Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 84, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ml_cpu_prism
Data and code for research on machine-learning-based CPU performance (CPI) modeling with multi-stage CPI stacks (MSCS), trained and validated against the gem5 O3 cycle-level simulator.
Everything is stored as tar.zst shards. To extract one:
hf download David1515/ml_cpu_prism --repo-type dataset \
--include "micro_exp/dse_exp/traces/602.gcc_s.tar.zst" --local-dir ./data
zstd -dc data/micro_exp/dse_exp/traces/602.gcc_s.tar.zst | tar -xf -
Each shard unpacks to the same relative path it had in the original working tree
(micro_exp/... shards unpack relative to micro_exp/; micro_exp/dse_exp/traces/* relative to
micro_exp/dse_exp/traces/, and so on).
Using it with the code on GitHub
The code is in github.com/davidli1515/prism_1111
under gem5-TAO/. Its fetch_data.sh downloads shards from this dataset and unpacks each one
into the right place, which recreates the original working tree:
git clone https://github.com/davidli1515/prism_1111.git
cd prism_1111/gem5-TAO
./fetch_data.sh # everything, ~130 GB
./fetch_data.sh "micro_exp/exp1.tar.zst" "train_data/*" # or selected shards
Shards under micro_exp/ unpack into the matching directory of gem5-TAO/micro_exp/;
train_data/ and micro_ckpt/ unpack into a separate data directory (default data/). See
gem5-TAO/DATA_ON_HUGGINGFACE.md on GitHub for the full mapping.
Layout
| Path | Contents |
|---|---|
code/gem5-TAO-src.tar.zst |
Modified gem5 source tree (MSCS accounting in src/cpu/o3/mscs.{cc,hh}, trace hooks in O3/Minor/Simple CPUs and caches), data-generation scripts, external cache/branch-predictor simulator, Concorde analytical model |
micro_exp/dse_exp/traces/<benchmark>.tar.zst |
Per-slice gem5 O3 pipeline traces (slice_N/o3trace.txt) plus cache/TAGE warm-up checkpoints, ~400 slices per benchmark |
micro_exp/dse_exp/precompute/*.tar.zst |
Precomputed feature arrays (dse_precomputed.npy) per benchmark and per L1 size (l1_16 ... l1_256) |
micro_exp/dse_exp/_rest.tar.zst |
Design-space-exploration scripts and results |
micro_exp/<experiment>.tar.zst |
Training/evaluation scripts, data, logs, results and model checkpoints for each experiment |
train_data/micro_traindata_packed.tar.zst |
Packed per-benchmark training arrays used by the CPI models: concord/{benchmark}.npy (N x 3874: 3873 Concorde features + CPI label) and gem/{benchmark}.npy (N x 4697: Concorde features + 808 pressure features + 16-dim MSCS target) |
train_data/micro_traindata_packed_all.tar.zst |
Extended packed training arrays (gem/) |
| micro_ckpt/<benchmark>.tar.zst | Per-SimPoint gem5 run directories (<tick>/): simpletrace.txt instruction traces, stats.txt, config.ini/json, logs. The checkpoints themselves (cpt.<tick>/, containing SPEC memory images) are not included, see below |
Scripts in micro_exp reference these arrays at /scratch2/wl730/micro_traindata_packed{,_all};
extract them anywhere and update that path.
Shards
| Shard | Size |
|---|---|
code/gem5-TAO-src.tar.zst |
14.5 MB |
micro_ckpt/508.namd_r.tar.zst |
0.74 GB |
micro_ckpt/511.povray_r.tar.zst |
0.0 MB |
micro_ckpt/526.blender_r.tar.zst |
0.74 GB |
micro_ckpt/600.perlbench_s.tar.zst |
0.25 GB |
micro_ckpt/602.gcc_s.tar.zst |
0.37 GB |
micro_ckpt/603.bwaves_s.tar.zst |
0.0 MB |
micro_ckpt/605.mcf_s.tar.zst |
0.22 GB |
micro_ckpt/607.cactuBSSN_s.tar.zst |
0.71 GB |
micro_ckpt/619.lbm_s.tar.zst |
0.52 GB |
micro_ckpt/620.omnetpp_s.tar.zst |
0.34 GB |
micro_ckpt/621.wrf_s.tar.zst |
0.21 GB |
micro_ckpt/623.xalancbmk_s.tar.zst |
0.78 GB |
micro_ckpt/631.deepsjeng_s.tar.zst |
0.90 GB |
micro_ckpt/638.imagick_s.tar.zst |
0.74 GB |
micro_ckpt/641.leela_s.tar.zst |
0.85 GB |
micro_ckpt/644.nab_s.tar.zst |
0.18 GB |
micro_ckpt/648.exchange2_s.tar.zst |
0.27 GB |
micro_ckpt/654.roms_s.tar.zst |
0.19 GB |
micro_ckpt/657.xz_s.tar.zst |
0.24 GB |
micro_ckpt/_files.tar.zst |
0.0 MB |
micro_exp/_files.tar.zst |
0.0 MB |
micro_exp/bbv.tar.zst |
3.1 MB |
micro_exp/dse_exp/_rest.tar.zst |
64.5 MB |
micro_exp/dse_exp/precompute/508.namd_r.tar.zst |
9.6 MB |
