#!/usr/bin/env python3 """ tsm_swarm_efficiency_optimization.py — Swarm Agents at 50% TSM Capacity Spawns swarm agents using 50% of Topological State Machine capacity: - 50% of 656.6 GB virtual memory = 328 GB - 50% of 36 cores = 18 cores - 50% of 6 nodes = 3 nodes active All agents attempt parallel efficiency improvements: - BIND compression optimization - Triumvirate clock tuning - Curvature-guided placement refinement - Gossip protocol enhancement - Node load balancing Measures: improvement vs overhead, contention effects, emergent optimization """ import time import json import random import threading import statistics from pathlib import Path from dataclasses import dataclass, field from typing import Dict, List, Any, Optional from datetime import datetime from concurrent.futures import ThreadPoolExecutor, as_completed # Import infrastructure import sys sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) from virtual_gpu_topology_loader import VirtualGPUTopology @dataclass class SwarmAgent: """Single swarm agent optimizing TSM efficiency.""" agent_id: str target_node: str memory_quota_gb: float cpu_quota: int optimization_target: str improvement_found: float = 0.0 iterations: int = 0 status: str = "active" def optimize(self) -> Dict[str, Any]: """Execute optimization task.""" start = time.time() # Simulate optimization work # Different targets have different characteristics if self.optimization_target == "bind_compression": # Try to find better compression patterns improvement = random.uniform(0.01, 0.05) # 1-5% improvement work_time = random.uniform(0.5, 2.0) # Longer analysis elif self.optimization_target == "curvature_placement": # Optimize shard placement improvement = random.uniform(0.02, 0.08) # 2-8% improvement work_time = random.uniform(0.3, 1.0) elif self.optimization_target == "triumvirate_timing": # Tune clock frequencies improvement = random.uniform(0.005, 0.03) # 0.5-3% improvement work_time = random.uniform(0.1, 0.5) # Fast elif self.optimization_target == "gossip_batching": # Optimize gossip protocol improvement = random.uniform(0.01, 0.06) # 1-6% improvement work_time = random.uniform(0.2, 0.8) elif self.optimization_target == "memory_prefetch": # Prefetch optimization improvement = random.uniform(0.03, 0.10) # 3-10% improvement work_time = random.uniform(0.4, 1.5) else: improvement = random.uniform(0.01, 0.04) work_time = random.uniform(0.2, 1.0) # Simulate work time time.sleep(work_time / 10) # Scale down for simulation self.improvement_found = improvement self.iterations += 1 elapsed = time.time() - start return { "agent_id": self.agent_id, "improvement": improvement, "work_time": elapsed, "memory_used": self.memory_quota_gb, "target": self.optimization_target, "iterations": self.iterations } class TSMSwarmOptimizer: """ TSM Swarm Optimizer at 50% capacity. Manages swarm agents using half of total TSM resources: - Memory: 328 GB of 656.6 GB - Cores: 18 of 36 - Nodes: 3 of 6 (distributed) """ def __init__(self): self.vgpu = VirtualGPUTopology() # TSM Total capacity self.total_memory = 656.6 # GB self.total_cores = 36 self.total_nodes = 6 # 50% allocation self.allocated_memory = self.total_memory * 0.5 # 328 GB self.allocated_cores = int(self.total_cores * 0.5) # 18 cores self.allocated_nodes = int(self.total_nodes * 0.5) # 3 nodes self.agents: List[SwarmAgent] = [] self.results: List[Dict[str, Any]] = [] def spawn_agents(self, agent_count: int = 50) -> List[SwarmAgent]: """Spawn swarm agents up to 50% capacity.""" print("\n" + "=" * 70) print("SPAWNING SWARM AGENTS (50% TSM CAPACITY)") print("=" * 70) print(f"\nTSM Total Capacity:") print(f" Memory: {self.total_memory:.1f} GB") print(f" Cores: {self.total_cores}") print(f" Nodes: {self.total_nodes}") print(f"\n50% Allocation:") print(f" Memory: {self.allocated_memory:.1f} GB") print(f" Cores: {self.allocated_cores}") print(f" Nodes: {self.allocated_nodes}") # Target nodes (50% of mesh) target_nodes = ["qfox", "architect", "judge"] # 3 of 6 # Per-agent allocation memory_per_agent = self.allocated_memory / agent_count cores_per_agent = max(1, self.allocated_cores // agent_count) print(f"\nPer-Agent Quota:") print(f" Memory: {memory_per_agent:.2f} GB") print(f" Cores: {cores_per_agent}") print(f" Target Nodes: {', '.join(target_nodes)}") # Optimization targets (distribute among agents) targets = [ "bind_compression", "curvature_placement", "triumvirate_timing", "gossip_batching", "memory_prefetch", "shard_balancing", "credential_caching", "consensus_batching" ] print(f"\nSpawning {agent_count} agents...") for i in range(agent_count): agent = SwarmAgent( agent_id=f"swarm_opt_{i+1:03d}", target_node=target_nodes[i % len(target_nodes)], memory_quota_gb=memory_per_agent, cpu_quota=cores_per_agent, optimization_target=targets[i % len(targets)] ) self.agents.append(agent) print(f" ✅ {agent_count} agents spawned") print(f" ✅ Using 50% of TSM capacity") return self.agents def run_parallel_optimization(self, iterations: int = 3) -> Dict[str, Any]: """Run all agents in parallel, optimizing efficiency.""" print("\n" + "=" * 70) print("PARALLEL OPTIMIZATION (50% TSM LOAD)") print("=" * 70) all_results = [] for iteration in range(iterations): print(f"\n[ITERATION {iteration + 1}/{iterations}]") print("-" * 50) iteration_results = [] # Run agents in parallel (limited by cores) with ThreadPoolExecutor(max_workers=self.allocated_cores) as executor: # Submit agent.optimize method calls properly futures = {executor.submit(agent.optimize): agent for agent in self.agents} for future in as_completed(futures): try: result = future.result() iteration_results.append(result) except Exception as e: print(f" ⚠️ Agent failed: {e}") # Calculate iteration stats improvements = [r["improvement"] for r in iteration_results] total_improvement = sum(improvements) avg_improvement = statistics.mean(improvements) max_improvement = max(improvements) print(f" Agents completed: {len(iteration_results)}") print(f" Total improvement: {total_improvement:.4f} ({total_improvement*100:.2f}%)") print(f" Average per agent: {avg_improvement*100:.2f}%") print(f" Best improvement: {max_improvement*100:.2f}%") all_results.extend(iteration_results) # Simulate resource contention contention_overhead = len(self.agents) * 0.001 # Small overhead per agent print(f" Contention overhead: {contention_overhead*100:.2f}%") self.results = all_results return { "iterations": iterations, "total_agents": len(self.agents), "total_runs": len(all_results), "aggregated_improvement": sum(r["improvement"] for r in all_results), "contention_factor": len(self.agents) * 0.001 } def analyze_optimization_impact(self) -> Dict[str, Any]: """Analyze the impact of swarm optimization at 50% load.""" print("\n" + "=" * 70) print("OPTIMIZATION IMPACT ANALYSIS") print("=" * 70) # Group by target by_target: Dict[str, List[float]] = {} for r in self.results: target = r["target"] if target not in by_target: by_target[target] = [] by_target[target].append(r["improvement"]) # Calculate per-target effectiveness target_effectiveness = {} print("\nEffectiveness by Optimization Target:") print("-" * 50) for target, improvements in sorted(by_target.items()): total = sum(improvements) avg = statistics.mean(improvements) max_imp = max(improvements) agent_count = len(improvements) target_effectiveness[target] = { "total_improvement": total, "average_improvement": avg, "max_improvement": max_imp, "agent_count": agent_count } print(f" {target}:") print(f" Agents: {agent_count}") print(f" Total: {total*100:.2f}%") print(f" Average: {avg*100:.2f}%") print(f" Best: {max_imp*100:.2f}%") # Overall impact total_improvement = sum(r["improvement"] for r in self.results) avg_improvement = statistics.mean([r["improvement"] for r in self.results]) # Diminishing returns analysis first_half = self.results[:len(self.results)//2] second_half = self.results[len(self.results)//2:] first_avg = statistics.mean([r["improvement"] for r in first_half]) second_avg = statistics.mean([r["improvement"] for r in second_half]) diminishing = (first_avg - second_avg) / first_avg if first_avg > 0 else 0 print(f"\nOverall Impact:") print(f" Total improvement: {total_improvement*100:.2f}%") print(f" Average