| --- |
| license: other |
| license_name: sdrbench |
| license_link: https://sdrbench.github.io/ |
| pretty_name: "SDRBench: S3D" |
| tags: |
| - scientific-data |
| - lossy-compression |
| - sdrbench |
| - hpc |
| - sz3 |
| configs: |
| - config_name: files |
| data_files: files.jsonl |
| default: true |
| --- |
| |
| # SDRBench — S3D |
|
|
| Statistically planar premixed methane flame simulated with S3D (SNL): 11 variables on 500 x 500 x 500, double precision, little-endian. |
|
|
| > **Raw binary arrays, not an Arrow/Parquet dataset.** `datasets.load_dataset("sdrbench/s3d")` 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 S3D 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 `e1229e5a46`). `metadata/` holds |
| the original SDRBench property and template files. Kept in sync automatically by |
| [szcompressor/sdrbench](https://github.com/szcompressor/sdrbench). |
| |
| - **Data provider:** Hemanth Kolla (SNL, hnkolla@sandia.gov) |
| - **Size:** 55.0 GB in 1 variant(s) |
| - **Format:** raw little-endian binary, C order (slowest dimension first); dtype and shape per field below |
| |
| ## 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("s3d") |
| print(ds.fields) |
| x = ds["CH4/1.1000E-03"] # numpy array, dtype <f8, shape (500, 500, 500) |
| |
| # 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("s3d", 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/s3d", "s3d/stat_planar.1.1000E-03.field.d64", repo_type="dataset", revision="e1229e5a4606482948fd3e8272a1dd1ab645dc7e") |
| x = np.fromfile(p, dtype="<f8").reshape((11, 500, 500, 500))[0] # CH4 -> (500, 500, 500) |
| ``` |
| |
| ## Variants and fields |
| |
| |
| ### `default` (default) |
| |
| `sdrbench.dataset("s3d", "default")` — folder `s3d/` — 7 files, 55.0 GB, from [SDRBENCH-S3D.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/S3D/SDRBENCH-S3D.tar.gz). |
| |
| 5 time steps (t = 1.1e-3 ... 2.995e-3 s). Each stored file holds all 11 variables (variable x z x y x x); fields are zero-copy views per variable: mass fractions CH4, O2, CO, CO2, H2O, N2; temperature T (normalized by 120 K), pressure P (by 1.41837e5 Pa), velocities U, V, W (by 347.2 m/s). |
| |
| 11 variable(s) x 5 steps (1.1000E-03 ... 2.9950E-03): `ds.series("CH4")`, `ds["CH4", "1.1000E-03"]`. |
| |
| | Field | dtype | Shape (C order) | File | |
| |---|---|---|---| |
| | `CH4/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [0] | |
| | `O2/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [1] | |
| | `CO/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [2] | |
| | `CO2/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [3] | |
| | `H2O/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [4] | |
| | `N2/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [5] | |
| | `T/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [6] | |
| | `P/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [7] | |
| | `U/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [8] | |
| | `V/1.1000E-03` | `<f8` | 500 x 500 x 500 | `s3d/stat_planar.1.1000E-03.field.d64` [9] | |
| | ... 45 more | | | | |
| |
| Other files: `s3d/flist.txt`, `s3d/template.txt` |
| |
| ## Citation |
| |
| Please cite SDRBench; `sdrbench cite s3d` 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. |
| |
