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LithosAnanake/experiments/std79-doe/analysis-20260912/ANALYSIS.md
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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

23 KiB
Raw Blame History

std79-doe 3×9 Factorial Analysis — Heartbeat/Physics Telemetry

Generated 2026-09-12 12:45. Source: combined.csv (768 rows), built by correlate_doe.py from the three raw logs in ../results-20260912-with-heartbeat-csv/. See that file's own docstring for the correlation methodology (the heartbeat tick's async CSV printer and the DoE trial loop's own console output share one serial line with no locking, so rows can land mid-token in the raw logs; the underlying FORTH execution and values are unaffected, only reconstructing which trial owns which row needed care).

Design: 3 architectures (amd64, aarch64, riscv64) × 9 identities (zuse, rajames, 00-06) × 3 replicates, Fisher-Yates-shuffled run order, same seed (12345) on every architecture (FABRIC-3.md §XV). Each of the 27 trials per architecture runs the same 24-word FORTH-79 exerciser; every heartbeat tick during the whole EXEC-STD79-DOE run (bracketed by HB-ON/HB-OFF) emits one telemetry row (doe_log.c), correlated back to whichever trial was active when it printed.

Known limitation: aarch64's raw log lost 1 of 27 run_id markers to interleaving beyond recovery (trial 12, identity rajames rep 1) -- its rows are folded into trial 11 (identity rajames rep 0) in this dataset, so aarch64's rajames cell for rep-sensitive metrics is not perfectly separable for those two replicates specifically. Every other cell on every architecture is unaffected.

Key findings (p < 0.05, two-way ANOVA)

  • Architecture main effect: APIC ticks per heartbeat tick, TIME-TRUST, Timing variance, Hera fleet heat, Hermes fleet heat
  • Identity main effect: Hot word count, Mean word execution heat, Rolling window width, Actual analysis window size
  • Architecture × identity interaction: none
  • No significant effect of either factor: Estimated timer jitter (ns), Max VM call depth, Artemis fleet heat

TIME-TRUST's architecture effect is a numerical artifact worth reading correctly, not a substantive result: it is essentially constant within each architecture (residual variance ~1e-31, i.e. floating-point noise) and differs between architectures — so the F statistic is enormous simply because the within-group denominator is near zero, not because TIME-TRUST is meaningfully more variable across architectures than the other metrics here. It is architecture-determined and identity-independent, which is itself the interesting part.

Hot word count (hot_word_count)

Hot word count

Architecture Identity n mean sd CV%
amd64 zuse 23 67.91 1.411 2.08
amd64 rajames 25 64.28 7.414 11.53
amd64 00 26 64.46 7.235 11.22
amd64 01 26 62.62 6.929 11.07
amd64 02 28 62.5 6.251 10.00
amd64 03 30 62.5 5.71 9.14
amd64 04 33 66.55 5.386 8.09
amd64 05 33 64.45 3.874 6.01
amd64 06 31 65.1 3.953 6.07
aarch64 zuse 23 69.35 1.191 1.72
aarch64 rajames 16 65.19 8.336 12.79
aarch64 00 26 62.92 7.451 11.84
aarch64 01 27 59.81 8.005 13.38
aarch64 02 37 61.27 8.425 13.75
aarch64 03 29 60.79 7.208 11.86
aarch64 04 33 67.48 5.745 8.51
aarch64 05 33 62.73 4.692 7.48
aarch64 06 32 63.19 4.351 6.89
riscv64 zuse 23 69.48 1.039 1.50
riscv64 rajames 26 65.27 11.27 17.27
riscv64 00 27 62.33 8.953 14.36
riscv64 01 28 61.68 7.252 11.76
riscv64 02 27 62.3 8.109 13.02
riscv64 03 28 63 7.123 11.31
riscv64 04 32 67.53 6.069 8.99
riscv64 05 32 65.56 5.691 8.68
riscv64 06 34 63.85 3.839 6.01

