| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - cuda |
| - gpu-acceleration |
| - code-generation |
| - benchmark |
| - llm-evaluation |
| - sample |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: tasks |
| data_files: tasks.parquet |
| default: true |
| --- |
| |
| # AccelEval — Reviewer Sample (small split only) |
|
|
| A **representative sample** of [AccelEval](https://huggingface.co/datasets/Accel-Eval/AccelEval-data) |
| provided per NeurIPS 2026 dataset-hosting guidelines for reviewers |
| inspecting a >4 GB dataset. |
|
|
| > ⚠️ This dataset is uploaded **anonymously** to support a double-blind |
| > conference submission. Author information is intentionally omitted. |
|
|
| ## Relationship to the full dataset |
|
|
| | | Sample (this repo) | Full dataset | |
| |---|---|---| |
| | Tasks | All 42 tasks | All 42 tasks | |
| | Scales | **`small` only** | `small`, `medium`, `large` | |
| | Packed size | ~30 MB | ~12 GB | |
| | Manifest | `tasks.parquet`, `tasks.csv` (full) | identical | |
| | URL | this repo | [`Accel-Eval/AccelEval-data`](https://huggingface.co/datasets/Accel-Eval/AccelEval-data) | |
|
|
| The two repos share an identical 42-row `tasks.parquet` (the manifest is |
| not affected by which scales ship), so reviewers can browse every task's |
| metadata, full `cpu_reference.c` source, and L1/L2/L3 `prompt_template.yaml` |
| directly here without needing the larger tarballs. |
|
|
| ### How the sample was created |
|
|
| `small.tar.gz` is the unmodified `small`-scale tarball from the full |
| dataset — same SHA256, same byte layout. Specifically, every task's |
| `gen_data.py` is run with the parameters listed under `"input_sizes.small"` |
| in `tasks/<task_id>/task.json`, with the deterministic seed |
| (`seed=42`); the resulting `input.bin`, `expected_output.txt`, |
| `cpu_time_ms.txt`, and `requests.txt` files for all 42 tasks are then |
| packed under `tasks/<task_id>/data/small/...` into a single |
| gzipped tar. The same code path produces `medium.tar.gz` and |
| `large.tar.gz` in the full dataset; this sample simply omits those two |
| tarballs. |
|
|
| The `manifest.json` shipped here contains the SHA256 of `small.tar.gz`, |
| which matches the entry for `small.tar.gz` in the full dataset's |
| `manifest.json`. |
|
|
| ## What's in this repo |
|
|
| | File | Role | |
| |---|---| |
| | `tasks.parquet` | The 42-task manifest (browseable above), 13 columns. **Includes the full `cpu_reference.c` source and the LLM `prompt_template.yaml` for every task** so the benchmark is self-contained at the metadata level. | |
| | `tasks.csv` | Same content as `tasks.parquet`, plain text. | |
| | `small.tar.gz` | All 42 tasks at the *small* scale (~30 MB packed). | |
| | `manifest.json` | SHA256 of `small.tar.gz` + per-task metadata. | |
|
|
| `small.tar.gz` expands to `tasks/<task_id>/data/small/{input.bin, |
| expected_output.txt, cpu_time_ms.txt, requests.txt}`. |
| |
| ## Quick load (manifest table) |
| |
| ```python |
| import pandas as pd |
| tasks = pd.read_parquet("hf://datasets/Accel-Eval/AccelEval-sample/tasks.parquet") |
| print(tasks.head()) |
| ``` |
| |
| ## Pulling the small inputs |
| |
| ```bash |
| huggingface-cli download Accel-Eval/AccelEval-sample small.tar.gz \ |
| --repo-type dataset --local-dir . |
| tar xzf small.tar.gz # populates tasks/<task>/data/small/ |
| ``` |
| |
| Or via the companion code repo's helper: |
|
|
| ```bash |
| ACCELEVAL_HF_REPO=Accel-Eval/AccelEval-sample \ |
| python3 scripts/download_data.py small |
| ``` |
|
|
| ## Categories (same as the full dataset) |
|
|
| | Category | Count | Examples | |
| |---|---:|---| |
| | Operations research | 12 | `crew_pairing`, `network_rm_dp`, `pdlp`, `gittins_index`, ... | |
| | Graph algorithms | 7 | `bellman_ford`, `held_karp_tsp`, `gapbs_pagerank_pullgs`, ... | |
| | Spatial--temporal | 7 | `dbscan`, `dtw_distance`, `euclidean_distance_matrix`, ... | |
| | HPC reference | 7 | `hpcg_spmv_27pt`, `npb_lu_ssor_structured`, `miniWeather`, ... | |
| | Financial computing | 5 | `black_scholes`, `bonds_pricing`, `monte_carlo`, ... | |
| | Scientific simulation | 4 | `sph_cell_index`, `sph_forces`, `sph_position`, `hotspot_2d` | |
|
|
| The full row-by-row description is in `tasks.parquet`. |
|
|
| ## Determinism |
|
|
| Every task generator uses a fixed seed (`seed=42` in `gen_data.py`); the |
| tarball in this repo is byte-reproducible from the code repo's |
| `tasks/<task>/gen_data.py`. The `manifest.json` ships SHA256 hashes so |
| an independent regeneration can be verified bit-exactly. |
|
|
| ## License |
|
|
| Released under Apache-2.0. Each individual task adapts code from a |
| publicly available source (GitHub repository or published research paper); |
| the original sources retain their respective licenses, and `tasks.parquet` |
| records each source for attribution. |
|
|
