mirror of
https://github.com/allaunthefox/Research-Stack.git
synced 2026-07-31 03:05:21 +00:00
413 lines
16 KiB
Python
413 lines
16 KiB
Python
#!/usr/bin/env python3
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"""
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tsm_swarm_efficiency_optimization.py — Swarm Agents at 50% TSM Capacity
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Spawns swarm agents using 50% of Topological State Machine capacity:
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- 50% of 656.6 GB virtual memory = 328 GB
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- 50% of 36 cores = 18 cores
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- 50% of 6 nodes = 3 nodes active
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All agents attempt parallel efficiency improvements:
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- BIND compression optimization
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- Triumvirate clock tuning
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- Curvature-guided placement refinement
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- Gossip protocol enhancement
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- Node load balancing
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Measures: improvement vs overhead, contention effects, emergent optimization
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"""
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import time
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import json
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import random
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import threading
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import statistics
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from pathlib import Path
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from dataclasses import dataclass, field
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from typing import Dict, List, Any, Optional
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from datetime import datetime
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from concurrent.futures import ThreadPoolExecutor, as_completed
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# Import infrastructure
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import sys
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sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra"))
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from virtual_gpu_topology_loader import VirtualGPUTopology
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@dataclass
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class SwarmAgent:
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"""Single swarm agent optimizing TSM efficiency."""
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agent_id: str
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target_node: str
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memory_quota_gb: float
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cpu_quota: int
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optimization_target: str
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improvement_found: float = 0.0
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iterations: int = 0
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status: str = "active"
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def optimize(self) -> Dict[str, Any]:
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"""Execute optimization task."""
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start = time.time()
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# Simulate optimization work
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# Different targets have different characteristics
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if self.optimization_target == "bind_compression":
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# Try to find better compression patterns
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improvement = random.uniform(0.01, 0.05) # 1-5% improvement
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work_time = random.uniform(0.5, 2.0) # Longer analysis
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elif self.optimization_target == "curvature_placement":
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# Optimize shard placement
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improvement = random.uniform(0.02, 0.08) # 2-8% improvement
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work_time = random.uniform(0.3, 1.0)
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elif self.optimization_target == "triumvirate_timing":
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# Tune clock frequencies
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improvement = random.uniform(0.005, 0.03) # 0.5-3% improvement
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work_time = random.uniform(0.1, 0.5) # Fast
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elif self.optimization_target == "gossip_batching":
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# Optimize gossip protocol
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improvement = random.uniform(0.01, 0.06) # 1-6% improvement
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work_time = random.uniform(0.2, 0.8)
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elif self.optimization_target == "memory_prefetch":
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# Prefetch optimization
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improvement = random.uniform(0.03, 0.10) # 3-10% improvement
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work_time = random.uniform(0.4, 1.5)
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else:
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improvement = random.uniform(0.01, 0.04)
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work_time = random.uniform(0.2, 1.0)
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# Simulate work time
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time.sleep(work_time / 10) # Scale down for simulation
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self.improvement_found = improvement
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self.iterations += 1
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elapsed = time.time() - start
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return {
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"agent_id": self.agent_id,
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"improvement": improvement,
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"work_time": elapsed,
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"memory_used": self.memory_quota_gb,
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"target": self.optimization_target,
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"iterations": self.iterations
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}
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class TSMSwarmOptimizer:
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"""
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TSM Swarm Optimizer at 50% capacity.
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Manages swarm agents using half of total TSM resources:
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- Memory: 328 GB of 656.6 GB
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- Cores: 18 of 36
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- Nodes: 3 of 6 (distributed)
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"""
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def __init__(self):
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self.vgpu = VirtualGPUTopology()
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# TSM Total capacity
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self.total_memory = 656.6 # GB
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self.total_cores = 36
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self.total_nodes = 6
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# 50% allocation
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self.allocated_memory = self.total_memory * 0.5 # 328 GB
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self.allocated_cores = int(self.total_cores * 0.5) # 18 cores
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self.allocated_nodes = int(self.total_nodes * 0.5) # 3 nodes
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self.agents: List[SwarmAgent] = []
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self.results: List[Dict[str, Any]] = []
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def spawn_agents(self, agent_count: int = 50) -> List[SwarmAgent]:
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"""Spawn swarm agents up to 50% capacity."""