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 193 96.5 2.333 0.09771
id_label 8 3985 498.2 12.04 2.984e-16
arch:id_label 16 379.9 23.74 0.574 0.9044
Residuals 741 3.065e+04 41.36 — —

Mean word execution heat (avg_word_heat)

Mean word execution heat

Architecture Identity n mean sd CV%
amd64 zuse 23 0.003006 0.001235 41.07
amd64 rajames 25 0.008839 0.008672 98.11
amd64 00 26 0.008918 0.006694 75.06
amd64 01 26 0.01001 0.007327 73.18
amd64 02 28 0.01069 0.004371 40.89
amd64 03 30 0.01032 0.005016 48.61
amd64 04 33 0.004205 0.002958 70.35
amd64 05 33 0.008833 0.001235 13.98
amd64 06 31 0.008481 0.003743 44.13
aarch64 zuse 23 0.002911 0.001418 48.72
aarch64 rajames 16 0.007391 0.005726 77.48
aarch64 00 26 0.009563 0.006967 72.85
aarch64 01 27 0.01071 0.007785 72.67
aarch64 02 37 0.01146 0.007358 64.20
aarch64 03 29 0.01046 0.005237 50.05
aarch64 04 33 0.004262 0.00298 69.93
aarch64 05 33 0.009139 0.001443 15.79
aarch64 06 32 0.008941 0.003745 41.89
riscv64 zuse 23 0.00289 0.00163 56.41
riscv64 rajames 26 0.0106 0.0133 125.42
riscv64 00 27 0.01041 0.009052 86.95
riscv64 01 28 0.01068 0.007445 69.74
riscv64 02 27 0.01104 0.005331 48.29
riscv64 03 28 0.01007 0.005873 58.30
riscv64 04 32 0.004007 0.002863 71.45
riscv64 05 32 0.009038 0.001403 15.52
riscv64 06 34 0.009104 0.003387 37.20

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 3.325e-05 1.662e-05 0.5221 0.5935
id_label 8 0.005366 0.0006707 21.07 6.051e-29
arch:id_label 16 0.0001362 8.514e-06 0.2674 0.9983
Residuals 741 0.02359 3.184e-05 — —

Rolling window width (window_width)

Rolling window width

Architecture Identity n mean sd CV%
amd64 zuse 23 256 0 0.00
amd64 rajames 25 256 0 0.00
amd64 00 26 256 0 0.00
amd64 01 26 256 0 0.00
amd64 02 28 256 0 0.00
amd64 03 30 256 0 0.00
amd64 04 33 318.1 192.3 60.47
amd64 05 33 256 0 0.00
amd64 06 31 256 0 0.00
aarch64 zuse 23 256 0 0.00
aarch64 rajames 16 256 0 0.00
aarch64 00 26 256 0 0.00
aarch64 01 27 256 0 0.00
aarch64 02 37 256 0 0.00
aarch64 03 29 256 0 0.00
aarch64 04 33 318.1 192.3 60.47
aarch64 05 33 256 0 0.00
aarch64 06 32 256 0 0.00
riscv64 zuse 23 256 0 0.00
riscv64 rajames 26 256 0 0.00
riscv64 00 27 256 0 0.00
riscv64 01 28 256 0 0.00
riscv64 02 27 256 0 0.00
riscv64 03 28 256 0 0.00
riscv64 04 32 296 147 49.66
riscv64 05 32 256 0 0.00
riscv64 06 34 256 0 0.00

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 1575 787.7 0.1922 0.8252
id_label 8 2.57e+05 3.213e+04 7.839 3.816e-10
arch:id_label 16 9163 572.7 0.1397 1
Residuals 741 3.037e+06 4098 — —

Actual analysis window size (actual_window_size)

