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---
license: other
license_name: sdrbench
license_link: https://sdrbench.github.io/
pretty_name: "SDRBench: HACC"
tags:
- scientific-data
- lossy-compression
- sdrbench
- hpc
- sz3
configs:
- config_name: files
data_files: files.jsonl
default: true
---
# SDRBench — HACC
Cosmology N-body simulation (HACC, ECP ExaSky): particle coordinates xx, yy, zz and velocities vx, vy, vz as separate 1D arrays, single precision, little-endian.
> **Raw binary arrays, not an Arrow/Parquet dataset.** `datasets.load_dataset("sdrbench/hacc")` returns the table of files (`files.jsonl`: path, dtype, shape, sha256). Read the arrays with the `sdrbench` package or `hf_hub_download` + numpy as shown below.
This repository is an **unmodified mirror** of the HACC data of [SDRBench](https://sdrbench.github.io/datasets.html), the
Scientific Data Reduction Benchmark. The originals are hosted by Argonne National Laboratory on Globus;
every archive was unpacked and its files uploaded byte-for-byte (sha256 verified) (revision `36891dee00`). `metadata/` holds
the original SDRBench property and template files. Kept in sync automatically by
[szcompressor/sdrbench](https://github.com/szcompressor/sdrbench).
- **Data provider:** HACC team (ECP ExaSky); https://www.alcf.anl.gov/science/case-studies/opencosmo-community-portal-cosmological-simulations
- **Size:** 150.2 GB in 8 variant(s)
- **Format:** raw little-endian binary, C order (slowest dimension first); dtype and shape per field below
**Citation requested by the data provider:** Please cite Heitmann et al., Astrophys. J. Suppl. 252 (2021) 2, 19 (https://inspirehep.net/literature/1799008) when using HACC data.
## Usage
```bash
pip install "sdrbench[sz3]" # sdrbench + pysz (SZ3)
```
```python
import numpy as np
import sdrbench
from pysz import sz, szConfig, szErrorBoundMode
ds = sdrbench.dataset("hacc") # default variant "medium"
print(ds.fields)
x = ds["vx"] # numpy array, dtype <f4, shape (280953867,)
# compress with SZ3 (pysz) at a 1e-3 value-range-relative error bound
conf = szConfig()
conf.errorBoundMode = szErrorBoundMode.REL
conf.relErrorBound = 1e-3
compressed, ratio = sz.compress(np.ascontiguousarray(x), conf)
y, _ = sz.decompress(compressed, x.dtype.type, x.shape)
max_err, psnr, nrmse = sz.verify(np.asarray(x), y)
print(f"ratio {ratio:.1f}x, PSNR {psnr:.1f} dB, max error {max_err:.3g}")
```
Files are fetched on first use (in parallel with `ds.download("data/")`) and cached, pinned to the revision
of the installed `sdrbench` release; if Hugging Face is unreachable the package falls back to the original
archive on Globus. Existing local SDRBench copies can be used with `sdrbench.dataset("hacc", root="/path")`.
Without the package, with `huggingface_hub` and numpy only:
```python
from huggingface_hub import hf_hub_download
import numpy as np
p = hf_hub_download("sdrbench/hacc", "medium/vx.f32", repo_type="dataset", revision="36891dee003a1aa08bb7da3c3e6b90badcd661e6")
x = np.fromfile(p, dtype="<f4").reshape((280953867,))
```
## Variants and fields
### `medium` (default)
`sdrbench.dataset("hacc", "medium")` — folder `medium/` — 8 files, 6.7 GB, from [EXASKY-HACC-data-medium-size.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/EXASKY-HACC-data-medium-size.tar.gz).
One snapshot with 280,953,867 particles.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `vx` | `<f4` | 280953867 | `medium/vx.f32` |
| `vy` | `<f4` | 280953867 | `medium/vy.f32` |
| `vz` | `<f4` | 280953867 | `medium/vz.f32` |
| `xx` | `<f4` | 280953867 | `medium/xx.f32` |
| `yy` | `<f4` | 280953867 | `medium/yy.f32` |
| `zz` | `<f4` | 280953867 | `medium/zz.f32` |
Other files: `medium/EXASKY-HACC-data-medium-size-property.txt`, `medium/EXASKY-HACC-data-medium-size-template_data.txt`
### `big`
`sdrbench.dataset("hacc", "big")` — folder `big/` — 8 files, 25.8 GB, from [EXASKY-HACC-data-big-size.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/EXASKY-HACC-data-big-size.tar.gz).
