# Enabling Reconfiguration-Communication Overlap for Collective Communication in Optical Networks

arXiv: https://arxiv.org/abs/2510.19322 · HTML: https://mlsanity.com/paper/2510.19322/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings](https://mlsanity.com/paper/2304.01433/) (2023)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches](https://mlsanity.com/paper/2111.04867/) (2021)
- [Blink: Fast and Generic Collectives for Distributed ML](https://mlsanity.com/paper/1910.04940/) (2019)
- [Synthesizing Optimal Collective Algorithms](https://mlsanity.com/paper/2008.08708/) (2020)
- [TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs](https://mlsanity.com/paper/2202.00433/) (2022)
- [Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem](https://mlsanity.com/paper/2305.13479/) (2023)
- [Swing: Short-cutting Rings for Higher Bandwidth Allreduce](https://mlsanity.com/paper/2401.09356/) (2024)
- [MixNet: A Runtime Reconfigurable Optical-Electrical Fabric for Distributed Mixture-of-Experts Training](https://mlsanity.com/paper/2501.03905/) (2025)
- [InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching Transceivers](https://mlsanity.com/paper/2502.03885/) (2025)
- [Efficient Direct-Connect Topologies for Collective Communications](https://mlsanity.com/paper/2202.03356/) (2022)
- [Birkhoff's Decomposition Revisited: Sparse Scheduling for High-Speed Circuit Switches](https://mlsanity.com/paper/2011.02752/) (2020)
- [D3: An Adaptive Reconfigurable Datacenter Network](https://mlsanity.com/paper/2406.13380/) (2024)

## Abstract (arXiv)

Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a great opportunity for applying optical network technologies. Optical networks with reconfigurable topologies promise high bandwidth and low latency for collective communications. However, existing approaches face inherent limitations: static topologies are inefficient for dynamic communication patterns within CC algorithm, while frequent topology reconfiguration matching every step of the algorithm incurs significant overhead. In this paper, we propose SWOT, a demand-aware optical network framework that employs ``intra-collective reconfiguration'' to dynamically align network resources with CC traffic patterns. SWOT hides reconfiguration latency by overlapping it with data transmission through three key techniques: textitHeterogeneous Message Splitting, textitAsynchronous Overlapping, and textitTopology Bypassing. Extensive simulations demonstrate that SWOT reduces communication completion time up to 89.7% across diverse CC algorithm compared to static baselines, demonstrating strong robustness to varying optical resources and reconfiguration delay.