Actual analysis window size

Architecture Identity n mean sd CV%
amd64 zuse 23 256 0 0.00
amd64 rajames 25 256 0 0.00
amd64 00 26 256 0 0.00
amd64 01 26 256 0 0.00
amd64 02 28 256 0 0.00
amd64 03 30 256 0 0.00
amd64 04 33 318.1 192.3 60.47
amd64 05 33 256 0 0.00
amd64 06 31 256 0 0.00
aarch64 zuse 23 256 0 0.00
aarch64 rajames 16 256 0 0.00
aarch64 00 26 256 0 0.00
aarch64 01 27 256 0 0.00
aarch64 02 37 256 0 0.00
aarch64 03 29 256 0 0.00
aarch64 04 33 318.1 192.3 60.47
aarch64 05 33 256 0 0.00
aarch64 06 32 256 0 0.00
riscv64 zuse 23 256 0 0.00
riscv64 rajames 26 256 0 0.00
riscv64 00 27 256 0 0.00
riscv64 01 28 256 0 0.00
riscv64 02 27 256 0 0.00
riscv64 03 28 256 0 0.00
riscv64 04 32 296 147 49.66
riscv64 05 32 256 0 0.00
riscv64 06 34 256 0 0.00

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 1575 787.7 0.1922 0.8252
id_label 8 2.57e+05 3.213e+04 7.839 3.816e-10
arch:id_label 16 9163 572.7 0.1397 1
Residuals 741 3.037e+06 4098 — —

Estimated timer jitter (ns) (jitter_ns)

Estimated timer jitter (ns)

Architecture Identity n mean sd CV%
amd64 zuse 23 0 0 —
amd64 rajames 25 7.379e+17 3.689e+18 500.00
amd64 00 26 7.095e+17 3.618e+18 509.90
amd64 01 26 9.462e+04 4.824e+05 509.90
amd64 02 28 6.588e+17 3.486e+18 529.15
amd64 03 30 1.23e+18 4.68e+18 380.56
amd64 04 33 5.59e+17 3.211e+18 574.46
amd64 05 33 5.59e+17 3.211e+18 574.46
amd64 06 31 5.951e+17 3.313e+18 556.78
aarch64 zuse 23 0 0 —
aarch64 rajames 16 0 0 —
aarch64 00 26 7.095e+17 3.618e+18 509.90
aarch64 01 27 6.832e+17 3.55e+18 519.62
aarch64 02 37 9.971e+17 4.229e+18 424.10
aarch64 03 29 6.361e+17 3.425e+18 538.52
aarch64 04 33 5.59e+17 3.211e+18 574.46
aarch64 05 33 1.118e+18 4.47e+18 399.80
aarch64 06 32 5.765e+17 3.261e+18 565.69
riscv64 zuse 23 0 0 —
riscv64 rajames 26 1.419e+18 5.013e+18 353.27
riscv64 00 27 6.832e+17 3.55e+18 519.62
riscv64 01 28 6.588e+17 3.486e+18 529.15
riscv64 02 27 6.832e+17 3.55e+18 519.62
riscv64 03 28 6.588e+17 3.486e+18 529.15
riscv64 04 32 5.765e+17 3.261e+18 565.69
riscv64 05 32 5.765e+17 3.261e+18 565.69
riscv64 06 34 1.085e+18 4.406e+18 406.02

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 2.475e+36 1.237e+36 0.1045 0.9008
id_label 8 4.303e+37 5.379e+36 0.4543 0.888
arch:id_label 16 4.688e+37 2.93e+36 0.2475 0.9989
Residuals 741 8.772e+39 1.184e+37 — —

APIC ticks per heartbeat tick (apic_delta)

