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--- |
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configs: |
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- config_name: amd_submissions |
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data_files: "submissions.parquet" |
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- config_name: amd_successful_submissions |
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data_files: "successful_submissions.parquet" |
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- config_name: nvidia_nvfp4_submissions |
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data_files: "nvidia_nvfp4_submissions.parquet" |
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- config_name: leaderboards |
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data_files: "leaderboards.parquet" |
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tags: |
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- code |
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license: cc-by-4.0 |
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--- |
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# KernelBot Competition Data |
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This dataset contains GPU kernel submissions from the KernelBot competition platform. Submissions are optimized GPU kernels written for specific hardware targets. |
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## Data Files |
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### AMD MI300 Submissions |
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| File | Description | |
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|------|-------------| |
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| `submissions.parquet` | All AMD competition submissions | |
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| `successful_submissions.parquet` | AMD submissions that passed correctness tests | |
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| `deduplicated_submissions.parquet` | AMD submissions deduplicated by (user, code) | |
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| `deduplicated_successful_submissions.parquet` | Deduplicated passing AMD submissions | |
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**AMD Problems:** fp8-gemm, moe (mixture of experts), mla-decode, all2all, gemm+reducescatter, allgather+gemm |
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### NVIDIA Blackwell NVFP4 Submissions |
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| File | Size | Description | |
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|------|------|-------------| |
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| `nvidia_nvfp4_submissions.parquet` | ~1.4 GB | NVFP4 submissions deduplicated by (user, code), with full code content | |
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**NVFP4 Problems:** gemv (leaderboard 595), gemm (597), dual_gemm (598), modal_dual_gemm (697) |
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**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. |
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**Note:** Scores are execution time in seconds. **Lower is better.** |
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## Helper Scripts |
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- `analyze_submissions.py` - Python functions for analyzing submissions |
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- `skills.md` - Documentation for data processing workflows |
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### Quick Start |
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```python |
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from analyze_submissions import load_submissions, top_contestants, author_progression |
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# Load NVIDIA NVFP4 data |
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df = load_submissions() |
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# Get top 20 for a problem |
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leaders = top_contestants(df, problem_name='nvfp4_gemm', n=20) |
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# See a user's progression over time |
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progression = author_progression(df, user_name='username', problem_name='nvfp4_gemm') |
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``` |
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## Learn More |
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- Competition platform: [gpumode.com](https://gpumode.com) |
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- Reference kernels and problem specs: [github.com/gpu-mode/reference-kernels](https://github.com/gpu-mode/reference-kernels) |
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## License |
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This dataset is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). |
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You are free to share and adapt the material for any purpose, even commercially, provided you give appropriate credit. |
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**Attribution:** Please cite GPU Mode and link to this dataset. For academic papers, use the citation below. |
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## Citation |
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If you use this dataset in your work, please cite: |
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```bibtex |
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@inproceedings{ |
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kernelbot2025, |
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title={KernelBot: A Competition Platform for Writing Heterogeneous {GPU} Code}, |
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author={Alex L Zhang and Matej Sirovatka and Erik Schultheis and Benjamin Horowitz and Mark Saroufim}, |
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booktitle={Championing Open-source DEvelopment in ML Workshop @ ICML25}, |
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year={2025}, |
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url={https://openreview.net/forum?id=bq9U4dmuyJ} |
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} |
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``` |
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