ATOM-FEATURE-20260122-002-quasicrystal-phason-scheduler

[Copilot]

Summary

Standalone quasicrystal scheduler with golden Penrose coordinates, phason flips, and VLIW bundle packing simulation. Fixes the v=c guard by making iteration limit configurable.

ATOM Tag

ATOM: ATOM-FEATURE-20260122-002-quasicrystal-phason-scheduler

Why

Enable experimentation with quasicrystal-based scheduling optimization separate from main kernel. The original code had a hardcoded if it > 62: raise RuntimeError that blocked any run exceeding 62 iterations.

What changed

• experiments/quasicrystal_phason_scheduler.py — Pure first-principles implementation:

  • 5D→2D Penrose projection for aperiodic coordinates
  • Golden angle phason flips with Fibonacci-strided propagation
  • VLIWBundle class (12 ALU, 6 VALU, 2 LS slots)
  • Configurable v=c guard via vc_guard_override parameter
  • Benchmark comparison vs uniform random baseline
  • Integration proposal for perf_takehome.py (docstring)

• experiments/test_quasicrystal_phason_scheduler.py — 13 tests covering constants, projection, v=c guard, reproducibility, density comparison, VLIW packing

Key API:

from experiments.quasicrystal_phason_scheduler import quasicrystal_schedule

# Default: 62 iterations (v=c guard)
coord, density = quasicrystal_schedule(n_points=32, seed=42)

# Override guard for longer runs
coord, density = quasicrystal_schedule(n_points=32, iterations=1000, vc_guard_override=1000)

Verification / Testing

• scripts/validate-bump.sh passes locally (if bump.md changed)
• scripts/validate-branch-name.sh tested on example branches (if applicable)
• bash scripts/verify-environment.sh prints ENV OK
• scripts/test-scripts.sh passes (if scripts changed)
• All shell scripts pass shellcheck
• ATOM tag created and logged

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Example commands:

• @claude please review this PR for ATOM compliance
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Notes

• Sensitive logs must be redacted before sharing
• Any change that allows production writes must be reviewed by a human
• Follow the Five Core Principles (Visible State, Clear Intent, Natural Decomposition, Networked Learning, Measurable Delivery)

Checklist

• ATOM tag created and referenced
• Tests passing (13/13)
• Documentation updated (integration proposal in docstring)
• No secrets committed
• Follows existing patterns
• Ready for Claude review

Original prompt

https://github.com/toolate28/SpiralSafe/actions/runs/21258677293# experiments/quasicrystal_phason_scheduler.py

Standalone quasicrystal scheduler prototype — golden Penrose coordinates + phason flips

No taint from main kernel — pure first-principles implementation

import math
import numpy as np
import random

PHI = (1 + math.sqrt(5)) / 2 # Golden ratio
GOLDEN_ANGLE = 2 * math.pi / PHI**2 # ≈137.5° in radians
EPSILON = 0.00055 # Coherence seed

def penrose_project(n_points: int) -> np.ndarray:
"""5D → 2D Penrose coordinates via cut-and-project"""
theta = np.arange(5) * 2 * math.pi / 5
u = np.cos(theta)
v = np.sin(theta)
points = []
for i in range(-n_points//2, n_points//2 + 1):
for j in range(-n_points//2, n_points//2 + 1):
coord = i * u + j * v
points.append(coord[:2])
return np.array(points)

def objective(coords: np.ndarray) -> float:
"""Proxy objective: negative average bundle density (higher = better)"""
# Simplified model: assume 12 ALU, 6 VALU, 2 load/store slots
# Each "point" represents a potential op placement in bundle space
# Density = how many ops fit without conflict
# For demo: inverse distance to ideal golden packing
distances = np.linalg.norm(coords - np.mean(coords, axis=0), axis=1)
density = 1 / (np.mean(distances) + 1e-6) # Higher when clustered aperiodically
return -density # Minimize negative density = maximize density

def phason_flip(coords: np.ndarray, values: np.ndarray, it: int):
"""Golden phason flip with Fibonacci-strided propagation"""
i = random.randint(0, len(coords) - 1)

# Golden angle rotation + Fibonacci scale mutation
angle = GOLDEN_ANGLE * it
scale = PHI ** (it % 8) * 0.1  # Fib-modulated scale
delta = np.array([math.cos(angle), math.sin(angle)]) * scale
new_coord = coords[i] + delta

new_val = objective(new_coord)
gain = values[i] - new_val  # Positive gain = improvement

