173 lines
7.1 KiB
TeX
173 lines
7.1 KiB
TeX
%% SCRAP: architecture/03-architecture/physics-engine/ssm-raw-data-analysis
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%% SOURCE: docs/working/architecture/03-architecture/physics-engine/ssm-raw-data-analysis.md
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%% STATUS: CURRENT
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%% FITS: dev-guide/ch-physics
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%% EDITORIAL: lifted — prose rewritten to press voice
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\section{Steady-State Machine: Experimental Validation}
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%% PATENT: The source frames much of this data around patent claims and a DARPA
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%% pitch. Those framing sections are summarized as empirical results only; no claim
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%% language is drafted. Flag for Bob before promoting the claims discussion.
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Four experimental campaigns, totaling 51{,}840 runs of the StarForth VM collected
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over 22--27 November 2025 (roughly 15.2\,MB of raw data), validate the
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Steady-State Machine (SSM) adaptive runtime. The campaigns establish four
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results: static configuration choice carries an 88\% performance spread; the L8
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adaptive mode selector converges to a near-optimal configuration; the system is
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shape-invariant across waveform types to within 0.6\% CV; and the runtime
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exhibits attractor-basin behavior, self-organizing to a stable operating point.
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\subsection{Dataset 1 --- Full Factorial DoE}
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The full factorial swept all $2^7 = 128$ binary combinations of feedback loops
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L1--L7, with 300 replicates each (38{,}400 runs) against a fixed Forth benchmark
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of 4{,}501 word executions.
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\begin{table}[ht]
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\centering
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\small
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\begin{tabular}{ll}
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\toprule
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Metric & Value \\
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\midrule
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Best config & \#35 @ 31.59 ms/word \\
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Worst config & \#124 @ 59.48 ms/word \\
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Performance spread & 88.3\% slower (worst vs best) \\
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Median performance & 40.77 ms/word \\
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CV range & 13.77\% -- 26.90\% \\
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\bottomrule
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\end{tabular}
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\caption{Full factorial performance summary.}
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\end{table}
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The best configuration, \#35 (binary 0100011), enables only the window,
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decay-inference, and heartrate loops (L2, L6, L7) and runs at
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$31.59 \pm 4.78$\,ms (CV 15.13\%) with a 0.00\% cache hit rate. The worst,
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\#124 (binary 1111100), enables five of seven loops (L1--L5) and posts a 31.24\%
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cache hit rate yet runs at $59.48 \pm 10.80$\,ms --- 88\% slower. The lesson is
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that more adaptation does not imply better performance: loop coordination
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matters more than loop count.
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\subsection{Dataset 2 --- Runoff Competition}
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The eight top DoE finalists were re-run head to head with 30 replicates each
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(240 runs) to identify a single best static configuration.
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\begin{table}[ht]
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\centering
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\small
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\begin{tabular}{lrrr}
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\toprule
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Config (binary) & Mean (ms) & Std (ms) & CV (\%) \\
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\midrule
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0100101 & 30.84 & 3.85 & 12.49 \\
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0000000 & 31.17 & 4.34 & 13.93 \\
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0010111 & 31.19 & 3.90 & 12.50 \\
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0100100 & 31.52 & 4.63 & 14.69 \\
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0110111 & 31.68 & 4.47 & 14.11 \\
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0010010 & 31.89 & 4.36 & 13.68 \\
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0000011 & 33.90 & 11.66 & 34.40 \\
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1000101 & 34.30 & 5.07 & 14.78 \\
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\bottomrule
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\end{tabular}
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\caption{Runoff results. The winner is config 0100101.}
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\end{table}
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The winner, config 0100101, enables the window, window-inference, and heartrate
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loops (L2, L5, L7), balancing the fastest mean (30.84\,ms) against the best
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stability (12.49\% CV).
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\subsection{Dataset 3 --- L8 Adaptive Mode Selector}
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The L8 selector was validated across five workload families (STABLE, TEMPORAL,
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VOLATILE, TRANSITION, DIVERSE) against eight strategies (L8 adaptive plus seven
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static configs), 2{,}400 runs per family (12{,}000 total). The central result is
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that L8 converged to config~\#55 (binary 0110111 --- L2, L3, L5, L6, L7 enabled)
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for all 1{,}500 adaptive runs across every workload family. Config~\#55 ranks
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\#6 of 128 on performance (31.91\,ms/word) and \#73 of 128 on stability
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(17.53\% CV).
