anonymous-neurips26-ljasd/LM-SimBench_example

LM-SimBench (Example Snapshot)

Dataset Description

This repository distributes a compact example snapshot of LM-SimBench, the structured CSV release of large-scale LLM training-performance profiling data. The snapshot is provided so reviewers and readers can inspect file layout, schemas, and representative records without downloading the multi–tens-of-gigabyte full release.

The profiling methodology, software stack, and field definitions are the same as in the complete LM-SimBench dataset:

Full release: anonymous-neurips26-ljasd/LM-SimBench

Organized CSV fields correspond to the dataset card of that repository. The original raw traces are indexed from the companion raw-data repository linked there (see Reference(s) on the full dataset card).

This snapshot is intended for research inspection and format verification. It is not a downsized substitute for experiments that require full corpus coverage or model diversity. For modeling and benchmarking at scale, use the full LM-SimBench release above.

The snapshot is released under the same license as the full dataset (see License/Terms of Use).

Relationship to the Full LM-SimBench Release

Aspect	This repository (LM-SimBench_example)	Full release (LM-SimBench)
Scope	Single model directory (qwen3_8b_4k), fixed subset of feasible parallel configurations	Multiple model families and contexts; full feasible-configuration coverage per model
Repository-level data_manifest.csv	Not included (not required for a path-preview snapshot)	Included for corpus-wide indexing
CSV schemas (overlapping files)	Same column semantics as documented on the full dataset card	Canonical documentation

Contents of This Snapshot

Model directory: qwen3_8b_4k

Model-level CSV files (same roles as in the full release where names overlap):

• step_time_summary.csv — configuration-level iteration time.
• feasible_configs_sorted.csv — feasible configurations sorted by iteration_time_ms within this model directory.
• peak_memory.csv — rank-level peak allocated memory.
• operator_latency_by_category.csv — operator latency by category and type.

Additional model-level CSV files in this snapshot (derived from the same preprocessing pipeline; useful for debugging and fine-grained analysis; not listed on the main LM-SimBench dataset card):

• step_trace_time_summary.csv — rank-level breakdown of iteration time into coarse phases (e.g., compute, communication, overlap-related fields as emitted by the profiler pipeline).
• comm_summary.csv — rank- and configuration-level aggregated communication statistics (sums/means of elapsed and wait-related quantities, link-type aggregates, and related summaries), summarizing content related to per-event comm_time.csv files below.

Per-configuration layout (unchanged from the full release):

qwen3_8b_4k/
  step_time_summary.csv
  feasible_configs_sorted.csv
  peak_memory.csv
  operator_latency_by_category.csv
  comm_summary.csv
  step_trace_time_summary.csv
  <parallel_config>/
    rank_<id>/
      comm_time.csv
      matrix_operator_latency.csv

Parallel configurations included: this snapshot contains 26 configuration directories under qwen3_8b_4k, following the naming convention dp<dp>-tp<tp>-pp<pp>-cp<cp>-<RC|noRC> documented on the full dataset card.

Approximate total size: on the order of 2 GB (exact byte size depends on Hugging Face packaging).

Dataset Fields (Overlapping Files)

Field definitions for step_time_summary.csv, feasible_configs_sorted.csv, peak_memory.csv, operator_latency_by_category.csv, comm_time.csv, and matrix_operator_latency.csv are identical to those in the full LM-SimBench dataset card:

https://huggingface.co/datasets/anonymous-neurips26-ljasd/LM-SimBench

Readers should consult that card for authoritative column-level documentation.

step_trace_time_summary.csv (this snapshot only)

Rank-level summary of iteration-time attribution (microseconds in the released files), including columns such as computing, communication-related buckets, pipeline stage and bubble-related fields, and other profiler-phase labels as produced by the extraction tooling. Exact column names appear in the file header.

comm_summary.csv (this snapshot only)

Rank-level aggregates of communication behavior per configuration, including elapsed-time summaries, wait- and synchronization-related aggregates where present, per-link-type transit summaries, and matrix-level communication summaries where applicable. Exact column names appear in the file header.

Intended Usage

• Verify CSV schemas and directory layout before retrieving the full LM-SimBench release.
• Lightweight reproduction of loading and parsing code in restricted download environments.
• Teaching and onboarding for the profiling data format.

For substantial empirical studies, use the full LM-SimBench dataset linked above.

License/Terms of Use

This snapshot is governed by the Creative Commons Attribution 4.0 International License (CC BY 4.0), consistent with the full LM-SimBench release.

Users may share and adapt the data subject to attribution and license compatibility requirements stated on the full dataset card.

Limitations

• Subset only: statistics and model coverage do not match the full corpus.
• Environment specificity: measurements reflect the stack and hardware configuration described on the full dataset card.
• Profiler artifacts: data may include measurement overhead and runtime variability as in the full release.
• Extra CSV files (step_trace_time_summary.csv, comm_summary.csv) are ancillary to the main card documentation; downstream tools should not assume their presence in every third-party mirror of LM-SimBench unless explicitly stated.

Dataset Characterization

Data collection method: Automated profiler collection from distributed LLM training runs (same pipeline as the full release).

Labeling method: Automated extraction from training logs and profiler records.

Ethical Considerations

As with LM-SimBench, this snapshot contains performance profiling metrics, not natural-language conversations, personal identifiers, or user-generated text. Downstream use must comply with the dataset license, hosting platform terms, and applicable policies.