mlsanityData to 14 Oct 2026

In-Network Collective Operations: Game Changer or Challenge for AI Workloads?

arXiv 2601.19132 ↗First posted 27 Jan 2026cs.NI cs.AI cs.ARcited by 0

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Abstract (arXiv)

This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non-experts in AI or networking, fostering a connection between these communities. Consider two types of INC: Edge-INC, where the system is implemented at the node level, and Core-INC, where the system is embedded within network switches. We outline the potential performance benefits as well as six key obstacles in the context of both Edge-INC and Core-INC that may hinder their adoption. Finally, we present a set of predictions for the future development and application of INC.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 27 Jan 2026.

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10 of 31 references.

References · 10

  1. 2019ZeRO: Memory Optimizations Toward Training Trillion Parameter Modelscited by 1,301
  2. 2023PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallelcited by 660
  3. 2021Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networkscited by 527
  4. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 156
  5. 2023Microscaling Data Formats for Deep Learningcited by 152
  6. 2018SparCML: High-Performance Sparse Communication for Machine Learningcited by 34
  7. 2022Near-Optimal Sparse Allreduce for Distributed Deep Learningcited by 23
  8. 2021Flare: Flexible In-Network Allreducecited by 9
  9. 2024Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AIcited by 3
  10. 2017sPIN: High-performance streaming Processing in the Networkcited by 2

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