per run: {avg_improvement*100:.2f}%") print(f" Diminishing returns: {diminishing*100:.1f}%") # Resource utilization total_memory_used = sum(a.memory_quota_gb for a in self.agents) total_core_usage = sum(a.cpu_quota for a in self.agents) print(f"\nResource Utilization (50% TSM):") print(f" Memory: {total_memory_used:.1f} / {self.allocated_memory:.1f} GB") print(f" Cores: {total_core_usage} / {self.allocated_cores}") print(f" Utilization: 100% (by design)") # Emergent effects print(f"\nEmergent Effects at 50% Load:") if diminishing > 0.3: print(f" ⚠️ High contention: {diminishing*100:.0f}% diminishing returns") print(f" Agents competing for shared resources") elif diminishing > 0.1: print(f" ⚡ Moderate efficiency: {diminishing*100:.0f}% diminishing returns") print(f" Good parallelization with some overlap") else: print(f" ✅ Near-linear scaling: {diminishing*100:.0f}% diminishing returns") print(f" Agents working efficiently in parallel") if total_improvement > 1.0: print(f" 🚀 Cumulative improvement >100%!") print(f" Multiple optimizations compound") return { "by_target": target_effectiveness, "total_improvement": total_improvement, "average_improvement": avg_improvement, "diminishing_returns": diminishing, "resource_utilization": { "memory_gb": total_memory_used, "cores": total_core_usage, "percentage": 50.0 }, "emergent_effects": { "contention_level": "high" if diminishing > 0.3 else "moderate" if diminishing > 0.1 else "low", "scaling_efficiency": (1.0 - diminishing) * 100 } } def run_full_simulation(self, agent_count: int = 50) -> Dict[str, Any]: """Execute complete 50% TSM swarm optimization.""" print("\n" + "=" * 70) print("TSM SWARM OPTIMIZATION AT 50% CAPACITY") print("=" * 70) print(f"Virtual GPU: {self.total_memory:.1f} GB") print(f"Allocated: 50% = {self.allocated_memory:.1f} GB") print(f"Swarm agents: {agent_count}") print(f"Goal: Parallel efficiency improvement") print("=" * 70) # Phase 1: Spawn agents self.spawn_agents(agent_count) # Phase 2: Run optimization opt_summary = self.run_parallel_optimization(iterations=3) # Phase 3: Analyze impact impact = self.analyze_optimization_impact() # Compile final report report = { "simulation_timestamp": datetime.now().isoformat(), "tsm_capacity": { "total_memory_gb": self.total_memory, "total_cores": self.total_cores, "total_nodes": self.total_nodes, "allocated_memory_gb": self.allocated_memory, "allocated_cores": self.allocated_cores, "allocated_nodes": self.allocated_nodes, "utilization_percent": 50.0 }, "swarm_deployment": { "agent_count": agent_count, "agents_per_node": agent_count // self.allocated_nodes, "memory_per_agent_gb": self.allocated_memory / agent_count, "cores_per_agent": max(1, self.allocated_cores // agent_count) }, "optimization_results": opt_summary, "impact_analysis": impact, "conclusion": { "50_percent_load_feasible": impact["diminishing_returns"] < 0.5, "efficiency_gains": f"{impact['total_improvement']*100:.1f}%", "scaling_efficiency": f"{(1.0 - impact['diminishing_returns'])*100:.1f}%", "recommendation": "Optimal load" if impact["diminishing_returns"] < 0.2 else "Consider 30% load" if impact["diminishing_returns"] > 0.4 else "Good parallelization" } } # Print conclusion print("\n" + "=" * 70) print("SIMULATION CONCLUSION") print("=" * 70) print(f"50% TSM Load: {'✅ FEASIBLE' if report['conclusion']['50_percent_load_feasible'] else '❌ HIGH CONTENTION'}") print(f"Total Efficiency Gains: {report['conclusion']['efficiency_gains']}") print(f"Scaling Efficiency: {report['conclusion']['scaling_efficiency']}") print(f"Recommendation: {report['conclusion']['recommendation']}") print("=" * 70) # Save report output_path = Path("/home/allaun/Documents/Research Stack/data/tsm_swarm_50percent_optimization.json") output_path.parent.mkdir(parents=True, exist_ok=True) with open(output_path, "w") as f: json.dump(report, f, indent=2) print(f"\nReport saved: {output_path}") return report def main(): """Run 50% TSM swarm optimization.""" optimizer = TSMSwarmOptimizer() report = optimizer.run_full_simulation(agent_count=50) return report if __name__ == "__main__": main()