APIC ticks per heartbeat tick

Architecture Identity n mean sd CV%
amd64 zuse 20 2.9 1.119 38.59
amd64 rajames 22 2.045 4.18 204.37
amd64 00 23 2.304 3.363 145.96
amd64 01 23 2.739 1.214 44.33
amd64 02 25 3.12 2.505 80.30
amd64 03 27 3.481 2.737 78.61
amd64 04 30 3.1 1.605 51.77
amd64 05 30 1.767 6.157 348.51
amd64 06 28 2.357 5.87 249.04
aarch64 zuse 20 4.45 2.585 58.09
aarch64 rajames 14 4.786 2.517 52.59
aarch64 00 23 3.348 7.158 213.81
aarch64 01 24 2.75 7.651 278.23
aarch64 02 34 2.618 9.711 370.98
aarch64 03 26 3.538 3.301 93.30
aarch64 04 30 4.3 2.409 56.02
aarch64 05 30 3.4 5.083 149.49
aarch64 06 29 2.414 10.24 424.30
riscv64 zuse 20 1.35 0.6708 49.69
riscv64 rajames 23 1.435 2.952 205.71
riscv64 00 24 1.667 1.761 105.67
riscv64 01 25 1.2 2.217 184.78
riscv64 02 24 1.167 2.599 222.75
riscv64 03 25 1.16 2.939 253.40
riscv64 04 29 1.448 1.572 108.53
riscv64 05 29 1.207 3.783 313.47
riscv64 06 31 1.452 3.414 235.19

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 495.9 247.9 11.21 1.626e-05
id_label 8 71.4 8.924 0.4036 0.9187
arch:id_label 16 135.9 8.492 0.384 0.986
Residuals 661 1.462e+04 22.11 — —

TIME-TRUST (time_trust)

TIME-TRUST

Architecture Identity n mean sd CV%
amd64 zuse 23 0.6132 0 0.00
amd64 rajames 25 0.6132 0 0.00
amd64 00 26 0.6132 0 0.00
amd64 01 26 0.6132 0 0.00
amd64 02 28 0.6132 0 0.00
amd64 03 30 0.6132 0 0.00
amd64 04 33 0.6132 0 0.00
amd64 05 33 0.6132 0 0.00
amd64 06 31 0.6132 0 0.00
aarch64 zuse 23 1 0 0.00
aarch64 rajames 16 1 0 0.00
aarch64 00 26 1 0 0.00
aarch64 01 27 1 0 0.00
aarch64 02 37 1 0 0.00
aarch64 03 29 1 0 0.00
aarch64 04 33 1 0 0.00
aarch64 05 33 1 0 0.00
aarch64 06 32 1 0 0.00
riscv64 zuse 23 1 0 0.00
riscv64 rajames 26 1 0 0.00
riscv64 00 27 1 0 0.00
riscv64 01 28 1 0 0.00
riscv64 02 27 1 0 0.00
riscv64 03 28 1 0 0.00
riscv64 04 32 1 0 0.00
riscv64 05 32 1 0 0.00
riscv64 06 34 1 0 0.00

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 25.48 12.74 5.379e+31 0
id_label 8 1.306e-30 1.632e-31 0.6892 0.7014
arch:id_label 16 2.579e-30 1.612e-31 0.6806 0.8149
Residuals 741 1.755e-28 2.369e-31 — —

Timing variance (variance)

Timing variance

Architecture Identity n mean sd CV%
amd64 zuse 23 0.6307 0 0.00
amd64 rajames 25 0.6307 0 0.00
amd64 00 26 0.6307 0 0.00
amd64 01 26 0.6307 0 0.00
amd64 02 28 0.6307 0 0.00
amd64 03 30 0.6307 0 0.00
amd64 04 33 0.6307 0 0.00
amd64 05 33 0.6307 0 0.00
amd64 06 31 0.6307 0 0.00
aarch64 zuse 23 0 0 —
aarch64 rajames 16 0 0 —
aarch64 00 26 0 0 —
aarch64 01 27 0 0 —
aarch64 02 37 0 0 —
aarch64 03 29 0 0 —
aarch64 04 33 0 0 —
aarch64 05 33 0 0 —
aarch64 06 32 0 0 —
riscv64 zuse 23 0 0 —
riscv64 rajames 26 0 0 —
riscv64 00 27 0 0 —
riscv64 01 28 0 0 —
riscv64 02 27 0 0 —
riscv64 03 28 0 0 —
riscv64 04 32 0 0 —
riscv64 05 32 0 0 —
riscv64 06 34 0 0 —

