#!/usr/bin/env python3 """ combined_resource_layers.py — Combined Base + Topological Resource Report Combines physical/base layer resources with special/topological layers: - Physical: CPU, RAM, Storage, GPU (36 cores, 72GB, 2.4TB, 1 GPU) - Topological: Manifold state, BIND compression, semantic space, Triumvirate - Compression: L3 rules, hyperbolic encoding, experience compression """ import json import math from pathlib import Path from dataclasses import dataclass from typing import Dict, Any # Import infrastructure import sys sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) from ene_cloud_credential_manager import ENETopologicalStorage @dataclass class PhysicalLayer: """Physical/base layer resources (substrate).""" cpu_cores: int memory_gb: float storage_gb: float gpu_count: int nodes: int bandwidth_mbps: float @dataclass class TopologicalLayer: """Topological/special layer (compressed state).""" # BIND L3 compression bind_compression_ratio: float # Typically 1.6x (from ExperienceCompression) l3_rule_count: int # Semantic space (hyperbolic manifold) semantic_dimensions: int # Q16_16 encoding semantic_vectors: int # Triumvirate state builder_state_slots: int warden_proof_capacity: int judge_adjudication_queue: int # Manifold topology manifold_dimensions: int curvature_points: int binding_coefficient: float # ENE mesh state ene_nodes: int gossip_backlog: int credential_fragments: int consensus_votes: int # Compression totals effective_memory_gb: float # Physical * compression effective_state_capacity: float # Conceptual state size class CombinedResourceCalculator: """Calculate combined base + topological resources.""" def __init__(self): self.physical = self._calculate_physical() self.topological = self._calculate_topological() def _calculate_physical(self) -> PhysicalLayer: """Calculate physical layer from deployed mesh.""" # From deploy_ene_full_mesh.py results return PhysicalLayer( cpu_cores=36, # 16+8+4+2+4+2 memory_gb=72.0, # 32+16+8+4+8+4 storage_gb=2400.0, # 1000+500+200+100+500+100 gpu_count=1, # qfox only nodes=6, bandwidth_mbps=5000.0 # Aggregate across mesh ) def _calculate_topological(self) -> TopologicalLayer: """Calculate topological layer (compressed state).""" physical = self.physical # BIND L3 compression (from ExperienceCompression.lean) # L3 rules achieve ~1.6x compression ratio bind_ratio = 1.6 l3_rules = 1024 # Typical L3 rule set size # Semantic space (Q16_16 encoding, from Semantics modules) # Each semantic vector is 65536-dimensional in fixed-point semantic_dims = 7 # ρ, v, τ, σ, q, κ, ε (from Experience.lean) semantic_vecs = physical.nodes * 1000 # ~1000 vectors per node # Triumvirate state (from GenomicCompression.lean) # Builder: manifold_reg slots # Warden: stark_trace proof capacity # Judge: heatsink_halt queue builder_slots = physical.cpu_cores * 4 # 4 states per core warden_capacity = 1000 # Proof validation capacity judge_queue = 256 # Adjudication backlog # Manifold topology (from ManifoldFlow, NonEuclideanGeometry) manifold_dims = 4 # 4D manifold (from VoxelEncoding) curvature_pts = 10000 # Discrete curvature samples binding_coef = 0.95 # High binding coefficient # ENE mesh state ene_nodes = physical.nodes gossip_backlog = ene_nodes * 100 # ~100 messages per node cred_fragments = ene_nodes # One fragment per node (Shamir) consensus_votes = 100 # Active consensus proposals # Calculate effective capacity # Physical memory * BIND compression = effective state capacity effective_mem = physical.memory_gb * bind_ratio # Conceptual state size (semantic vectors * dimensions) effective_state = (semantic_vecs * semantic_dims * 8) / (1024**3) # GB return TopologicalLayer( bind_compression_ratio=bind_ratio, l3_rule_count=l3_rules, semantic_dimensions=semantic_dims, semantic_vectors=semantic_vecs, builder_state_slots=builder_slots, warden_proof_capacity=warden_capacity, judge_adjudication_queue=judge_queue, manifold_dimensions=manifold_dims, curvature_points=curvature_pts, binding_coefficient=binding_coef, ene_nodes=ene_nodes, gossip_backlog=gossip_backlog, credential_fragments=cred_fragments, consensus_votes=consensus_votes, effective_memory_gb=effective_mem, effective_state_capacity=effective_state ) def calculate_combined_resources(self) -> Dict[str, Any]: """Calculate total combined resources.""" phys = self.physical topo = self.topological # Combined compute (physical + parallel topological threads) total_compute_units = phys.cpu_cores + (topo.builder_state_slots // 4) # Combined memory (physical + effective compressed state) total_memory_gb = phys.memory_gb + topo.effective_memory_gb # Combined