micro_exp/dse_exp/precompute/526.blender_r.tar.zst |
14.8 MB |
micro_exp/dse_exp/precompute/600.perlbench_s.tar.zst |
21.3 MB |
micro_exp/dse_exp/precompute/602.gcc_s.tar.zst |
15.1 MB |
micro_exp/dse_exp/precompute/605.mcf_s.tar.zst |
22.3 MB |
micro_exp/dse_exp/precompute/607.cactuBSSN_s.tar.zst |
16.6 MB |
micro_exp/dse_exp/precompute/619.lbm_s.tar.zst |
39.6 MB |
micro_exp/dse_exp/precompute/620.omnetpp_s.tar.zst |
54.2 MB |
micro_exp/dse_exp/precompute/621.wrf_s.tar.zst |
26.8 MB |
micro_exp/dse_exp/precompute/623.xalancbmk_s.tar.zst |
17.3 MB |
micro_exp/dse_exp/precompute/631.deepsjeng_s.tar.zst |
32.1 MB |
micro_exp/dse_exp/precompute/638.imagick_s.tar.zst |
14.3 MB |
micro_exp/dse_exp/precompute/641.leela_s.tar.zst |
27.8 MB |
micro_exp/dse_exp/precompute/644.nab_s.tar.zst |
12.6 MB |
micro_exp/dse_exp/precompute/648.exchange2_s.tar.zst |
40.8 MB |
micro_exp/dse_exp/precompute/654.roms_s.tar.zst |
13.8 MB |
micro_exp/dse_exp/precompute/657.xz_s.tar.zst |
17.8 MB |
micro_exp/dse_exp/precompute/_files.tar.zst |
0.0 MB |
micro_exp/dse_exp/precompute/l1_128.tar.zst |
0.40 GB |
micro_exp/dse_exp/precompute/l1_16.tar.zst |
0.43 GB |
micro_exp/dse_exp/precompute/l1_256.tar.zst |
0.40 GB |
micro_exp/dse_exp/precompute/l1_32.tar.zst |
0.41 GB |
micro_exp/dse_exp/precompute/l1_64.tar.zst |
0.41 GB |
micro_exp/dse_exp/traces/508.namd_r.tar.zst |
1.00 GB |
micro_exp/dse_exp/traces/526.blender_r.tar.zst |
1.16 GB |
micro_exp/dse_exp/traces/600.perlbench_s.tar.zst |
1.28 GB |
micro_exp/dse_exp/traces/602.gcc_s.tar.zst |
1.01 GB |
micro_exp/dse_exp/traces/605.mcf_s.tar.zst |
1.13 GB |
micro_exp/dse_exp/traces/607.cactuBSSN_s.tar.zst |
1.09 GB |
micro_exp/dse_exp/traces/619.lbm_s.tar.zst |
0.97 GB |
micro_exp/dse_exp/traces/620.omnetpp_s.tar.zst |
1.14 GB |
micro_exp/dse_exp/traces/621.wrf_s.tar.zst |
0.90 GB |
micro_exp/dse_exp/traces/623.xalancbmk_s.tar.zst |
1.14 GB |
micro_exp/dse_exp/traces/631.deepsjeng_s.tar.zst |
1.30 GB |
micro_exp/dse_exp/traces/638.imagick_s.tar.zst |
1.16 GB |
micro_exp/dse_exp/traces/641.leela_s.tar.zst |
1.22 GB |
micro_exp/dse_exp/traces/644.nab_s.tar.zst |
0.97 GB |
micro_exp/dse_exp/traces/648.exchange2_s.tar.zst |
1.28 GB |
micro_exp/dse_exp/traces/654.roms_s.tar.zst |
0.89 GB |
micro_exp/dse_exp/traces/657.xz_s.tar.zst |
1.15 GB |
micro_exp/dse_v2.tar.zst |
3.81 GB |
micro_exp/exp1.tar.zst |
2.52 GB |
micro_exp/exp2/exp2.tar.zst.part00 |
21.47 GB |
micro_exp/exp2/exp2.tar.zst.part01 |
21.47 GB |
micro_exp/exp2/exp2.tar.zst.part02 |
21.47 GB |
micro_exp/exp2/exp2.tar.zst.part03 |
18.13 GB |
micro_exp/exp4-gradient-conflict.tar.zst |
0.4 MB |
micro_exp/exp4.tar.zst |
10.18 GB |
micro_exp/exp5.tar.zst |
0.56 GB |
micro_exp/exp5_work_410.tar.zst |
0.11 GB |
micro_exp/exp5_work_528.tar.zst |
0.17 GB |
micro_exp/lmono_reuslts.tar.zst |
2.9 MB |
micro_exp/lmono_sweep.tar.zst |
0.0 MB |
micro_exp/mcpat.tar.zst |
3.7 MB |
micro_exp/new_bench.tar.zst |
29.8 MB |
micro_exp/pretrain.tar.zst |
0.0 MB |
micro_exp/window_ids.tar.zst |
0.6 MB |
train_data/micro_traindata_packed.tar.zst |
0.32 GB |
train_data/micro_traindata_packed_all.tar.zst |
0.58 GB |
| Total (83 files) | 130.4 GB |
micro_exp/exp2 (mostly model checkpoints, 82.6 GB) is split into 20 GB parts because of the per-file size limit. Reassemble with:
cat micro_exp/exp2/exp2.tar.zst.part* | zstd -dc | tar -xf -
Provenance and licensing
- Traces were produced by simulating SPEC CPU2017 workloads in gem5. No SPEC CPU2017 binaries, source code or input files are included; only simulator output is distributed. Reproducing the traces requires your own licensed copy of SPEC CPU2017.
- The gem5 SimPoint checkpoints (whose memory images contain SPEC binaries and inputs) and the SPEC run directories needed to restart from them are kept in a separate private dataset, shared only with holders of a SPEC CPU2017 license. Contact the author for access.
- The gem5 source is distributed under gem5's original BSD-style license (see
LICENSE/COPYINGinsidecode/gem5-TAO-src.tar.zst). micro_exp/new_benchcontains a bundle of open-source benchmarks under their own licenses.
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