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 67.75 33.88 4.293e+32 0
id_label 8 6.232e-31 7.79e-32 0.9873 0.4444
arch:id_label 16 1.249e-30 7.806e-32 0.9893 0.4663
Residuals 741 5.847e-29 7.89e-32 — —

Max VM call depth (vm_call_depth_max)

Max VM call depth

Architecture Identity n mean sd CV%
amd64 zuse 23 1 0 0.00
amd64 rajames 25 0.96 0.2 20.83
amd64 00 26 0.9615 0.1961 20.40
amd64 01 26 0.9615 0.1961 20.40
amd64 02 28 0.9643 0.189 19.60
amd64 03 30 0.9667 0.1826 18.89
amd64 04 33 0.9697 0.1741 17.95
amd64 05 33 0.9697 0.1741 17.95
amd64 06 31 0.9677 0.1796 18.56
aarch64 zuse 23 1 0 0.00
aarch64 rajames 16 1 0 0.00
aarch64 00 26 0.9615 0.1961 20.40
aarch64 01 27 0.963 0.1925 19.99
aarch64 02 37 0.9459 0.2292 24.23
aarch64 03 29 0.9655 0.1857 19.23
aarch64 04 33 0.9697 0.1741 17.95
aarch64 05 33 0.9394 0.2423 25.79
aarch64 06 32 0.9688 0.1768 18.25
riscv64 zuse 23 1 0 0.00
riscv64 rajames 26 0.9231 0.2717 29.44
riscv64 00 27 0.9259 0.2669 28.82
riscv64 01 28 0.9643 0.189 19.60
riscv64 02 27 0.963 0.1925 19.99
riscv64 03 28 0.9643 0.189 19.60
riscv64 04 32 0.9688 0.1768 18.25
riscv64 05 32 0.9688 0.1768 18.25
riscv64 06 34 0.9706 0.1715 17.67

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 0.007273 0.003636 0.1044 0.9009
id_label 8 0.1239 0.01549 0.4447 0.8942
arch:id_label 16 0.1016 0.006352 0.1823 0.9999
Residuals 741 25.82 0.03484 — —

Hera fleet heat (hera_heat)

Hera fleet heat

Architecture Identity n mean sd CV%
amd64 zuse 23 0.6532 0 0.00
amd64 rajames 25 0.6532 0 0.00
amd64 00 26 0.6532 0 0.00
amd64 01 26 0.6532 0 0.00
amd64 02 28 0.6532 0 0.00
amd64 03 30 0.6532 0 0.00
amd64 04 33 0.6532 0 0.00
amd64 05 33 0.6532 0 0.00
amd64 06 31 0.6532 0 0.00
aarch64 zuse 23 0.6767 0 0.00
aarch64 rajames 16 0.6767 0 0.00
aarch64 00 26 0.6767 0 0.00
aarch64 01 27 0.6767 0 0.00
aarch64 02 37 0.6767 0 0.00
aarch64 03 29 0.6767 0 0.00
aarch64 04 33 0.6767 0 0.00
aarch64 05 33 0.6767 0 0.00
aarch64 06 32 0.6767 0 0.00
riscv64 zuse 23 0.6384 0 0.00
riscv64 rajames 26 0.6384 0 0.00
riscv64 00 27 0.6384 0 0.00
riscv64 01 28 0.6384 0 0.00
riscv64 02 27 0.6384 0 0.00
riscv64 03 28 0.6384 0 0.00
riscv64 04 32 0.6384 0 0.00
riscv64 05 32 0.6384 0 0.00
riscv64 06 34 0.6384 0 0.00

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 0.1909 0.09546 3.106e+30 0
id_label 8 1.995e-31 2.494e-32 0.8114 0.5927
arch:id_label 16 3.935e-31 2.459e-32 0.8003 0.6863
Residuals 741 2.277e-29 3.073e-32 — —

Hermes fleet heat (hermes_heat)

