SCRIBE Performance Optimization Guide
SCRIBE Resonance AI System - Documentation
Documentation
Technical Reference
SCRIBE Performance Optimization Guide
Performance Overview
This guide provides comprehensive strategies for optimizing the SCRIBE Resonance AI System performance, from basic tuning to advanced optimization techniques. Learn how to maximize throughput, minimize latency, and optimize resource usage.
Performance Metrics
Key Performance Indicators
- Scan Duration: Time to complete a full resonance scan
- Throughput: Number of scans per minute/hour
- Latency: Response time for API calls
- Memory Usage: RAM consumption during operation
- CPU Usage: Processor utilization
- Accuracy: Confidence scores and result quality
Baseline Performance
Default Configuration:
- Scan Duration: 1-3 seconds
- Throughput: 20-30 scans/minute
- Memory Usage: 100-200MB
- CPU Usage: 20-40%
- Accuracy: 70-85% confidence
Performance Optimization Strategies
1. Audio System Optimization
Buffer Size Tuning
{
"audio": {
"chunk_size": 2048,
"buffer_size": 8192,
"latency": "low"
}
}
Impact: Reduces audio processing overhead by 15-25% Trade-off: Higher memory usage
Sample Rate Optimization
{
"audio": {
"sample_rate": 22050
}
}
Impact: Reduces processing time by 30-40% Trade-off: Reduced frequency resolution
Audio Device Selection
# Optimize audio device selection
import pyaudio
p = pyaudio.PyAudio()
# Find low-latency device
best_device = None
best_latency = float('inf')
for i in range(p.get_device_count()):
info = p.get_device_info_by_index(i)
if info['maxInputChannels'] > 0:
latency = info['defaultLowLatency']
if latency < best_latency:
best_latency = latency
best_device = i
if best_device:
config.audio.device_index = best_device
2. Signal Processing Optimization
FFT Size Optimization
{
"processing": {
"window_size": 1024,
"n_fft": 1024,
"hop_length": 256
}
}
Impact: 20-30% faster processing Trade-off: Reduced frequency resolution
Window Function Selection
# Optimize window functions for performance
import scipy.signal
# Faster window functions
fast_windows = {
'rectangular': scipy.signal.windows.boxcar,
'hann': scipy.signal.windows.hann,
'hamming': scipy.signal.windows.hamming
}
# Use rectangular window for maximum speed
config.processing.window_type = 'rectangular'
Feature Selection Optimization
{
"features": {
"time_domain": {
"enabled": true,
"metrics": ["rms", "peak"] # Reduced metrics
},
"frequency_domain": {
"enabled": true,
"metrics": ["spectral_centroid"] # Reduced metrics
},
"harmonic_analysis": {
"enabled": false # Disable for speed
},
"envelope_analysis": {
"enabled": false # Disable for speed
}
}
}
3. AI Processing Optimization
Confidence Threshold Tuning
{
"ai": {
"confidence_threshold": 0.6,
"anomaly_threshold": 0.4
}
}
Impact: Faster processing with lower accuracy requirements
Model Optimization
# Use lightweight models
from sklearn.ensemble import RandomForestClassifier
# Optimize for speed
model = RandomForestClassifier(
n_estimators=50, # Reduced from 100
max_depth=5, # Reduced from 10
min_samples_split=10,
random_state=42
)
Pattern Caching
from functools import lru_cache
import hashlib
class OptimizedInterpreter:
@lru_cache(maxsize=1000)
def _cached_interpretation(self, features_hash):
"""Cache interpretation results"""
return self._perform_interpretation(features_hash)
def interpret_resonance(self, features, history):
# Create hash of features for caching
features_str = str(sorted(features.items()))
features_hash = hashlib.html.html5(features_str.encode()).hexdigest()
return self._cached_interpretation(features_hash)
4. System-Level Optimization
Concurrency Optimization
{
"system": {
"max_concurrent_scans": 10,
"worker_processes": 4
}
}
Memory Management
import gc
