Flare: Flexible In-Network Allreduce
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Abstract (arXiv)
The allreduce operation is one of the most commonly used communication routines in distributed applications. To improve its bandwidth and to reduce network traffic, this operation can be accelerated by offloading it to network switches, that aggregate the data received from the hosts, and send them back the aggregated result. However, existing solutions provide limited customization opportunities and might provide suboptimal performance when dealing with custom operators and data types, with sparse data, or when reproducibility of the aggregation is a concern. To deal with these problems, in this work we design a flexible programmable switch by using as a building block PsPIN, a RISC-V architecture implementing the sPIN programming model. We then design, model, and analyze different algorithms for executing the aggregation on this architecture, showing performance improvements compared to state-of-the-art approaches.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 2; 2024: 2; 2025: 0; 2026: 2.
8 papers.
- 2023ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scalecited by 47
- 2021Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Modelscited by 15
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learningcited by 8
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreducecited by 8
- 2024ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabricscited by 2
- 2023SparDL: Distributed Deep Learning Training with Efficient Sparse Communicationcited by 0
- 2026In-Network Collective Operations: Game Changer or Challenge for AI Workloads?cited by 0
- 2026Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUscited by 0
- 2026Accelerating MoE with Dynamic In-Switch Computing on Multi-GPUs7 May 2026
- 2026In-Network Collective Operations: Game Changer or Challenge for AI Workloads?27 Jan 2026
- 2024ForestColl: Throughput-Optimal Collective Communications on Heterogeneous Network Fabrics9 Feb 2024
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreduce17 Jan 2024
- 2023SparDL: Distributed Deep Learning Training with Efficient Sparse Communication3 Apr 2023
- 2023ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale24 Mar 2023
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learning3 Sep 2022
- 2021Themis: A Network Bandwidth-Aware Collective Scheduling Policy for Distributed Training of DL Models9 Oct 2021