Hermes fleet heat

Architecture Identity n mean sd CV%
amd64 zuse 23 0.1692 0 0.00
amd64 rajames 25 0.1692 0 0.00
amd64 00 26 0.1692 0 0.00
amd64 01 26 0.1692 0 0.00
amd64 02 28 0.1692 0 0.00
amd64 03 30 0.1692 0 0.00
amd64 04 33 0.1692 0 0.00
amd64 05 33 0.1692 0 0.00
amd64 06 31 0.1692 0 0.00
aarch64 zuse 23 0.1581 0 0.00
aarch64 rajames 16 0.1581 0 0.00
aarch64 00 26 0.1581 0 0.00
aarch64 01 27 0.1581 0 0.00
aarch64 02 37 0.1581 0 0.00
aarch64 03 29 0.1581 0 0.00
aarch64 04 33 0.1581 0 0.00
aarch64 05 33 0.1581 0 0.00
aarch64 06 32 0.1581 0 0.00
riscv64 zuse 23 0.1762 0 0.00
riscv64 rajames 26 0.1762 0 0.00
riscv64 00 27 0.1762 0 0.00
riscv64 01 28 0.1762 0 0.00
riscv64 02 27 0.1762 0 0.00
riscv64 03 28 0.1762 0 0.00
riscv64 04 32 0.1762 0 0.00
riscv64 05 32 0.1762 0 0.00
riscv64 06 34 0.1762 0 0.00

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 0.04251 0.02125 1.491e+31 0
id_label 8 9.968e-33 1.246e-33 0.8744 0.5376
arch:id_label 16 1.967e-32 1.229e-33 0.8627 0.6132
Residuals 741 1.056e-30 1.425e-33 — —

Artemis fleet heat (artemis_heat)

Artemis fleet heat

Architecture Identity n mean sd CV%
amd64 zuse 23 1.545e+17 5.118e+17 331.32
amd64 rajames 25 1.421e+17 4.919e+17 346.11
amd64 00 26 2.05e+17 5.788e+17 282.37
amd64 01 26 4.1e+08 7.633e+08 186.19
amd64 02 28 1.269e+17 4.659e+17 367.17
amd64 03 30 5.922e+16 3.244e+17 547.72
amd64 04 33 1.077e+17 4.305e+17 399.80
amd64 05 33 1.615e+17 5.186e+17 321.13
amd64 06 31 1.719e+17 5.339e+17 310.56
aarch64 zuse 23 7.184e+16 3.445e+17 479.58
aarch64 rajames 16 2.065e+17 5.643e+17 273.25
aarch64 00 26 1.906e+17 5.383e+17 282.37
aarch64 01 27 3.671e+08 6.999e+08 190.65
aarch64 02 37 8.931e+16 3.788e+17 424.10
aarch64 03 29 5.697e+16 3.068e+17 538.52
aarch64 04 33 1.001e+17 4.003e+17 399.80
aarch64 05 33 1.502e+17 4.823e+17 321.13
aarch64 06 32 1.549e+17 4.893e+17 315.89
riscv64 zuse 23 8.062e+16 3.866e+17 479.58
riscv64 rajames 26 2.853e+08 6.822e+08 239.16
riscv64 00 27 1.374e+17 4.949e+17 360.29
riscv64 01 28 3.973e+08 7.748e+08 195.00
riscv64 02 27 6.868e+16 3.568e+17 519.62
riscv64 03 28 6.622e+16 3.504e+17 529.15
riscv64 04 32 5.794e+16 3.278e+17 565.69
riscv64 05 32 1.159e+17 4.56e+17 393.50
riscv64 06 34 1.636e+17 5.338e+17 326.29

Two-way ANOVA (metric ~ arch * id_label):

Term Df Sum Sq Mean Sq F value Pr(>F)
arch 2 2.807e+35 1.404e+35 0.801 0.4492
id_label 8 1.988e+36 2.486e+35 1.419 0.1849
arch:id_label 16 4.978e+35 3.111e+34 0.1776 0.9999
Residuals 741 1.298e+38 1.752e+35 — —
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