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print("\n" + "=" * 70)
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print("SPAWNING SWARM AGENTS (50% TSM CAPACITY)")
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print("=" * 70)
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print(f"\nTSM Total Capacity:")
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print(f" Memory: {self.total_memory:.1f} GB")
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print(f" Cores: {self.total_cores}")
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print(f" Nodes: {self.total_nodes}")
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print(f"\n50% Allocation:")
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print(f" Memory: {self.allocated_memory:.1f} GB")
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print(f" Cores: {self.allocated_cores}")
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print(f" Nodes: {self.allocated_nodes}")
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# Target nodes (50% of mesh)
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target_nodes = ["qfox", "architect", "judge"] # 3 of 6
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# Per-agent allocation
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memory_per_agent = self.allocated_memory / agent_count
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cores_per_agent = max(1, self.allocated_cores // agent_count)
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print(f"\nPer-Agent Quota:")
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print(f" Memory: {memory_per_agent:.2f} GB")
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print(f" Cores: {cores_per_agent}")
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print(f" Target Nodes: {', '.join(target_nodes)}")
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# Optimization targets (distribute among agents)
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targets = [
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"bind_compression",
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"curvature_placement",
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"triumvirate_timing",
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"gossip_batching",
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"memory_prefetch",
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"shard_balancing",
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"credential_caching",
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"consensus_batching"
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]
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print(f"\nSpawning {agent_count} agents...")
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for i in range(agent_count):
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agent = SwarmAgent(
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agent_id=f"swarm_opt_{i+1:03d}",
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target_node=target_nodes[i % len(target_nodes)],
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memory_quota_gb=memory_per_agent,
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cpu_quota=cores_per_agent,
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optimization_target=targets[i % len(targets)]
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)
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self.agents.append(agent)
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print(f" ✅ {agent_count} agents spawned")
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print(f" ✅ Using 50% of TSM capacity")
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return self.agents
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def run_parallel_optimization(self, iterations: int = 3) -> Dict[str, Any]:
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"""Run all agents in parallel, optimizing efficiency."""
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print("\n" + "=" * 70)
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print("PARALLEL OPTIMIZATION (50% TSM LOAD)")
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print("=" * 70)
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all_results = []
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for iteration in range(iterations):
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print(f"\n[ITERATION {iteration + 1}/{iterations}]")
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print("-" * 50)
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iteration_results = []
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# Run agents in parallel (limited by cores)
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with ThreadPoolExecutor(max_workers=self.allocated_cores) as executor:
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# Submit agent.optimize method calls properly
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futures = {executor.submit(agent.optimize): agent for agent in self.agents}
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for future in as_completed(futures):
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try:
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result = future.result()
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iteration_results.append(result)
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except Exception as e:
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print(f" ⚠️ Agent failed: {e}")
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# Calculate iteration stats
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improvements = [r["improvement"] for r in iteration_results]
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total_improvement = sum(improvements)
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avg_improvement = statistics.mean(improvements)
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max_improvement = max(improvements)
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print(f" Agents completed: {len(iteration_results)}")
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print(f" Total improvement: {total_improvement:.4f} ({total_improvement*100:.2f}%)")
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print(f" Average per agent: {avg_improvement*100:.2f}%")
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print(f" Best improvement: {max_improvement*100:.2f}%")
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all_results.extend(iteration_results)
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# Simulate resource contention
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contention_overhead = len(self.agents) * 0.001 # Small overhead per agent
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print(f" Contention overhead: {contention_overhead*100:.2f}%")
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self.results = all_results
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return {
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"iterations": iterations,
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"total_agents": len(self.agents),
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"total_runs": len(all_results),
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"aggregated_improvement": sum(r["improvement"] for r in all_results),
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"contention_factor": len(self.agents) * 0.001
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}
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def analyze_optimization_impact(self) -> Dict[str, Any]:
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"""Analyze the impact of swarm optimization at 50% load."""
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print("\n" + "=" * 70)
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print("OPTIMIZATION IMPACT ANALYSIS")
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print("=" * 70)
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# Group by target
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by_target: Dict[str, List[float]] = {}
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for r in self.results:
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target = r["target"]
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if target not in by_target:
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by_target[target] = []
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by_target[target].append(r["improvement"])
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# Calculate per-target effectiveness
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target_effectiveness = {}
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print("\nEffectiveness by Optimization Target:")
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print("-" * 50)
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for target, improvements in sorted(by_target.items()):
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total = sum(improvements)
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avg = statistics.mean(improvements)
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max_imp = max(improvements)
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agent_count = len(improvements)
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target_effectiveness[target] = {
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"total_improvement": total,
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"average_improvement": avg,
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"max_improvement": max_imp,
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"agent_count": agent_count
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}
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print(f" {target}:")
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print(f" Agents: {agent_count}")
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print(f" Total: {total*100:.2f}%")
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print(f" Average: {avg*100:.2f}%")
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print(f" Best: {max_imp*100:.2f}%")
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# Overall impact
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total_improvement = sum(r["improvement"] for r in self.results)
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avg_improvement = statistics.mean([r["improvement"] for r in self.results])
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# Diminishing returns analysis
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first_half = self.results[:len(self.results)//2]
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second_half = self.results[len(self.results)//2:]
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first_avg = statistics.mean([r["improvement"] for r in first_half])
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second_avg = statistics.mean([r["improvement"] for r in second_half])
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diminishing = (first_avg - second_avg) / first_avg if first_avg > 0 else 0
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print(f"\nOverall Impact:")
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print(f" Total improvement: {total_improvement*100:.2f}%")
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print(f" Average per run: {avg_improvement*100:.2f}%")
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print(f" Diminishing returns: {diminishing*100:.1f}%")
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# Resource utilization
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total_memory_used = sum(a.memory_quota_gb for a in self.agents)
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total_core_usage = sum(a.cpu_quota for a in self.agents)
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print(f"\nResource Utilization (50% TSM):")
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print(f" Memory: {total_memory_used:.1f} / {self.allocated_memory:.1f} GB")
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print(f" Cores: {total_core_usage} / {self.allocated_cores}")
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print(f" Utilization: 100% (by design)")
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# Emergent effects
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print(f"\nEmergent Effects at 50% Load:")
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if diminishing > 0.3:
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print(f" ⚠️ High contention: {diminishing*100:.0f}% diminishing returns")
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print(f" Agents competing for shared resources")
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elif diminishing > 0.1:
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print(f" ⚡ Moderate efficiency: {diminishing*100:.0f}% diminishing returns")
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print(f" Good parallelization with some overlap")
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else:
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print(f" ✅ Near-linear scaling: {diminishing*100:.0f}% diminishing returns")
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print(f" Agents working efficiently in parallel")
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if total_improvement > 1.0:
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print(f" 🚀 Cumulative improvement >100%!")
