README.md

metadata

license: mit
tags:
  - benchmark
  - systems-ml
  - distributed-training
  - muon
  - optimizer
  - performance-analysis

🔬 Distributed Muon: Field Notes & Reproducibility Artifacts

Code, Performance Traces, and Analysis Logs

This repository contains the raw engineering artifacts for the deep-dive investigation: "Reproducing and Validating Distributed Muon".

It serves as the proof of work for the performance claims regarding the Muon optimizer's communication efficiency and computational overhead in a distributed setting (Data Parallel + Tensor Parallel).

📄 Read the Full Report: Reproducing and Validating Distributed Muon 🐢✨: A Practical Verification of Communication Efficiency Claims 🛠️ Get the Tutorial Code: bird-of-paradise/muon-distributed

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📂 Repository Structure

• traces/: Raw Chrome Trace (.json) files generated by PyTorch Profiler. You can load these into chrome://tracing or ui.perfetto.dev to visualize the exact CPU/GPU execution timeline.
  • comparison/: Side-by-side traces of AdamW vs. Muon (Hybrid DP=2/TP=2).
  • distributed_muon/: Scaling traces for DP=4, TP=4, and Hybrid configurations.
• analysis_scripts/: The exact Python scripts used to generate the traces and parse the performance metrics.
• figures/: High-resolution charts and trace visualizations used in the report.
• report/: A PDF archive of the full technical investigation.

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🔍 Key Findings (Verified in Traces)

The traces in this repository provide empirical evidence for the following:

1. Communication Efficiency: Muon (Hybrid DP2/TP2) demonstrates 0.57x the communication overhead of AdamW on a bandwidth-constrained cluster (PCIe Gen4 x4).
   • Evidence: Compare traces/comparison/adamw_fullstep_rank0.json vs muon_fullstep_dp2_tp2_rank0.json.
2. Optimizer Latency: The Muon step accounts for ~1.1% of total training time, validating the paper's "negligible overhead" claim.
3. Hybrid Scaling: The DP=2, TP=2 configuration outperforms pure DP or pure TP on 4 GPUs, balancing memory bandwidth with communication overhead.

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🛠️ How to Reproduce

To run these benchmarks yourself on a 4-GPU cluster:

1. Clone this repository.
2. Install dependencies: torch.
3. Run the benchmark script:

# This will generate new JSON traces in your local directory
python analysis_scripts/muon_vs_adam.py

4. Run the performance analysis on included trace files

python analysis_scripts/performance_comparison.py

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🙏 Acknowledgments

• Mahdi Chaker for generously providing GPU cluster access
• MoonShot AI team for open-sourcing their PoC implementation

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📖 Citation If you use these traces or analysis in your work, please cite:

@misc{wei2025muoneproducibility, author = {Wei, Jen}, title = {Distributed Muon: Performance Artifacts and Benchmarks}, year = {2025}, publisher = {Hugging Face}, journal = {Hugging Face Datasets}, howpublished = {\url{https://huggingface.co/datasets/bird-of-paradise/muon-distributed-reproducibility}} }