README.md

---
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/your-org/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("YOUR_ORG/ParallelKernelBench", 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("YOUR_ORG/ParallelKernelBench", "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: **ParallelKernelBench-kernels** (same org).

## Eval

```bash
python run_local.py --mode eval --problem 17 --solution cuda \
  --solutions-root path/to/solutions_dir --dtype bfloat16 --trials 5
```