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README.md

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+---
+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.