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 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 |
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)
import pandas as pd
tasks = pd.read_parquet("hf://datasets/Accel-Eval/AccelEval-sample/tasks.parquet")
print(tasks.head())
Pulling the small inputs
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:
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.