| --- |
| license: apache-2.0 |
| task_categories: |
| - text-generation |
| tags: |
| - code |
| - cuda |
| - distributed-systems |
| - gpu-kernels |
| - benchmark |
| size_categories: |
| - n<1K |
| --- |
| |
| # ParallelKernelBench (benchmark) |
|
|
| Reference problems for [ParallelKernelBench](https://github.com/togethercomputer/ParallelKernelBench): a benchmark for LLM-generated multi-GPU CUDA kernels. |
|
|
| This dataset contains **87** reference implementations in `reference/` and the input tensor specification in `utils/input_output_tensors.py`. Inputs are **deterministic** — reproduce them with `create_input_tensor(rank, world_size, problem_id, base_shape, dtype, trial)` from that file; you do not need stored `.pt` files. |
|
|
| ## Files |
|
|
| | Path | Description | |
| |------|-------------| |
| | `data/problems.parquet` | One row per problem (tabular access) | |
| | `reference/*.py` | Reference `solution()` implementations | |
| | `utils/input_output_tensors.py` | Input/output tensor generation for every problem | |
|
|
| ## Columns (`data/problems.parquet`) |
|
|
| - `problem_id`, `stem` — problem identity |
| - `reference_code` — full Python source |
| - `reference_path` — path to the same file in this repo |
| - `input_tensor_spec_path` — path to `utils/input_output_tensors.py` (same on every row) |
| - `world_size`, `default_m`, `default_n`, `default_dtype`, `default_trials` — default eval settings (8× H100, 1024×1024, bfloat16, 5 trials) |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| from huggingface_hub import hf_hub_download |
| |
| ds = load_dataset("togethercomputer/ParallelKernelBench_Problems", split="train") |
| print(ds[0]["stem"], ds[0]["reference_code"][:200]) |
| |
| # Fetch the input tensor spec (same file on disk in this dataset repo) |
| spec_path = hf_hub_download("togethercomputer/ParallelKernelBench_Problems", "utils/input_output_tensors.py", repo_type="dataset") |
| ``` |
|
|
| Reproduce inputs locally (add the downloaded `utils/` folder to PYTHONPATH, or clone this repo): |
|
|
| ```python |
| from utils.input_output_tensors import create_input_tensor |
| import torch |
| |
| x = create_input_tensor( |
| rank=0, world_size=8, problem_id=17, |
| base_shape=(1024, 1024), dtype=torch.bfloat16, |
| ) |
| ``` |
|
|
| ## Related |
|
|
| Net-new LLM-generated kernels live in a separate dataset repo containing only `solutions/<run_id>/*.py`. |
|
|
| ## Eval |
|
|
| ```bash |
| python run_local.py --mode eval --problem 17 --solution cuda \ |
| --solutions-root path/to/solutions_dir --dtype bfloat16 --trials 5 |
| ``` |
|
|