One snapshot with 1,073,726,487 particles.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `vx` | `<f4` | 1073726487 | `big/vx.f32` |
| `vy` | `<f4` | 1073726487 | `big/vy.f32` |
| `vz` | `<f4` | 1073726487 | `big/vz.f32` |
| `xx` | `<f4` | 1073726487 | `big/xx.f32` |
| `yy` | `<f4` | 1073726487 | `big/yy.f32` |
| `zz` | `<f4` | 1073726487 | `big/zz.f32` |
Other files: `big/EXASKY-HACC-data-big-size-property.txt`, `big/template_data_big-size.txt`
### `sbig`
`sdrbench.dataset("hacc", "sbig")` — folder `sbig/` — 3 files, 19.5 GB, from [SDRBENCH-EXASKY-HACC-data-sbig-size.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/SDRBENCH-EXASKY-HACC-data-sbig-size.tar.gz).
One region (1/768) of time step 499 of a trillion-particle run: coordinates only.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx` | `<f4` | 1621417608 | `sbig/27035-xx.f32` |
| `yy` | `<f4` | 1621417608 | `sbig/27035-yy.f32` |
| `zz` | `<f4` | 1621417608 | `sbig/27035-zz.f32` |
### `region1`
`sdrbench.dataset("hacc", "region1")` — folder `multi-timesteps/1840/` — 304 files, 19.5 GB, from [1840.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/multi-timesteps/1840.tar.gz).
Region 1840: coordinates xx, yy, zz over about 100 time steps (the particle count changes per step) and the particle IDs.
4 variable(s) x 101 steps (00042 ... 00499): `ds.series("xx")`, `ds["xx", "00042"]`.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx/00042` | `<f4` | 16141859 | `multi-timesteps/1840/00042-xx.f32` |
| `yy/00042` | `<f4` | 16141859 | `multi-timesteps/1840/00042-yy.f32` |
| `zz/00042` | `<f4` | 16141859 | `multi-timesteps/1840/00042-zz.f32` |
| `xx/00043` | `<f4` | 16140933 | `multi-timesteps/1840/00043-xx.f32` |
| `yy/00043` | `<f4` | 16140933 | `multi-timesteps/1840/00043-yy.f32` |
| `zz/00043` | `<f4` | 16140933 | `multi-timesteps/1840/00043-zz.f32` |
| `xx/00044` | `<f4` | 16139797 | `multi-timesteps/1840/00044-xx.f32` |
| `yy/00044` | `<f4` | 16139797 | `multi-timesteps/1840/00044-yy.f32` |
| `zz/00044` | `<f4` | 16139797 | `multi-timesteps/1840/00044-zz.f32` |
| `xx/00045` | `<f4` | 16138878 | `multi-timesteps/1840/00045-xx.f32` |
| ... 294 more | | | |
### `region2`
`sdrbench.dataset("hacc", "region2")` — folder `multi-timesteps/20000/` — 304 files, 19.6 GB, from [20000.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/multi-timesteps/20000.tar.gz).
Region 20000: coordinates xx, yy, zz over about 100 time steps (the particle count changes per step) and the particle IDs.
4 variable(s) x 101 steps (00042 ... 00499): `ds.series("xx")`, `ds["xx", "00042"]`.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx/00042` | `<f4` | 16153547 | `multi-timesteps/20000/00042-xx.f32` |
| `yy/00042` | `<f4` | 16153547 | `multi-timesteps/20000/00042-yy.f32` |
| `zz/00042` | `<f4` | 16153547 | `multi-timesteps/20000/00042-zz.f32` |
| `xx/00043` | `<f4` | 16152817 | `multi-timesteps/20000/00043-xx.f32` |
| `yy/00043` | `<f4` | 16152817 | `multi-timesteps/20000/00043-yy.f32` |
| `zz/00043` | `<f4` | 16152817 | `multi-timesteps/20000/00043-zz.f32` |
| `xx/00044` | `<f4` | 16152061 | `multi-timesteps/20000/00044-xx.f32` |
| `yy/00044` | `<f4` | 16152061 | `multi-timesteps/20000/00044-yy.f32` |
| `zz/00044` | `<f4` | 16152061 | `multi-timesteps/20000/00044-zz.f32` |
| `xx/00045` | `<f4` | 16151268 | `multi-timesteps/20000/00045-xx.f32` |
| ... 294 more | | | |
### `region3`
`sdrbench.dataset("hacc", "region3")` — folder `multi-timesteps/24120/` — 304 files, 19.6 GB, from [24120.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/multi-timesteps/24120.tar.gz).
Region 24120: coordinates xx, yy, zz over about 100 time steps (the particle count changes per step) and the particle IDs.