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\begin{table}[ht]
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\centering
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\small
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\begin{tabular}{lrrr}
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\toprule
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Workload family & L8 adaptive (ms) & C0 baseline (ms) & Best static (ms) \\
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\midrule
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DIVERSE & $57.89 \pm 2.52$ & $57.59 \pm 2.61$ & 57.52 \\
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STABLE & $57.88 \pm 2.62$ & $58.28 \pm 3.82$ & 57.88 \\
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TEMPORAL & $57.96 \pm 2.51$ & $57.79 \pm 2.50$ & 57.79 \\
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TRANSITION & $57.67 \pm 2.62$ & $57.80 \pm 2.63$ & 57.67 \\
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VOLATILE & $57.93 \pm 2.59$ & $58.18 \pm 2.56$ & 57.75 \\
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\bottomrule
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\end{tabular}
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\caption{L8 adaptive selector versus baseline and best static configuration, by workload family.}
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\end{table}
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L8 matches or beats the static configurations on every family while performing
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near-zero mode switching --- it converges to a single mode and stays there.
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\subsection{Dataset 4 --- Shape-Invariant Validation}
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Four waveform types (baseline, damped sine, square wave, triangle) were run at
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300 replicates each (1{,}200 runs) under the fixed runoff-winner configuration
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(0100101).
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\begin{table}[ht]
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\centering
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\small
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\begin{tabular}{lrrr}
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\toprule
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Waveform & Mean (ms) & Std (ms) & CV (\%) \\
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\midrule
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baseline & 59.01 & 1.11 & 1.89 \\
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triangle & 58.93 & 1.09 & 1.84 \\
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square wave & 59.16 & 1.30 & 2.19 \\
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damped sine & 59.02 & 1.44 & 2.44 \\
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\bottomrule
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\end{tabular}
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\caption{Shape-invariance results across four waveform types.}
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\end{table}
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The CV range is 1.84\%--2.44\%, a spread of only 0.60\%, with a max/min mean
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performance ratio of $1.0039\times$. The system is shape-invariant --- a
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critical property for unpredictable real-world workloads.
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\subsection{Attractor Surface}
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Plotting configuration ID (0--127), mean rolling-window size (3900--4300), and
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coefficient of variation (0.14--0.26) reveals a clear attractor structure. Most
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configurations cluster at CV $\approx 0.16$--$0.20$; the basin centers on the
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optimal performance zone; configurations with CV $> 0.22$ are rare and unstable;
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and a 10\% variation in window size still maintains convergence. This geometric
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structure is the foundation for formal verification of convergence via Lyapunov
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stability analysis.
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\subsection{Methodology}
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Data was collected on x86-64 hardware using high-resolution nanosecond timers
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against the fixed 4{,}501-word Forth benchmark, with 30--300 replicates per
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configuration. Quality controls tracked coefficient of variation on every
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measurement, applied z-score outlier detection, monitored CPU thermal stability,
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and held a fixed memory footprint to exclude garbage-collection interference. The
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validation strategy was sequential: a full-factorial map of the design space, a
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head-to-head runoff for the best static configuration, the L8 adaptive-versus-
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static comparison across workload families, and the waveform sweep to demonstrate
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invariance.
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The raw data spans four CSV files ---
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\texttt{doe\_results\_20251123\_093204.csv} (11\,MB, 38{,}400 runs),
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\texttt{runoff\_results.csv} (67\,KB, 240 runs),
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\texttt{l8\_validation\_results.csv} (3.8\,MB, 12{,}000 runs), and
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\texttt{shape\_results.csv} (365\,KB, 1{,}200 runs) --- each carrying 68 columns:
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the L1--L7 configuration bits, performance metrics, state-vector components (heat,
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entropy, decay, pressure), cache and lookup statistics, window and inference
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parameters, and hardware thermal/frequency monitoring.
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%% PATENT: The source also enumerates candidate patent claims (attractor basin
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%% convergence, self-organization without tuning, shape-invariant bounds,
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%% autonomous mode selection) and DARPA pitch material. Omitted here pending Bob's
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%% instruction; no claim language drafted.
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%% TODO(bob): confirm which validation statements may appear in citable form.
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