An In-Depth Analysis of the Slingshot Interconnect
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Abstract (arXiv)
The interconnect is one of the most critical components in large scale computing systems, and its impact on the performance of applications is going to increase with the system size. In this paper, we will describe Slingshot, an interconnection network for large scale computing systems. Slingshot is based on high-radix switches, which allow building exascale and hyperscale datacenters networks with at most three switch-to-switch hops. Moreover, Slingshot provides efficient adaptive routing and congestion control algorithms, and highly tunable traffic classes. Slingshot uses an optimized Ethernet protocol, which allows it to be interoperable with standard Ethernet devices while providing high performance to HPC applications. We analyze the extent to which Slingshot provides these features, evaluating it on microbenchmarks and on several applications from the datacenter and AI worlds, as well as on HPC applications. We find that applications running on Slingshot are less affected by congestion compared to previous generation networks.
Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Aug 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 4; 2025: 5; 2026: 1.
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- 2021Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelinescited by 68
- 2025Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architecturescited by 38
- 2022Near-Optimal Sparse Allreduce for Distributed Deep Learningcited by 23
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learningcited by 8
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreducecited by 8
- 2024Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnectscited by 6
- 2024Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compressioncited by 4
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascalecited by 3
- 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learningcited by 3
- 2025Bine Trees: Enhancing Collective Operations by Optimizing Communication Localitycited by 1
- 2025Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioningcited by 0
- 2025GPU-centric Communication Schemes for HPC and ML Applicationscited by 0
1 more
- 2026ExaServe: Large-Scale LLM Serving on Exascale HPC Systems9 Sep 2026
- 2025Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality24 Aug 2025
- 2025Insights into DeepSeek-V3: Scaling Challenges and Reflections on Hardware for AI Architectures14 May 2025
- 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning29 Apr 2025
- 2025GPU-centric Communication Schemes for HPC and ML Applications31 Mar 2025
- 2025Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning8 Jan 2025
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale7 Oct 2024
- 2024Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects26 Aug 2024
- 2024Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression5 Jul 2024
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreduce17 Jan 2024
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learning3 Sep 2022
- 2022Near-Optimal Sparse Allreduce for Distributed Deep Learning19 Jan 2022
- 2021Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelinescited by 68
- 2022Near-Optimal Sparse Allreduce for Distributed Deep Learningcited by 23
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learningcited by 8
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreducecited by 8
- 2024Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnectscited by 6
- 2024Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compressioncited by 4
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascalecited by 3
- 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learningcited by 3
- 2025Bine Trees: Enhancing Collective Operations by Optimizing Communication Localitycited by 1
- 2025Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioningcited by 0
- 2025GPU-centric Communication Schemes for HPC and ML Applicationscited by 0
- 2025Bine Trees: Enhancing Collective Operations by Optimizing Communication Locality24 Aug 2025
- 2025Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning29 Apr 2025
- 2025GPU-centric Communication Schemes for HPC and ML Applications31 Mar 2025
- 2025Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning8 Jan 2025
- 2024A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale7 Oct 2024
- 2024Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects26 Aug 2024
- 2024Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression5 Jul 2024
- 2024Swing: Short-cutting Rings for Higher Bandwidth Allreduce17 Jan 2024
- 2022HammingMesh: A Network Topology for Large-Scale Deep Learning3 Sep 2022
- 2022Near-Optimal Sparse Allreduce for Distributed Deep Learning19 Jan 2022
- 2021Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines14 Jul 2021