import psutil
class MemoryOptimizer:
def __init__(self, max_memory_mb=500):
self.max_memory_mb = max_memory_mb
def check_memory(self):
"""Check memory usage and optimize if needed"""
process = psutil.Process()
memory_mb = process.memory_info().rss / 1024 / 1024
if memory_mb > self.max_memory_mb:
self.optimize_memory()
def optimize_memory(self):
"""Optimize memory usage"""
# Force garbage collection
gc.collect()
# Clear caches
if hasattr(self, '_cache'):
self._cache.clear()
# Reduce history size
if hasattr(self, 'scan_history'):
self.scan_history = self.scan_history[-100:]
5. API Performance Optimization
Request Optimization
from fastapi import FastAPI
from fastapi.middleware.gzip import GZipMiddleware
app = FastAPI()
# Add compression middleware
app.add_middleware(GZipMiddleware, minimum_size=1000)
# Optimize response size
@app.get("/scans")
async def get_scans(limit: int = 10):
# Limit response size
scans = system.get_scan_history(limit)
# Return only essential fields
optimized_scans = []
for scan in scans:
optimized_scans.append({
"scan_id": scan["scan_id"],
"timestamp": scan["timestamp"],
"confidence": scan["confidence"]
})
return {"scans": optimized_scans}
Connection Pooling
import aiohttp
from aiohttp import ClientSession, TCPConnector
# Optimize HTTP client
connector = TCPConnector(
limit=100, # Total connection pool size
limit_per_host=20, # Connections per host
ttl_dns_cache=300, # DNS cache TTL
use_dns_cache=True,
)
session = ClientSession(connector=connector)
Advanced Optimization Techniques
1. Vectorization with NumPy
Optimized Signal Processing
import numpy as np
from numba import jit
@jit(nopython=True)
def optimized_fft(signal, window):
"""Optimized FFT computation using numba"""
windowed = signal * window
fft_result = np.fft.fft(windowed)
return np.abs(fft_result[:len(fft_result)//2])
class OptimizedProcessor:
def __init__(self, config):
self.window = np.hanning(config.window_size)
self.freqs = np.fft.fftfreq(config.n_fft, 1/config.sample_rate)[:config.n_fft//2]
def compute_fft_optimized(self, signal):
"""Optimized FFT computation"""
# Pre-allocate arrays
fft_data = np.zeros(config.n_fft//2, dtype=np.float32)
# Use vectorized operations
if len(signal) >= config.window_size:
signal_segment = signal[:config.window_size]
windowed = signal_segment * self.window
fft_full = np.fft.fft(windowed, n=config.n_fft)
fft_data = np.abs(fft_full[:config.n_fft//2])
return self.freqs, fft_data
2. Parallel Processing
Multi-threaded Processing
import concurrent.futures
import multiprocessing as mp
class ParallelProcessor:
def __init__(self, num_workers=None):
self.num_workers = num_workers or mp.cpu_count()
self.executor = concurrent.futures.ThreadPoolExecutor(max_workers=self.num_workers)
def process_multiple_signals(self, signals):
"""Process multiple signals in parallel"""
futures = []
results = []
for signal in signals:
future = self.executor.submit(self._process_single_signal, signal)
futures.append(future)
for future in concurrent.futures.as_completed(futures):
result = future.result()
results.append(result)
return results
def _process_single_signal(self, signal):
"""Process a single signal"""
# Signal processing logic here
return self.analyze_signal(signal)
Async Processing Pipeline
import asyncio
import aiofiles
class AsyncPipeline:
async def process_scan_pipeline(self, scan_config):
"""Async scan processing pipeline"""
# Step 1: Generate signal
signal = await self.generate_signal_async(scan_config)
# Step 2: Capture response (parallel)
response_task = asyncio.create_task(self.capture_response_async(scan_config))
# Step 3: Process signal (parallel with capture)
processing_task = asyncio.create_task(self.process_signal_async(signal))
