NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning
The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prior works often rely on heuristic or topology-agnostic search, handling communication and memory separately. Without per-device memory awareness, these methods typically ensure feasibility post hoc by sharding parameters and activations across many devices, increasing synchronization, inflating communication, and underutilizing compute-limiting scalability and efficiency on real datacenter networks. We present NEST, a network-, compute-, and memory-aware device placement framework that unifies model parallelism, topology modeling, and memory fea...
| Authors | Irene Wang, Vishnu Varma Venkata, Arvind Krishnamurthy et al. |
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| Primary category | cs.LG |
| Published | 6 March 2026 |
| Last updated at source | 24 May 2026 |
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