# An In-Depth Analysis of the Slingshot Interconnect

arXiv: https://arxiv.org/abs/2008.08886 · HTML: https://mlsanity.com/paper/2008.08886/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Aug 2020.

## Built on by (most cited first, 13 of 13)

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- [Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning](https://mlsanity.com/paper/2501.04266/) (2025)
- [GPU-centric Communication Schemes for HPC and ML Applications](https://mlsanity.com/paper/2503.24230/) (2025)
- [ExaServe: Large-Scale LLM Serving on Exascale HPC Systems](https://mlsanity.com/paper/2609.10812/) (2026)

## 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.