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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xe8 in position 52: invalid continuation byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 196, in _generate_tables
                  csv_file_reader = pd.read_csv(file, iterator=True, dtype=dtype, **self.config.pd_read_csv_kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1279, in xpandas_read_csv
                  return pd.read_csv(xopen(filepath_or_buffer, "rb", download_config=download_config), **kwargs)
                         ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1026, in read_csv
                  return _read(filepath_or_buffer, kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 620, in _read
                  parser = TextFileReader(filepath_or_buffer, **kwds)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1620, in __init__
                  self._engine = self._make_engine(f, self.engine)
                                 ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/readers.py", line 1898, in _make_engine
                  return mapping[engine](f, **self.options)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 93, in __init__
                  self._reader = parsers.TextReader(src, **kwds)
                                 ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "pandas/_libs/parsers.pyx", line 574, in pandas._libs.parsers.TextReader.__cinit__
                File "pandas/_libs/parsers.pyx", line 663, in pandas._libs.parsers.TextReader._get_header
                File "pandas/_libs/parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "pandas/_libs/parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "pandas/_libs/parsers.pyx", line 2053, in pandas._libs.parsers.raise_parser_error
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe8 in position 52: invalid continuation byte

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HPCA 2027 CPU-Local Agentic Validated Traces

This dataset contains validated CPU-local traces used for HPCA 2027 agentic workload characterization. AppWorld, CORE-Bench, and Terminal-Bench are paper-backed benchmark workloads from official repositories; the hybrid-RAG and data-analysis agents are deterministic characterization workloads retained from the initial pass.

Workloads And Full 100M Results

Workload Source Frontend Backend Bad speculation Retiring
appworld ACL 2024 paper / official repo 8.19% 51.66% 19.21% 20.94%
core_bench_tomography_3497606 TMLR 2025 paper / official repo 4.33% 43.01% 12.55% 40.11%
terminalbench_train_fasttext ICLR 2026 paper / official repo 4.35% 51.63% 11.47% 32.55%
hybrid_rag_100k_d384_q64 deterministic CPU hybrid retrieval workflow 0.005044% 83.782907% 0.012178% 16.199871%
data_analysis_agent_e4m_r8 deterministic CPU data-analysis workflow 3.298938% 54.668990% 4.322370% 37.709701%

AppWorld runs released train/development solutions against its local 457-API environment. CORE-Bench runs official Code Ocean capsule 3497606, the iterative tomography workload. Terminal-Bench runs its official train-fasttext Yelp model-training task. All three measured phases are CPU-only and exclude external model/API waiting.

Percentages are derived from the four raw Scarab top-down counters in each workload's full-simulation validation directory. All four values sum to 100%.

Bundle Layout

Every workload uses the legacy BFS/ASPLOS-compatible directory structure:

<workload>/
  fingerprint/
  simpoints/
  traces/whole/<dr-folder>/{raw,trace,bin}/
  traces_simp/{raw,trace,bin}/
  trace_clustering_info.json
  validation/
  workload/

AppWorld selected segments 4, 5, and 9 with weights 0.100654, 0.100654, and 0.798692. CORE-Bench selected segments 3, 7, and 8 with weights 0.300328, 0.499453, and 0.200219. Terminal-Bench selected segments 2, 5, and 6 with weights 0.376507, 0.376507, and 0.246985.

The AppWorld and CORE-Bench traces require decoder history. Their selected ZIPs therefore contain contiguous chunks 0..selected, while retaining the same four-digit chunk names and BFS directory/descriptor interface. Simulations must use the selected segment's standard 10M-instruction ROI offset.

Validation

For AppWorld, CORE-Bench, and Terminal-Bench:

  • the whole trace covers the intended 100M-instruction window after a 1B skip;
  • every ZIP passes integrity testing;
  • selected weights normalize to 1.0;
  • every selected archive completes a 1M-instruction Scarab smoke simulation;
  • selected SimPoints have distinct raw top-down vectors;
  • full simulations use the Golden Cove setup with --dcache_assoc 8;
  • full simulations reproduce 51.66%, 43.01%, and 51.63% backend bound;
  • workload paper, repository revision, command, and compatibility notes are recorded under workload/ and validation/.

Per-file SHA-256 checksums are provided in the root SHA256SUMS. The DynamoRIO ARCH_REGDEPS warning is also present in the previously validated database and agentic releases; successful Scarab completion and raw counters are the acceptance criteria.

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