4 variable(s) x 101 steps (00042 ... 00499): `ds.series("xx")`, `ds["xx", "00042"]`.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx/00042` | `<f4` | 16156722 | `multi-timesteps/24120/00042-xx.f32` |
| `yy/00042` | `<f4` | 16156722 | `multi-timesteps/24120/00042-yy.f32` |
| `zz/00042` | `<f4` | 16156722 | `multi-timesteps/24120/00042-zz.f32` |
| `xx/00043` | `<f4` | 16156136 | `multi-timesteps/24120/00043-xx.f32` |
| `yy/00043` | `<f4` | 16156136 | `multi-timesteps/24120/00043-yy.f32` |
| `zz/00043` | `<f4` | 16156136 | `multi-timesteps/24120/00043-zz.f32` |
| `xx/00044` | `<f4` | 16155596 | `multi-timesteps/24120/00044-xx.f32` |
| `yy/00044` | `<f4` | 16155596 | `multi-timesteps/24120/00044-yy.f32` |
| `zz/00044` | `<f4` | 16155596 | `multi-timesteps/24120/00044-zz.f32` |
| `xx/00045` | `<f4` | 16154795 | `multi-timesteps/24120/00045-xx.f32` |
| ... 294 more | | | |
### `region4`
`sdrbench.dataset("hacc", "region4")` — folder `multi-timesteps/30210/` — 304 files, 19.6 GB, from [30210.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/multi-timesteps/30210.tar.gz).
Region 30210: coordinates xx, yy, zz over about 100 time steps (the particle count changes per step) and the particle IDs.
4 variable(s) x 101 steps (00042 ... 00499): `ds.series("xx")`, `ds["xx", "00042"]`.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx/00042` | `<f4` | 16160806 | `multi-timesteps/30210/00042-xx.f32` |
| `yy/00042` | `<f4` | 16160806 | `multi-timesteps/30210/00042-yy.f32` |
| `zz/00042` | `<f4` | 16160806 | `multi-timesteps/30210/00042-zz.f32` |
| `xx/00043` | `<f4` | 16160342 | `multi-timesteps/30210/00043-xx.f32` |
| `yy/00043` | `<f4` | 16160342 | `multi-timesteps/30210/00043-yy.f32` |
| `zz/00043` | `<f4` | 16160342 | `multi-timesteps/30210/00043-zz.f32` |
| `xx/00044` | `<f4` | 16159646 | `multi-timesteps/30210/00044-xx.f32` |
| `yy/00044` | `<f4` | 16159646 | `multi-timesteps/30210/00044-yy.f32` |
| `zz/00044` | `<f4` | 16159646 | `multi-timesteps/30210/00044-zz.f32` |
| `xx/00045` | `<f4` | 16158914 | `multi-timesteps/30210/00045-xx.f32` |
| ... 294 more | | | |
### `region6`
`sdrbench.dataset("hacc", "region6")` — folder `multi-timesteps/4529/` — 304 files, 19.9 GB, from [4529.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/EXASKY/HACC/multi-timesteps/4529.tar.gz).
Region 4529: coordinates xx, yy, zz over about 100 time steps (the particle count changes per step) and the particle IDs.
4 variable(s) x 101 steps (00042 ... 00499): `ds.series("xx")`, `ds["xx", "00042"]`.
| Field | dtype | Shape (C order) | File |
|---|---|---|---|
| `xx/00042` | `<f4` | 16211251 | `multi-timesteps/4529/00042-xx.f32` |
| `yy/00042` | `<f4` | 16211251 | `multi-timesteps/4529/00042-yy.f32` |
| `zz/00042` | `<f4` | 16211251 | `multi-timesteps/4529/00042-zz.f32` |
| `xx/00043` | `<f4` | 16211574 | `multi-timesteps/4529/00043-xx.f32` |
| `yy/00043` | `<f4` | 16211574 | `multi-timesteps/4529/00043-yy.f32` |
| `zz/00043` | `<f4` | 16211574 | `multi-timesteps/4529/00043-zz.f32` |
| `xx/00044` | `<f4` | 16212106 | `multi-timesteps/4529/00044-xx.f32` |
| `yy/00044` | `<f4` | 16212106 | `multi-timesteps/4529/00044-yy.f32` |
| `zz/00044` | `<f4` | 16212106 | `multi-timesteps/4529/00044-zz.f32` |
| `xx/00045` | `<f4` | 16212596 | `multi-timesteps/4529/00045-xx.f32` |
| ... 294 more | | | |
## Citation
Please cite SDRBench and the data provider (see above); `sdrbench cite hacc` prints it:
```bibtex
@inproceedings{zhao2020sdrbench,
title = {{SDRBench}: Scientific Data Reduction Benchmark for Lossy Compressors},
author = {Zhao, Kai and Di, Sheng and Liang, Xin and Li, Sihuan and Tao, Dingwen and Chen, Zizhong and Cappello, Franck},
booktitle = {2020 IEEE International Conference on Big Data (Big Data)},
pages = {2716--2724},
year = {2020}
}
```
## License and terms
The data are distributed by the SDRBench team for research use under the terms of the original
data providers listed above (see the [SDRBench website](https://sdrbench.github.io/datasets.html)); please acknowledge them as
requested. Rights remain with the data providers.