# Towards Easy and Realistic Network Infrastructure Testing for Large-scale Machine Learning

arXiv: https://arxiv.org/abs/2504.20854 · HTML: https://mlsanity.com/paper/2504.20854/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM](https://mlsanity.com/paper/2104.04473/) (2021)
- [MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs](https://mlsanity.com/paper/2402.15627/) (2024)
- [Accelerating Distributed MoE Training and Inference with Lina](https://mlsanity.com/paper/2210.17223/) (2022)
- [ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale](https://mlsanity.com/paper/2303.14006/) (2023)
- [TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches](https://mlsanity.com/paper/2111.04867/) (2021)
- [TopoOpt: Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs](https://mlsanity.com/paper/2202.00433/) (2022)
- [CASSINI: Network-Aware Job Scheduling in Machine Learning Clusters](https://mlsanity.com/paper/2308.00852/) (2023)
- [An In-Depth Analysis of the Slingshot Interconnect](https://mlsanity.com/paper/2008.08886/) (2020)
- [Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces](https://mlsanity.com/paper/2305.14516/) (2023)
- [Impact of RoCE Congestion Control Policies on Distributed Training of DNNs](https://mlsanity.com/paper/2207.10898/) (2022)
- [TACOS: Topology-Aware Collective Algorithm Synthesizer for Distributed Machine Learning](https://mlsanity.com/paper/2304.05301/) (2023)
- [Towards a Standardized Representation for Deep Learning Collective Algorithms](https://mlsanity.com/paper/2408.11008/) (2024)

## Built on by (most cited first, 3 of 3)

- [Scalable Synthesis of distributed LLM workloads through Symbolic Tensor Graphs](https://mlsanity.com/paper/2511.10480/) (2025)
- [MLCommons Chakra: Advancing Performance Benchmarking and Co-design using Standardized Execution Traces](https://mlsanity.com/paper/2605.11333/) (2026)
- [ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling](https://mlsanity.com/paper/2606.10440/) (2026)

## Abstract (arXiv)

This paper lays the foundation for Genie, a testing framework that captures the impact of real hardware network behavior on ML workload performance, without requiring expensive GPUs. Genie uses CPU-initiated traffic over a hardware testbed to emulate GPU to GPU communication, and adapts the ASTRA-sim simulator to model interaction between the network and the ML workload.