---
configs:
- config_name: amd_submissions
  data_files: "submissions.parquet"
- config_name: amd_successful_submissions
  data_files: "successful_submissions.parquet"
- config_name: amd_1_1m_competition
  data_files: "amd_1_1m_competition_submissions.parquet"
- config_name: helion_b200_nebius
  data_files: "helion_b200_nebius_submissions.parquet"
- config_name: trimul_submissions
  data_files: "trimul_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, 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

```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. 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:

```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}
}
```