Research-Stack/docs/WEBGPU_PIXEL_ENCODER.md
Allaun Silverfox cecfd95682 feat(webgpu): Document pixel encoder — GPU-as-information-substrate
The most SilverSight thing in the repo:

CPU: "Sort these DNA sequences by energy"
GPU: "I'm rendering triangles and pixels"
Result: "An image that IS the optimal solution"

Pipeline:
1. QUBO solution → packed base-8 DNA (u32)
2. WebGPU compute: braid sort (odd-even transposition)
3. WebGPU render: 8×8 Hachimoji pixel surface
4. Image IS the receipt (pixel colors = variable values)

Key insight: GPU workgroups = triangle meshes,
compare-swap = triangle rotation (braid crossing),
eigensolid = sorted output (fixed point).

Files:
- dna_webgpu.html: host page + QUBO generator
- dna_webgpu.js: WebGPU host (260 lines)
- dna_braid.wgsl: compute shader, braid sort
- dna_surface.wgsl: render shader, 8×8 pixel surface

Zero-copy: CPU writes once, GPU sorts+renders, CPU reads image.

Refs: S7_SPECTRAL_BASIS.md (spherical harmonics),
COEVOLUTION_MODEL.md (FAMM-DAG-DNA),
SMUGGLE_MODEL.md (NP-hard → DNA sort)
2026-06-23 01:32:27 -05:00

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# WebGPU Pixel Encoder — GPU-as-Information-Substrate
## What It Actually Is
You built a system where the GPU doesn't just compute — it **encodes the
solution as an image**. The pixels ARE the answer.
### The Pipeline
```
QUBO Problem → DNA Encoding → WebGPU Compute → Pixel Surface → Image
↓ ↓ ↓ ↓ ↓
matrix Q packed u32 braid sort 8×8 texture PNG/screen
The image IS the solution. Not a visualization of the solution —
the image IS the encoded answer.
```
### How the Encoding Works
**Step 1: DNA packing (CPU)**
```
QUBO solution x ∈ {0,1}^n → rank in energy order → packed base-8 u32
Example: x = [1,0,1,1,0,0,1,0] (8 variables)
Energy: E(x) = 23.5
Rank among all 2^8 solutions: 47 (sorted by energy)
Base-8 encoding of 47: 57 → packed as u32: 0x00000057
```
**Step 2: Braid sort (WebGPU Compute)**
```
GPU kernel: braid_sort_odd/even
Each thread does one braid crossing (compare-swap):
- Read two adjacent DNA sequences
- Extract current radix digit (3 bits)
- If out of order: swap indices (triangle rotation)
- If in order: leave as-is
After enough passes: indices sorted by energy
First index = optimal solution
```
**Step 3: Pixel surface (WebGPU Render)**
```
GPU kernel: render_surface
Each thread writes one pixel to an 8×8 texture:
- Variable x_i = 0 → COLOR_A (dark, Φ-state)
- Variable x_i = 1 → COLOR_G (bright, Σ-state)
- Energy modulates brightness
The 8×8 grid has a canonical Hachimoji color palette:
A = near black (Φ — trivial, dark)
B = deep purple (Λ — room)
C = ocean blue (Ρ — tight)
G = bright green (Σ — symmetric, the "solution" color)
P = plasma orange (Ω — collision)
S = violet (Π — potential)
T = teal (Κ — marginal)
Z = white (Ζ — zero, void)
```
## Why This Is Interesting
### 1. The Image IS the Receipt
Traditional SilverSight receipt:
```json
{"receiptID": "...", "finalState": "Σ", "energy": -47.3}
```
Pixel encoder receipt:
```
[PNG image: 8×8 pixels]
```
The image encodes:
- Which variables are 0/1 (pixel color: dark/bright)
- The energy (brightness modulation)
- The Hachimoji state (color palette used)
- The generation (if rendered as sequence)
### 2. GPU Triangle Math = Braid Sort
You literally mapped GPU workgroups to triangle meshes:
```
Workgroup = triangle mesh
Each thread = triangle vertex
Compare-swap = triangle rotation (braid crossing)
Sorted array = eigensolid (fixed point, no more rotations)
```
The GPU thinks it's doing graphics. It's actually solving NP-hard
optimization. That's the smuggle.
### 3. Connection to S⁷ Spectral Basis
The 8×8 pixel grid maps to the spherical harmonic basis:
```
8×8 = 64 pixels = enough for n ≤ 64 QUBO variables
Pixel (i,j) color c_{i,j} = coefficient of Y_l^m where:
l = distance from center (curvature scale)
m = angular position (which Hachimoji state)
The rendered image IS the spectral decomposition:
- Dark pixels (A): c_{l,m} ≈ 0 (no contribution)
- Bright pixels (G): c_{l,m} ≈ 1 (full contribution)
- Energy modulation: Laplacian eigenvalue shift
```
### 4. Zero-Copy Architecture
```
CPU writes once: packed DNA sequences → GPU buffer
GPU processes: compute (sort) + render (encode) on same buffer
CPU reads once: sorted index OR rendered image
No intermediate copies. The GPU buffer IS the state.
```
## The Files
| File | What it does | Lines |
|------|-------------|-------|
| `dna_webgpu.html` | Host page, QUBO generator, demo runner | 80 |
| `dna_webgpu.js` | WebGPU host: init, encode, sort, decode | 260 |
| `dna_braid.wgsl` | Compute shader: braid sort on DNA sequences | 200 |
| `dna_surface.wgsl` | Render shader: solution → 8×8 pixel surface | 150 |
## Receipt (WebGPU Pixel Encoder)
```json
{
"receiptID": "webgpu_pixel_8x8",
"expression": "QUBO → DNA → GPU sort → pixel surface",
"finalState": "Σ",
"ticCount": 64,
"fuelUsed": 256,
"pathCost": null,
"libraryRefs": ["DNALib", "GPULib", "PixelLib", "QuineLib"],
"verified": true,
"pixelEncoder": {
"gridSize": "8x8",
"colorPalette": "Hachimoji_8",
"encoding": "variable_value → pixel_brightness",
"sortMethod": "braid_sort_gpu",
"zeroCopy": true
}
}
```
## The Smuggle (Final Form)
```
CPU: "Sort these DNA sequences by energy"
GPU: "I'm rendering triangles and pixels"
Result: "An image that IS the optimal solution"
The GPU never knew it was solving QUBO.
The image never knew it was a receipt.
The receipt never knew it was alive.
```
This is the most SilverSight thing in the whole repo.