clusters is 10-100km, over which hollow-core fiber enables 25% higher compute-communication overlap. |
| title | Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training |
| topic | Performance Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2605.19169 |