Performance Optimization

This guide covers advanced techniques for optimizing the performance of your DegentCivil simulation.

Performance Overview

DegentCivil's performance is influenced by several key factors:

Number of active characters
Complexity of character states
Memory system usage
Database operations
LLM API calls
Event processing
Physical simulation calculations

Optimization Strategies

1. Character Management

Batch Processing

def process_characters_in_batches(characters, batch_size=100):
    for i in range(0, len(characters), batch_size):
        batch = characters[i:i+batch_size]
        process_character_batch(batch)

State Optimization

class OptimizedState(BaseState):
    def __init__(self):
        super().__init__()
        self.cached_data = {}

    def execute(self, character):
        if self.should_update_cache():
            self.cached_data = self.heavy_computation()
        return self.cached_data

2. Memory System

Memory Pruning

def optimize_memory(character):
    # Remove old, less relevant memories
    character.memory.prune_old_memories(threshold_days=30)

    # Compress similar memories
    character.memory.compress_similar_memories()

    # Archive rarely accessed memories
    character.memory.archive_inactive_memories()

Efficient Querying

def query_memory(character, query, limit=10):
    # Use indexed fields for faster queries
    return character.memory.query(
        query,
        limit=limit,
        use_cache=True,
        index_fields=['timestamp', 'importance']
    )

3. Database Optimization

Connection Pooling

from app.database import Database

class OptimizedDatabase(Database):
    def __init__(self):
        self.connection_pool = create_connection_pool(
            min_connections=5,
            max_connections=20,
            idle_timeout=300
        )

Query Optimization

class QueryOptimizer:
    def __init__(self):
        self.query_cache = {}

    def optimize_query(self, query):
        # Use prepared statements
        if query in self.query_cache:
            return self.query_cache[query]

        # Analyze and optimize new queries
        optimized = self.analyze_and_optimize(query)
        self.query_cache[query] = optimized
        return optimized

4. LLM Integration

Token Management

class TokenManager:
    def __init__(self, max_tokens=4096):
        self.max_tokens = max_tokens

    def optimize_prompt(self, prompt):
        # Truncate and optimize prompt to fit token limit
        return self.truncate_to_token_limit(prompt)

    def batch_requests(self, prompts):
        # Combine similar prompts to reduce API calls
        return self.combine_similar_prompts(prompts)

Response Caching

class LLMCache:
    def __init__(self):
        self.cache = {}

    def get_response(self, prompt):
        cache_key = self.generate_cache_key(prompt)
        if cache_key in self.cache:
            return self.cache[cache_key]

        response = self.call_llm_api(prompt)
        self.cache[cache_key] = response
        return response

Monitoring and Profiling

1. Performance Metrics

class PerformanceMonitor:
    def __init__(self):
        self.metrics = {
            'character_updates': [],
            'memory_operations': [],
            'database_queries': [],
            'llm_calls': []
        }

    def record_metric(self, category, duration):
        self.metrics[category].append({
            'timestamp': time.time(),
            'duration': duration
        })

    def analyze_performance(self):
        return {
            category: self.calculate_statistics(data)
            for category, data in self.metrics.items()
        }

2. Profiling Tools

import cProfile
import pstats

def profile_simulation(simulation, duration):
    profiler = cProfile.Profile()
    profiler.enable()

    simulation.run(duration)

    profiler.disable()
    stats = pstats.Stats(profiler).sort_stats('cumulative')
    return stats

Memory Management

1. Memory Pooling

class MemoryPool:
    def __init__(self, initial_size=1000):
        self.pool = [None] * initial_size
        self.available = set(range(initial_size))

    def allocate(self):
        if not self.available:
            self.expand_pool()
        return self.pool[self.available.pop()]

    def release(self, index):
        self.available.add(index)

2. Garbage Collection

import gc

def optimize_memory_usage():
    # Run garbage collection
    gc.collect()

    # Disable automatic garbage collection
    gc.disable()

    # Manual collection at specific intervals
    def scheduled_gc():
        gc.collect()
        schedule_next_gc()

Configuration Optimization

1. Tuning Parameters

class SimulationConfig:
    def __init__(self):
        self.update_interval = 0.1  # seconds
        self.max_characters = 1000
        self.memory_limit = 1000000  # bytes
        self.cache_size = 10000
        self.batch_size = 100

2. Dynamic Adjustment

class DynamicOptimizer:
    def __init__(self, simulation):
        self.simulation = simulation

    def adjust_parameters(self):
        # Monitor system resources
        cpu_usage = self.get_cpu_usage()
        memory_usage = self.get_memory_usage()

        # Adjust parameters based on usage
        if cpu_usage > 80:
            self.reduce_update_frequency()
        if memory_usage > 80:
            self.clear_caches()

Best Practices

Profile First
Identify bottlenecks
Measure impact of changes
Set performance baselines
Optimize Incrementally
Make one change at a time
Measure impact of each change
Document improvements
Balance Resources
CPU vs Memory usage
Network vs Local processing
Storage vs Computation
Cache Strategically
Identify frequently accessed data
Set appropriate cache sizes
Implement cache invalidation
Monitor Continuously
Track performance metrics
Set up alerts
Regular optimization reviews