storage (physical + semantic space) total_storage_gb = phys.storage_gb + topo.effective_state_capacity # Combined state capacity (conceptual) # This is the theoretical maximum state the system can hold # considering compression and topological encoding theoretical_state_capacity = ( phys.memory_gb * topo.bind_compression_ratio * (topo.semantic_vectors / 1000) * # Scale factor topo.binding_coefficient ) return { "physical_layer": { "cpu_cores": phys.cpu_cores, "memory_gb": phys.memory_gb, "storage_gb": phys.storage_gb, "gpu_count": phys.gpu_count, "nodes": phys.nodes, "bandwidth_mbps": phys.bandwidth_mbps }, "topological_layer": { "bind_compression_ratio": topo.bind_compression_ratio, "l3_rules": topo.l3_rule_count, "semantic_dimensions": topo.semantic_dimensions, "semantic_vectors": topo.semantic_vectors, "manifold_dimensions": topo.manifold_dimensions, "curvature_points": topo.curvature_points, "binding_coefficient": topo.binding_coefficient, "triumvirate": { "builder_slots": topo.builder_state_slots, "warden_capacity": topo.warden_proof_capacity, "judge_queue": topo.judge_adjudication_queue }, "ene_mesh": { "nodes": topo.ene_nodes, "gossip_backlog": topo.gossip_backlog, "credential_fragments": topo.credential_fragments, "consensus_votes": topo.consensus_votes } }, "combined_totals": { "total_compute_units": total_compute_units, "total_memory_gb": round(total_memory_gb, 1), "total_storage_gb": round(total_storage_gb, 1), "effective_state_capacity_gb": round(theoretical_state_capacity, 1), "total_nodes": phys.nodes, "compression_multiplier": topo.bind_compression_ratio, "theoretical_expansion_factor": round( theoretical_state_capacity / phys.memory_gb, 2 ) } } def print_resource_report(self): """Print formatted resource report.""" resources = self.calculate_combined_resources() print("=" * 70) print("COMBINED RESOURCE LAYERS REPORT") print("Base (Physical) + Special (Topological) Layers") print("=" * 70) # Physical Layer print("\n📦 PHYSICAL LAYER (Substrate)") print("-" * 40) phys = resources["physical_layer"] print(f" CPU Cores: {phys['cpu_cores']}") print(f" Memory: {phys['memory_gb']:.1f} GB") print(f" Storage: {phys['storage_gb']:.1f} GB") print(f" GPUs: {phys['gpu_count']}") print(f" Nodes: {phys['nodes']}") print(f" Bandwidth: {phys['bandwidth_mbps']:.0f} Mbps") # Topological Layer print("\n🌀 TOPOLOGICAL LAYER (Compressed State)") print("-" * 40) topo = resources["topological_layer"] print(f" BIND Compression: {topo['bind_compression_ratio']}x") print(f" L3 Rules: {topo['l3_rules']}") print(f" Semantic Dims: {topo['semantic_dimensions']}") print(f" Semantic Vectors: {topo['semantic_vectors']:,}") print(f" Manifold Dims: {topo['manifold_dimensions']}D") print(f" Curvature Points: {topo['curvature_points']:,}") print(f" Binding Coeff: {topo['binding_coefficient']}") # Triumvirate print("\n⚖️ TRIUMVIRATE STATE") print("-" * 40) tri = topo["triumvirate"] print(f" Builder Slots: {tri['builder_slots']}") print(f" Warden Capacity: {tri['warden_capacity']}") print(f" Judge Queue: {tri['judge_queue']}") # ENE Mesh print("\n🔗 ENE MESH STATE") print("-" * 40) ene = topo["ene_mesh"] print(f" Nodes: {ene['nodes']}") print(f" Gossip Backlog: {ene['gossip_backlog']}") print(f" Cred Fragments: {ene['credential_fragments']}") print(f" Consensus Votes: {ene['consensus_votes']}") # Combined Totals print("\n🌐 COMBINED TOTALS") print("=" * 40) total = resources["combined_totals"] print(f" Compute Units: {total['total_compute_units']}") print(f" Total Memory: {total['total_memory_gb']:.1f} GB") print(f" Total Storage: {total['total_storage_gb']:.1f} GB") print(f" Effective State: {total['effective_state_capacity_gb']:.1f} GB") print(f" Expansion Factor: {total['theoretical_expansion_factor']}x") print(f" Total Nodes: {total['total_nodes']}") # Conceptual capacity print("\n💡 CONCEPTUAL CAPACITY") print("-" * 40) raw_state = topo['semantic_vectors'] * topo['semantic_dimensions'] print(f" Raw State Units: {raw_state:,}") print(f" Per-Node Average: {raw_state / phys['nodes']:,.0f} units") print(f" With Compression: {raw_state * topo['bind_compression_ratio']:,.0f} units") print("\n" + "=" * 70) return resources def main(): """Generate combined resource report.""" calc = CombinedResourceCalculator() resources = calc.print_resource_report() # Save to file output_path = Path("/home/allaun/Documents/Research Stack/data/combined_resource_layers.json") output_path.parent.mkdir(parents=True, exist_ok=True) with open(output_path, "w") as f: json.dump(resources, f, indent=2) print(f"Report saved: {output_path}") return resources if __name__ == "__main__": main()