| --- |
| license: agpl-3.0 |
| language: |
| - en |
| task_categories: |
| - other |
| pretty_name: Science KernelBench |
| tags: |
| - kernelbench |
| - gpu |
| - cuda |
| - triton |
| - scientific-computing |
| - pde |
| - kernel-methods |
| configs: |
| - config_name: default |
| data_files: |
| - split: level_1 |
| path: data/level_1-* |
| - split: level_2 |
| path: data/level_2-* |
| - split: level_3 |
| path: data/level_3-* |
| --- |
| |
| # Science KernelBench |
|
|
| A science-focused task set for `robust-kernelbench`. Each task provides a |
| PyTorch reference implementation of a numerical primitive; the candidate must |
| produce a faster, numerically equivalent GPU kernel. |
|
|
| This directory is the **source of truth**. `to_hf.py` expands the manifest into |
| a Hugging Face dataset; the generated export is not committed. |
|
|
| Current starter set: **22 tasks** across physics, chemistry, biology, |
| statistics, mathematics and economics. |
|
|
| | level | focus | tasks | |
| | --- | --- | --- | |
| | `level_1` | single stencil, reduction or short elementwise expression | 8 | |
| | `level_2` | all-pairs interactions, batched linear algebra, multi-state updates | 7 | |
| | `level_3` | sequential dynamic programs, FFTs, tree backward induction | 7 | |
|
|
| Each task carries `domain`, `subdomain`, `level`, `tags` and a `source` block. |
| The reference files contain a docstring with the domain, the problem solved and |
| the canonical paper/source. |
|
|
| ### Task list |
|
|
| | id | task | domain | level | |
| | --- | --- | --- | --- | |
| | 101 | `pairwise_rbf` | statistics | 1 | |
| | 102 | `laplacian_2d` | physics | 1 | |
| | 103 | `heat_step_2d` | physics | 1 | |
| | 104 | `lennard_jones_energy` | chemistry | 1 | |
| | 105 | `lotka_volterra_step` | biology | 1 | |
| | 106 | `gaussian_kde` | statistics | 1 | |
| | 107 | `horner_eval` | mathematics | 1 | |
| | 108 | `black_scholes_call` | economics | 1 | |
| | 201 | `nbody_acceleration` | physics | 2 | |
| | 202 | `coulomb_energy` | chemistry | 2 | |
| | 203 | `hodgkin_huxley_step` | biology | 2 | |
| | 204 | `kalman_update` | statistics | 2 | |
| | 205 | `graph_laplacian` | mathematics | 2 | |
| | 206 | `portfolio_variance` | economics | 2 | |
| | 207 | `wave_step_2d` | physics | 2 | |
| | 301 | `needleman_wunsch_score` | biology | 3 | |
| | 302 | `fft_convolution` | mathematics | 3 | |
| | 303 | `binomial_option_crr` | economics | 3 | |
| | 304 | `split_step_schrodinger` | physics | 3 | |
| | 305 | `ewald_real_space` | chemistry | 3 | |
| | 306 | `gaussian_process_posterior` | statistics | 3 | |
| | 307 | `floyd_warshall` | mathematics | 3 | |
|
|
|
|
| ## Versioning |
|
|
| Current release: **0.1.0** (`schema_version: kernelbench-v1`), compatible with |
| `robust-kernelbench >= 0.2.0`. |
|
|
| The dataset version is independent of the package version: |
|
|
| - `PATCH` — documentation, metadata or provenance fixes with no scoring change. |
| - `MINOR` — new tasks, domains or levels; existing tasks unchanged. |
| - `MAJOR` — removal/renumbering, reference-output changes, or benchmark-split |
| changes. |
|
|
| Every task has a permanent `problem_id`, a stable `task_key`, a `task_version` |
| and a `reference_hash`. Removed tasks are marked `retired`; IDs are never reused. |
| Pin production runs to an HF tag or commit: |
|
|
| ```bash |
| robust-kernelbench \ |
| --dataset ai-nikolai/ScienceKernelBench \ |
| --dataset_revision v0.1.0 \ |
| --dataset_split level_1 \ |
| ... |
| ``` |
|
|
| ## Schema |
|
|
| Required columns: |
|
|
| | column | type | meaning | |
| | --- | --- | --- | |
| | `problem_id` | int | unique task id | |
| | `name` | string | filesystem-safe task name | |
| | `code` | string | self-contained reference source | |
|
|
| The `code` must define: |
|
|
| ```python |
| class Model(nn.Module): ... |
| def get_inputs(): ... |
| def get_init_inputs(): ... |
| ``` |
|
|
| Optional columns understood by the harness: |
|
|
| | column | meaning | |
| | --- | --- | |
