# Slim Fly: A Cost Effective Low-Diameter Network Topology

arXiv: https://arxiv.org/abs/1912.08968 · HTML: https://mlsanity.com/paper/1912.08968/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2019.

## Built on by (most cited first, 7 of 7)

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- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [HammingMesh: A Network Topology for Large-Scale Deep Learning](https://mlsanity.com/paper/2209.01346/) (2022)
- [Efficient Direct-Connect Topologies for Collective Communications](https://mlsanity.com/paper/2202.03356/) (2022)
- [TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning](https://mlsanity.com/paper/2304.05301/) (2023)
- [Cluster Topology-Driven Placement of Experts Reduces Network Traffic in MoE Inference](https://mlsanity.com/paper/2508.09229/) (2025)

## Abstract (arXiv)

We introduce a high-performance cost-effective network topology called Slim Fly that approaches the theoretically optimal network diameter. Slim Fly is based on graphs that approximate the solution to the degree-diameter problem. We analyze Slim Fly and compare it to both traditional and state-of-the-art networks. Our analysis shows that Slim Fly has significant advantages over other topologies in latency, bandwidth, resiliency, cost, and power consumption. Finally, we propose deadlock-free routing schemes and physical layouts for large computing centers as well as a detailed cost and power model. Slim Fly enables constructing cost effective and highly resilient datacenter and HPC networks that offer low latency and high bandwidth under different HPC workloads such as stencil or graph computations.