Tuning Collective Patterns to Alleviate Congestion in Shared AI Clusters
Distributed AI training involves recurring rounds of data exchange between multiple pairs of GPU nodes. Slowdown in even one flow due to congestion can cause the entire communication round to slowdown. Current approaches for evading congestion in AI clusters assume global control over the entire workload (e.g. coordinating the schedule of all jobs) or assume infrastructural support (e.g. adaptive routing in switches). They are thus ill-suited in a shared cloud setting where AI jobs belonging to one user can face external congestion from other users' jobs or background traffic beyond its own control. In this paper, we build a system, REACT, that tunes the recurring pattern of data exchange...
| Authors | Eashan Gupta, Yongzhou Chen, Apoorve Mohan et al. |
|---|---|
| Primary category | cs.NI |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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