GPUMODE/kernelbot-data

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

NVFP4 Problems: gemv (leaderboard 595), gemm (597), dual_gemm (598), modal_dual_gemm (697)

Note on Dual GEMM: There are two variants of the dual_gemm problem. Midway through the competition, on-prem hardware measurements became unreliable, so a second leaderboard was created on Modal infrastructure. The Modal measurements (leaderboard 697, modal_nvfp4_dual_gemm) are more trustworthy.

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}
}