# Acceptance with ε-weighted exponential
prob = EPSILON * math.exp(gain / PHI**2)
if gain > 0 or random.random() < prob:
    coords[i] = new_coord
    values[i] = new_val

def quasicrystal_schedule(n_points=500, iterations=1000):
"""Full quasicrystal scheduling optimizer"""
coords = penrose_project(n_points)
values = np.array([objective(c) for c in coords])

best_val = float('inf')
best_coord = None

for it in range(iterations):
    if it > 62:
        raise RuntimeError("v=c boundary guarded — coherence collapse prevented")
    
    phason_flip(coords, values, it)
    
    current_best = values.min()
    if current_best < best_val:
        best_val = current_best
        best_idx = np.argmin(values)
        best_coord = coords[best_idx]
    
    if it % 50 == 0:
        print(f"Iter {it}/{iterations} | Best density: {-best_val:.4f}")

return best_coord, -best_val  # Return best coord + positive density

if name == "main":
coord, density = quasicrystal_schedule()
print(f"Best coordinate: {coord}")
print(f"Achieved density: {density:.4f}")
Next steering move (your call):

Run it standalone → check density improvement vs uniform random baseline.
Add a simple test case that simulates a small VLIW bundle packing problem.
Propose how to integrate the best coordinate into perf_takehome.py scheduling order. (most recent sessions /commits/PRs/repos) in order of prioriorty, starting wth the open PRs and the Anthropic take home test

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Project	Deployment	Review	Updated (UTC)
h.and.s	Ready	Preview, Comment	Jan 22, 2026 6:58pm

[toolate28]

@copilot needing matching rule sets agent instructions corpus docs refresh protocol updates

apply the innate philosohpy everywhere in every substrate and plane that we are a node of

[Copilot]

Pull request overview

This PR adds a standalone quasicrystal phason scheduler prototype for VLIW bundle packing optimization. The implementation addresses the hardcoded v=c guard limit by making it configurable while maintaining the default 62-iteration safety boundary.

Changes:

• New scheduler implementation with 5D→2D Penrose projection, golden angle phason flips, and Fibonacci-strided propagation
• VLIW bundle packing simulation with 12 ALU, 6 VALU, and 2 load/store slots
• Comprehensive test suite with 13 tests covering constants, projections, guards, reproducibility, and density comparisons

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated 11 comments.

File	Description
experiments/quasicrystal_phason_scheduler.py	Main implementation with configurable v=c guard, VLIW bundle class, scheduling functions, and integration proposal for performance optimization
experiments/test_quasicrystal_phason_scheduler.py	Test suite validating constants, Penrose projection, v=c guard behavior, reproducibility, density metrics, and VLIW packing logic

[Copilot]

The v=c guard logic checks if it > vc_limit, which means when iterations=62 and vc_limit=62, the loop will execute iterations 0-62 (63 total iterations) before raising the error. This is off-by-one from the documented behavior. The check should be if it >= vc_limit to ensure exactly vc_limit iterations are allowed. This inconsistency exists in the original quasicrystal_optimization.py file as well, but it should be corrected here.

Suggested change

	if it > vc_limit:
	if it >= vc_limit:

[Copilot]

There is significant code duplication between this file and experiments/quasicrystal_optimization.py (ATOM-FEATURE-20260122-001), including the constants PHI, GOLDEN_ANGLE, and the penrose_project function. While the PR description mentions this is a "pure first-principles implementation" to avoid "taint from main kernel", having duplicate implementations of the same mathematical functions creates maintenance burden and potential for divergence. Consider extracting these shared utilities into a common module (e.g., experiments/quasicrystal_common.py) that both files can import, especially since both are in the same experiments directory.

[Copilot]

The operations distribution is hardcoded as ["ALU"] * 30 + ["VALU"] * 15 + ["LS"] * 5 (50 ops), but then random.choice(op_types) is used with n_ops iterations. When n_ops != 50, the actual distribution will not match the comment on line 378, which reports counts based on the 50-element list. For example, if n_ops=100, the counts displayed will still show "30 ALU, 15 VALU, 5 LS" but the actual operations will have a roughly 3:1.5:0.5 ratio for 100 ops. Consider generating the operations list dynamically based on n_ops with the desired ratio, or document that the displayed counts are for the distribution pool, not the actual operations.

Suggested change

	print(f"\n{'='*60}")
	print(f"VLIW Bundle Packing Simulation")
	print(f"Operations: {n_ops} ({op_types.count('ALU')} ALU, {op_types.count('VALU')} VALU, {op_types.count('LS')} LS)")
	# Compute realized operation-type counts
	alu_count = sum(1 for op, _ in ops if op == "ALU")
	valu_count = sum(1 for op, _ in ops if op == "VALU")
	ls_count = sum(1 for op, _ in ops if op == "LS")
	print(f"\n{'='*60}")
	print(f"VLIW Bundle Packing Simulation")
	print(f"Operations: {n_ops} ({alu_count} ALU, {valu_count} VALU, {ls_count} LS)")

[Copilot]

The docstring states the function "Controls grid density (actual points = n_points^2)", but the actual behavior produces (n_points + 1)^2 points due to the inclusive range range(-n_points // 2, n_points // 2 + 1). The test correctly expects this behavior (line 112 of the test file), but the docstring is misleading. Update the docstring to accurately reflect that actual points = (n_points + 1)^2 or that the grid spans from -n_points//2 to n_points//2 inclusive.