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print(f" Multiple optimizations compound")
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return {
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"by_target": target_effectiveness,
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"total_improvement": total_improvement,
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"average_improvement": avg_improvement,
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"diminishing_returns": diminishing,
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"resource_utilization": {
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"memory_gb": total_memory_used,
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"cores": total_core_usage,
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"percentage": 50.0
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},
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"emergent_effects": {
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"contention_level": "high" if diminishing > 0.3 else "moderate" if diminishing > 0.1 else "low",
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"scaling_efficiency": (1.0 - diminishing) * 100
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}
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}
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def run_full_simulation(self, agent_count: int = 50) -> Dict[str, Any]:
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"""Execute complete 50% TSM swarm optimization."""
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print("\n" + "=" * 70)
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print("TSM SWARM OPTIMIZATION AT 50% CAPACITY")
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print("=" * 70)
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print(f"Virtual GPU: {self.total_memory:.1f} GB")
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print(f"Allocated: 50% = {self.allocated_memory:.1f} GB")
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print(f"Swarm agents: {agent_count}")
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print(f"Goal: Parallel efficiency improvement")
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print("=" * 70)
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# Phase 1: Spawn agents
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self.spawn_agents(agent_count)
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# Phase 2: Run optimization
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opt_summary = self.run_parallel_optimization(iterations=3)
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# Phase 3: Analyze impact
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impact = self.analyze_optimization_impact()
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# Compile final report
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report = {
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"simulation_timestamp": datetime.now().isoformat(),
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"tsm_capacity": {
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"total_memory_gb": self.total_memory,
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"total_cores": self.total_cores,
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"total_nodes": self.total_nodes,
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"allocated_memory_gb": self.allocated_memory,
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"allocated_cores": self.allocated_cores,
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"allocated_nodes": self.allocated_nodes,
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"utilization_percent": 50.0
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},
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"swarm_deployment": {
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"agent_count": agent_count,
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"agents_per_node": agent_count // self.allocated_nodes,
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"memory_per_agent_gb": self.allocated_memory / agent_count,
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"cores_per_agent": max(1, self.allocated_cores // agent_count)
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},
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"optimization_results": opt_summary,
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"impact_analysis": impact,
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"conclusion": {
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"50_percent_load_feasible": impact["diminishing_returns"] < 0.5,
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"efficiency_gains": f"{impact['total_improvement']*100:.1f}%",
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"scaling_efficiency": f"{(1.0 - impact['diminishing_returns'])*100:.1f}%",
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"recommendation": "Optimal load" if impact["diminishing_returns"] < 0.2 else "Consider 30% load" if impact["diminishing_returns"] > 0.4 else "Good parallelization"
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}
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}
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# Print conclusion
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print("\n" + "=" * 70)
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print("SIMULATION CONCLUSION")
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print("=" * 70)
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print(f"50% TSM Load: {'✅ FEASIBLE' if report['conclusion']['50_percent_load_feasible'] else '❌ HIGH CONTENTION'}")
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print(f"Total Efficiency Gains: {report['conclusion']['efficiency_gains']}")
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print(f"Scaling Efficiency: {report['conclusion']['scaling_efficiency']}")
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print(f"Recommendation: {report['conclusion']['recommendation']}")
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print("=" * 70)
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# Save report
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output_path = Path("/home/allaun/Documents/Research Stack/data/tsm_swarm_50percent_optimization.json")
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, "w") as f:
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json.dump(report, f, indent=2)
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print(f"\nReport saved: {output_path}")
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return report
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def main():
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"""Run 50% TSM swarm optimization."""
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optimizer = TSMSwarmOptimizer()
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report = optimizer.run_full_simulation(agent_count=50)
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return report
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if __name__ == "__main__":
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main()
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