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---
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 |
**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
```python
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](https://gpumode.com)
- Reference kernels and problem specs: [github.com/gpu-mode/reference-kernels](https://github.com/gpu-mode/reference-kernels)
## License
This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/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:
```bibtex
@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}
}
```
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