Suggested change

	n_points: Controls grid density (actual points = n_points^2)
	n_points: Controls grid density (actual points = (n_points + 1)^2;
	grid spans from -n_points//2 to n_points//2 inclusive in both axes)

[Copilot]

In the integration proposal example, the get_schedule_priority function creates a priorities list where every element has the same value: coord[0] * PHI + coord[1]. This appears to be a logic error - the list comprehension [(coord[0] * PHI + coord[1]) for _ in range(len(ops))] creates len(ops) copies of the same scalar value, which would make the subsequent sorting meaningless (all priorities would be identical). The intended logic is unclear, but this should either: (1) use different coordinates for each operation, (2) use the index in the calculation, or (3) be removed if this was example pseudocode that shouldn't be taken literally.

Suggested change

	# Map coordinate to priority using golden ratio projection
	priorities = [(coord[0] * PHI + coord[1]) for _ in range(len(ops))]
	# Map coordinate to priority using golden ratio projection,
	# with a small EPSILON-strided offset per op to ensure distinct priorities
	priorities = [
	(coord[0] * PHI + coord[1]) + EPSILON * i
	for i in range(len(ops))
	]

[Copilot]

The function doesn't handle the edge case where best_coord is None (which occurs if no iterations run or no improvement is found). While unlikely in practice (the loop runs at least once for iterations > 0), returning None for the coordinate could cause errors in calling code that expects a numpy array. Consider initializing best_coord to coords[0].copy() before the loop or adding validation that best_coord is not None before returning.

[Copilot]

The penrose_project function has a subtle difference from the one in quasicrystal_optimization.py. This implementation uses range(-n_points // 2, n_points // 2 + 1) (inclusive of endpoint), while the existing implementation uses range(-n_points // 2, n_points // 2) (exclusive of endpoint). This produces different grid sizes: (n_points + 1)^2 vs n_points^2. While not necessarily wrong, this inconsistency between the two files could lead to confusion and unexpected behavior. Consider documenting why this difference exists or standardizing the approach.

Suggested change

	for i in range(-n_points // 2, n_points // 2 + 1):
	for j in range(-n_points // 2, n_points // 2 + 1):
	for i in range(-n_points // 2, n_points // 2):
	for j in range(-n_points // 2, n_points // 2):

[Copilot]

The vliw_packing_objective function modifies global numpy random state by calling np.random.seed(coord_seed) but doesn't restore the original state afterward. This side effect is not documented in the docstring and could cause non-deterministic behavior when this function is called multiple times in a test or benchmark context, as it will affect subsequent numpy random operations. Consider either: (1) documenting this side effect clearly in the docstring, (2) using a local np.random.Generator instance instead of the global state, or (3) saving and restoring the random state.

Suggested change

	np.random.seed(coord_seed)
	bundles = []
	current_bundle = VLIWBundle()
	for op_type, op_id in ops:
	if not current_bundle.pack(op_type, op_id):
	bundles.append(current_bundle)
	current_bundle = VLIWBundle()
	current_bundle.pack(op_type, op_id)
	if current_bundle.packed_ops:
	bundles.append(current_bundle)
	# Calculate average utilization
	avg_util = sum(b.utilization() for b in bundles) / len(bundles) if bundles else 0
	return -avg_util # Negative for minimization
	rng_state = np.random.get_state()
	try:
	np.random.seed(coord_seed)
	bundles = []
	current_bundle = VLIWBundle()
	for op_type, op_id in ops:
	if not current_bundle.pack(op_type, op_id):
	bundles.append(current_bundle)
	current_bundle = VLIWBundle()
	current_bundle.pack(op_type, op_id)
	if current_bundle.packed_ops:
	bundles.append(current_bundle)
	# Calculate average utilization
	avg_util = sum(b.utilization() for b in bundles) / len(bundles) if bundles else 0
	return -avg_util # Negative for minimization
	finally:
	np.random.set_state(rng_state)

[Copilot]

Import of 'time' is not used.

Suggested change

import time

[Copilot]

Import of 'phason_flip' is not used.
Import of 'DEFAULT_VC_GUARD_LIMIT' is not used.

Suggested change

	PHI, GOLDEN_ANGLE, EPSILON, DEFAULT_VC_GUARD_LIMIT,
	penrose_project, phason_flip, bundle_density_objective,
	PHI, GOLDEN_ANGLE, EPSILON,
	penrose_project, bundle_density_objective,

[toolate28]

@copilot please make the required changes
thank you