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, mxfp4-mm, moe-mxfp4, mixed-mla

AMD 1.1M Competition

File	Size	Description
amd_1_1m_competition_submissions.parquet	~699 MB	Deduplicated submissions with code for amd-mxfp4-mm (763), amd-moe-mxfp4 (764), and amd-mixed-mla (765)

Trimul

File	Size	Description
trimul_submissions.parquet	~120 MB	Deduplicated submissions with code for trimul (leaderboard 496)

trimul is a separate mixed-GPU problem and is not grouped with the AMD competition exports.

Helion B200_Nebius

File	Size	Description
helion_b200_nebius_submissions.parquet	~4 MB	Deduplicated submissions with code for causal_conv1d (766), fp8_quant (767), gated_deltanet_chunk_fwd_h (768), gated_deltanet_chunk_fwd_o (769), and gated_deltanet_recompute_w_u (770)

Measurement note: these problems were run on B200_Nebius, and the measurements for this problem set are brittle. Treat leaderboard scores from this export with extra caution.

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), group_gemm (730)

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. If for whatever reason you cannot give appropriate credit then please reach out to marksaroufim@gmail.com to discuss other arrangements.

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}
}