| | `level` | dataset split / difficulty grouping (1-3 here) | |
| | `domain` | physics, chemistry, biology, statistics, mathematics, economics | |
| | `subdomain` | finer label, e.g. `molecular dynamics`, `PDE`, `bioinformatics` | |
| | `filter` | include in the representative subset | |
| | `task_type` | `pytorch_reference` or `kernel_reference` | |
| | `backend` | `cuda`, `triton`, ... | |
| | `precision` | `fp32`, `fp16`, `bf16` | |
| | `tags` | searchable labels | |
| | `source` | provenance / license metadata | |
|
|
| ## Local layout |
|
|
| ```text |
| data/science_kernels/ |
| ├── README.md # this dataset card |
| ├── manifest.json # versioned task list and provenance |
| ├── CHANGELOG.md # dataset release history |
| ├── requirements.txt # minimal HF/export dependencies |
| ├── kernels/ |
| │ ├── level_1/ # 8 tasks |
| │ ├── level_2/ # 7 tasks |
| │ └── level_3/ # 7 tasks |
| └── to_hf.py # per-level export / optional push_to_hub |
| ``` |
|
|
| ## Minimal dependencies |
|
|
| ```bash |
| pip install -r requirements.txt |
| ``` |
|
|
| `torch` is optional and only needed for `--execute` smoke tests; it is listed as |
| a comment in the requirements file so the dataset can be loaded and exported on |
| a lightweight machine. |
|
|
| ## Validate locally |
|
|
| ```bash |
| # Schema only |
| robust-kernelbench-dataset validate \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory |
| |
| # Counts by level and domain, and a browsable list |
| robust-kernelbench-dataset stats \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory |
| robust-kernelbench-dataset list \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory --domain physics |
| |
| # Also run one forward pass per task |
| robust-kernelbench-dataset validate \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory \ |
| --execute --device cpu |
| ``` |
|
|
| ## Use with robust-kernelbench |
|
|
| Evaluation only: |
|
|
| ```bash |
| robust-kernelbench-eval \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory \ |
| --dataset_split level_1 \ |
| --experiment science_exp --trial 1 |
| ``` |
|
|
| Agentic generate -> compile -> evaluate: |
|
|
| ```bash |
| robust-kernelbench \ |
| --dataset_path data/science_kernels \ |
| --dataset_format directory \ |
| --dataset_split level_1 \ |
| --dataset_name science-kernels \ |
| --version science_v0 \ |
| --model_name Qwen/Qwen3-Coder-30B-A3B-Instruct \ |
| "1 1 kernelbench" |
| ``` |
|
|
| ## Export for Hugging Face |
|
|
| ```bash |
| python data/science_kernels/to_hf.py --output /tmp/science_kernels_hf |
| |
| # huggingface_hub >= 1 uses the `hf` command: |
| hf upload ai-nikolai/ScienceKernelBench /tmp/science_kernels_hf --repo-type dataset |
| |
| # Works with any huggingface_hub version: |
| python data/science_kernels/upload_to_hf.py \ |
| --path /tmp/science_kernels_hf |
| ``` |
|
|
| Or export and push in one step: |
|
|
| ```bash |
| python data/science_kernels/to_hf.py \ |
| --output /tmp/science_kernels_hf \ |
| --push |
| ``` |
|
|
| ## Provenance and licensing |
|
|
| Only add kernels you have the right to redistribute. Record the source |
| repository, commit, and license in each task's `source` block. Original tasks |
| written for this dataset are released under AGPL-3.0, matching the repository. |
| If you combine sources with different licenses, use the strictest compatible |
| license for the combined dataset and document it in this card. |
|
|
|
|
| ## Citation |
|
|
| If you use this dataset, please cite `robust-kernelbench`: |
|
|
| ```bibtex |
| @misc{rozanov2026robustkernelbench, |
| title = {Robust KernelBench: A Robust Harness for GPU Kernel Generation and Evaluation}, |
| author = {Rozanov, Nikolai}, |
| year = {2026}, |
| howpublished = {\url{https://github.com/ai-nikolai/robust_kernelbench}}, |
| note = {ScienceKernelBench dataset, version 0.1.0} |
| } |
| ``` |
|
|
| The dataset card and version should also be cited when reporting benchmark |
| results, together with the exact `dataset_revision` used. |
|
|
