core12345/MoE_trace_analysis

Profiling Result Visualization Dataset

📖 Introduction

This repository is a part of "MoE_expert_selection_trace Repository". It provides analysis and visualization results of our profiled expert selection trace for MoE LLM on the MMLU dataset, including Llama4-Maverick, DeepSeek-R1, and Qwen3-235B. For a deeper understanding of the analysis, please refer to our paper.

Key Components:

• cross_token_heatmap/ – Expert selection heatmap across two adjacent tokens. This corresponds to token-level temporal relations in our paper. Results for prefill and decode stages are presented separately.
• column_by_layer/ – Expert selection heatmap across two adjacent layers. This corresponds to layer-level temporal relations in our paper. Results for prefill and decode stages are presented separately.
• same_layer_heatmap/ – Co-activation heatmap for experts. This corresponds to spatial relations for expert pairs in our paper.
• cross_layer_heatmap/ – Activation frequency for different experts, presented as column figures. This corresponds to spatial relations for single experts in our paper.

---

📂 Dataset Structure

Top-Level Layout

profiling_result_fig/
├── meta-llama
│   └── Llama-4-Maverick-17B-128E-Instruct
│
├── cognitivecomputations
│   └── DeepSeek-R1-AWQ
│       ├── cross_token_heatmap
│       │   └── mmlu
│       │       ├── decode
│       │       │   ├── xxx.png
│       │       │   ├── xxx.txt
│       │       │   ├── ...
│       │       │   
│       │       ├── prefill
│       │       └── prefill_decode_corr.txt
│       ├── same_layer_heatmap
│       ├── cross_layer_heatmap
│       └── column_by_layer
│
└── Qwen
    └── Qwen3-235B-A22B-FP8

---

📑 File Naming and Domains

The subfolders are named after academic or professional domains from the MMLU benchmark and related datasets. Examples:

Heatmap Files:

There are five types of files:

• layer_*.png – The original heatmap, reflecting the conditional probability of two activated experts.
• layer_*_avg.png – Normalized heatmap with each value divided by the average value of its corresponding column, eliminating vertical white lines caused by frequently selected experts.
• layer_*_skew.txt – Accumulated frequency of the most popular expert pairs, calculated by aggregating frequency.
• layer_*_cnt_skew.txt – Accumulated frequency of the most popular expert pairs, calculated by aggregating count. Similar to layer_*_skew.txt, but more accurate.
• prefill_decode_corr.txt – Correlation ratio between the prefill stage and decode stage.

Column Figures:

There are three types of files:

• layer_*_prefill.png – Statistical results for the prefill stage only.
• layer_*_decode.png – Statistical results for the decode stage only.
• layer_*_both.png – Statistical results considering both prefill and decode stages.

---

📌 Citation

If you use this dataset in your research or project, please cite it as:

@misc{yu2025orderschaosenhancinglargescale,
      title={Orders in Chaos: Enhancing Large-Scale MoE LLM Serving with Data Movement Forecasting}, 
      author={Zhongkai Yu and Yue Guan and Zihao Yu and Chenyang Zhou and Shuyi Pei and Yangwook Kang and Yufei Ding and Po-An Tsai},
      year={2025},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2510.05497}, 
}