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Robert Allan JamesandClaude Sonnet 5 934be5a257

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Add 3x9 factorial analysis of std79-doe heartbeat/physics telemetry

Correlates every HB-ON/HB-OFF heartbeat-tick CSV row (results-20260912-
with-heartbeat-csv/*-doe-raw.log) back to which trial (run_id, id_idx,
id_label, rep) was active when it printed, despite the async tick
printer splicing rows mid-token -- including mid a DOE-RUN marker itself
-- into the trial loop's own console output on the shared serial line.

Pipeline (analysis-20260912/, see its own README.md):
- correlate_doe.py: two-pass reconstruction per architecture (remove
  atomic CSV-row spans to rebuild the clean trial-output stream, map
  each removed row's offset back to the nearest preceding run_id
  marker); identity/rep looked up from a known-clean prior run's
  run_id mapping rather than re-parsed, since one aarch64 marker
  (trial 12, rajames rep 1) lost its id_idx digit to a zero-separator
  collision with an adjacent CSV field and is unrecoverable from that
  log alone -- its rows fold into trial 11 instead, documented as a
  known limitation.
- combine.py: merges all three architectures into combined.csv (768
  rows), decoding Q48.16 fields to floats and jitter_bits' IEEE754 bit
  pattern to real jitter_ns.
- analysis.R: per-cell (architecture x identity) means/SD and two-way
  ANOVA for each of 12 telemetry metrics, one boxplot SVG per metric,
  written up as ANALYSIS.md.

Key findings: identity significantly affects word-heat/window-sizing
metrics (expected -- different identities execute different word
sets), architecture significantly affects timing metrics (APIC
ticks/tick, timing variance, fleet heat -- expected, different QEMU
targets), zero architecture x identity interaction on any metric.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EXieurDfDSsDFdnSyusuWo

2026-09-12 12:46:34 -04:00

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std79-doe 3×9 factorial analysis (heartbeat/physics telemetry)

Read ANALYSIS.md for the report itself (key findings, per-metric cell means, two-way ANOVA tables, boxplot SVGs). This file just documents the pipeline.

Pipeline

Source data: ../results-20260912-with-heartbeat-csv/{amd64,aarch64,riscv64}-doe-raw.log — raw QEMU serial logs from the HB-ON/HB-OFF-instrumented std79 DoE campaign (see ../README.md and FABRIC-3.md §XV/§XVI/§XVII for the campaign itself).
correlate_doe.py <raw_log> <out_csv> — reconstructs which DoE trial (run_id, id_idx, id_label, rep) was active for every heartbeat-tick CSV row, despite the async tick printer splicing rows mid-token into the trial loop's own console output on the shared serial line (see the script's own docstring for the two-pass reconstruction method, and its RUN_ID_MAP for why identity/rep are looked up from a known-clean prior run rather than re-parsed from each log — one aarch64 marker was unrecoverable, see below). Run once per architecture:
```
python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/amd64-doe-raw.log amd64-correlated.csv
python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/aarch64-doe-raw.log aarch64-correlated.csv
python3 correlate_doe.py ../results-20260912-with-heartbeat-csv/riscv64-doe-raw.log riscv64-correlated.csv
```
combine.py <in1.csv> [in2.csv ...] <out.csv> — merges the three per-architecture correlated CSVs into combined.csv (committed, 768 rows), decoding Q48.16 fixed-point fields to plain floats and jitter_bits' raw IEEE754 bit pattern to a real jitter_ns value:
```
python3 combine.py amd64-correlated.csv aarch64-correlated.csv riscv64-correlated.csv combined.csv
```
analysis.R (run from this directory) — reads combined.csv, computes per-cell (architecture × identity) summary statistics and a two-way ANOVA for each of the 12 varying telemetry metrics, renders one boxplot SVG per metric to svg/, and writes ANALYSIS.md:
```
Rscript analysis.R
```
Requires dplyr, tidyr, ggplot2, svglite (all present in this environment already).

Known limitation

aarch64's raw log lost 1 of 27 DOE-RUN markers to interleaving beyond recovery — trial 12 (identity rajames, rep 1). Its rows fold into trial 11 (identity rajames, rep 0) in combined.csv, so aarch64's rajames cell isn't perfectly separable between those two replicates specifically. Every other cell, on every architecture, is unaffected — see ANALYSIS.md's own note and correlate_doe.py's docstring for the full explanation.

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std79-doe 3×9 factorial analysis (heartbeat/physics telemetry)

Pipeline

Known limitation

2.6 KiB

Raw Blame History