| --- |
| license: other |
| license_name: sdrbench |
| license_link: https://sdrbench.github.io/ |
| pretty_name: "SDRBench: QMCPACK" |
| tags: |
| - scientific-data |
| - lossy-compression |
| - sdrbench |
| - hpc |
| - sz3 |
| configs: |
| - config_name: files |
| data_files: files.jsonl |
| default: true |
| --- |
| |
| # SDRBench — QMCPACK |
|
|
| Many-body ab initio Quantum Monte Carlo (QMCPACK): 288 einspline orbitals on a 115 x 69 x 69 grid, single precision, little-endian. |
|
|
| > **Raw binary arrays, not an Arrow/Parquet dataset.** `datasets.load_dataset("sdrbench/qmcpack")` 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 QMCPACK 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 `1518533bae`). `metadata/` holds |
| the original SDRBench property and template files. Kept in sync automatically by |
| [szcompressor/sdrbench](https://github.com/szcompressor/sdrbench). |
| |
| - **Data provider:** QMCPACK performance test; contact Ye Luo (yeluo@anl.gov) |
| - **Size:** 630.7 MB in 2 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("qmcpack") # default variant "preconditioned" |
| print(ds.fields) |
| x = ds["einspline"] # numpy array, dtype <f4, shape (288, 115, 69, 69) |
| |
| # 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("qmcpack", 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/qmcpack", "115x69x69x288/einspline_115_69_69_288.f32", repo_type="dataset", revision="1518533baeeffa9dad33910ff639029bfac91f88") |
| x = np.fromfile(p, dtype="<f4").reshape((115, 69, 69, 288)).transpose(3, 0, 1, 2) # -> (288, 115, 69, 69) |
| ``` |
| |
| ## Variants and fields |
| |
| |
| ### `preconditioned` (default) |
| |
| `sdrbench.dataset("qmcpack", "preconditioned")` — derived from `115x69x69x288/`, no extra files. |
| |
| (orbital, x, y, z) = (288, 115, 69, 69): each orbital is a contiguous, smooth 3D block. This is the layout of the SDRBench command examples (einspline_288_115_69_69.pre.f32); computed on load from the stored file. |
| |
| | Field | dtype | Shape (C order) | File | |
| |---|---|---|---| |
| | `einspline` | `<f4` | 288 x 115 x 69 x 69 | `115x69x69x288/einspline_115_69_69_288.f32` | |
| |
| ### `original` |
| |
| `sdrbench.dataset("qmcpack", "original")` — folder `115x69x69x288/` — 3 files, 630.7 MB, from [SDRBENCH-QMCPack.tar.gz](https://g-d0cd3f.fd635.8443.data.globus.org/raw-data/QMCPACK/SDRBENCH-QMCPack.tar.gz). |
| |
| The layout QMCPACK uses in memory: (x, y, z, orbital) = (115, 69, 69, 288), orbital index fastest. |
| |
| | Field | dtype | Shape (C order) | File | |
| |---|---|---|---| |
| | `einspline` | `<f4` | 115 x 69 x 69 x 288 | `115x69x69x288/einspline_115_69_69_288.f32` | |
| |
| Other files: `115x69x69x288/SDRBENCH-QMCPACK-115x69x69x288-property.txt`, `115x69x69x288/template_data.txt` |
| |
| ## Citation |
| |
| Please cite SDRBench; `sdrbench cite qmcpack` 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. |
| |
