Datasets:

GPUMODE
/

kernelbot-data

configs:
  - config_name: amd_submissions
    data_files: submissions.parquet
  - config_name: amd_successful_submissions
    data_files: successful_submissions.parquet
  - config_name: nvidia_nvfp4_submissions
    data_files: nvidia_nvfp4_submissions.parquet
  - config_name: leaderboards
    data_files: leaderboards.parquet
tags:
  - code
license: cc-by-4.0

KernelBot Competition Data

This dataset contains GPU kernel submissions from the KernelBot competition platform. Submissions are optimized GPU kernels written for specific hardware targets.

Data Files

AMD MI300 Submissions

File	Description
`submissions.parquet`	All AMD competition submissions
`successful_submissions.parquet`	AMD submissions that passed correctness tests
`deduplicated_submissions.parquet`	AMD submissions deduplicated by (user, code)
`deduplicated_successful_submissions.parquet`	Deduplicated passing AMD submissions

AMD Problems: fp8-gemm, moe (mixture of experts), mla-decode, all2all, gemm+reducescatter, allgather+gemm

NVIDIA Blackwell NVFP4 Submissions

File	Size	Description
`nvidia_nvfp4_submissions.parquet`	~1.4 GB	NVFP4 submissions deduplicated by (user, code), with full code content

Note: Scores are execution time in seconds. Lower is better.

Helper Scripts

analyze_submissions.py - Python functions for analyzing submissions
skills.md - Documentation for data processing workflows

Quick Start

from analyze_submissions import load_submissions, top_contestants, author_progression

# Load NVIDIA NVFP4 data
df = load_submissions()

# Get top 20 for a problem
leaders = top_contestants(df, problem_name='nvfp4_gemm', n=20)

# See a user's progression over time
progression = author_progression(df, user_name='username', problem_name='nvfp4_gemm')

Learn More

Competition platform: gpumode.com
Reference kernels and problem specs: github.com/gpu-mode/reference-kernels

License

This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to share and adapt the material for any purpose, even commercially, provided you give appropriate credit.

Attribution: Please cite GPU Mode and link to this dataset. For academic papers, use the citation below.

Citation

If you use this dataset in your work, please cite:

@inproceedings{
  kernelbot2025,
  title={KernelBot: A Competition Platform for Writing Heterogeneous {GPU} Code},
  author={Alex L Zhang and Matej Sirovatka and Erik Schultheis and Benjamin Horowitz and Mark Saroufim},
  booktitle={Championing Open-source DEvelopment in ML Workshop @ ICML25},
  year={2025},
  url={https://openreview.net/forum?id=bq9U4dmuyJ}
}