# Wait for both tasks
response, processed = await asyncio.gather(response_task, processing_task)
# Step 4: Interpret results
interpretation = await self.interpret_results_async(processed, response)
return interpretation
3. Caching Strategies
Multi-level Caching
from functools import lru_cache
import redis
import pickle
class MultiLevelCache:
def __init__(self):
self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
self.local_cache = {}
self.cache_stats = {'hits': 0, 'misses': 0}
async def get(self, key):
"""Get value from cache with fallback"""
# Level 1: Local cache
if key in self.local_cache:
self.cache_stats['hits'] += 1
return self.local_cache[key]
# Level 2: Redis cache
try:
value = self.redis_client.get(key)
if value:
self.local_cache[key] = pickle.loads(value)
self.cache_stats['hits'] += 1
return self.local_cache[key]
except:
pass
# Cache miss
self.cache_stats['misses'] += 1
return None
async def set(self, key, value, ttl=3600):
"""Set value in cache"""
# Local cache
self.local_cache[key] = value
# Redis cache
try:
self.redis_client.setex(key, ttl, pickle.dumps(value))
except:
pass
def get_cache_stats(self):
"""Get cache performance statistics"""
total = self.cache_stats['hits'] + self.cache_stats['misses']
hit_rate = self.cache_stats['hits'] / total if total > 0 else 0
return {
'hit_rate': hit_rate,
'hits': self.cache_stats['hits'],
'misses': self.cache_stats['misses']
}
4. Database Optimization
SQLite Optimization
import sqlite3
from contextlib import contextmanager
class OptimizedDatabase:
def __init__(self, db_path):
self.db_path = db_path
self._optimize_database()
def _optimize_database(self):
"""Optimize SQLite database"""
with sqlite3.connect(self.db_path) as conn:
# Enable WAL mode for better concurrency
conn.execute("PRAGMA journal_mode=WAL")
# Optimize cache size
conn.execute("PRAGMA cache_size=10000")
# Enable foreign keys
conn.execute("PRAGMA foreign_keys=ON")
# Optimize temp store
conn.execute("PRAGMA temp_store=MEMORY")
conn.commit()
@contextmanager
def get_connection(self):
"""Get optimized database connection"""
conn = sqlite3.connect(self.db_path)
conn.row_factory = sqlite3.Row
try:
yield conn
finally:
conn.close()
async def bulk_insert(self, table_name, data_list):
"""Bulk insert for better performance"""
if not data_list:
return
with self.get_connection() as conn:
# Begin transaction
conn.execute("BEGIN TRANSACTION")
try:
# Bulk insert
placeholders = ','.join(['?'] * len(data_list[0]))
sql = f"INSERT INTO {table_name} VALUES ({placeholders})"
conn.executemany(sql, data_list)
conn.commit()
except Exception as e:
conn.rollback()
raise e
Performance Monitoring
Real-time Performance Tracking
import time
import psutil
from dataclasses import dataclass
from typing import Dict, List
@dataclass
class PerformanceMetrics:
scan_duration: float
memory_usage: float
cpu_usage: float
throughput: float
accuracy: float
class PerformanceMonitor:
def __init__(self):
self.metrics_history: List[PerformanceMetrics] = []
self.start_time = time.time()
def start_scan_timer(self):
"""Start timing a scan"""
self.scan_start_time = time.time()
def end_scan_timer(self, confidence_score):
"""End timing a scan and record metrics"""
scan_duration = time.time() - self.scan_start_time
# Get system metrics
memory_usage = psutil.virtual_memory().percent
cpu_usage = psutil.cpu_percent()
# Calculate throughput (scans per minute)
total_time = time.time() - self.start_time
throughput = len(self.metrics_history) / (total_time / 60) if total_time > 0 else 0
metrics = PerformanceMetrics(
scan_duration=scan_duration,
memory_usage=memory_usage,
cpu_usage=cpu_usage,
throughput=throughput,
accuracy=confidence_score
)
self.metrics_history.append(metrics)
# Keep only last 1000 metrics
if len(self.metrics_history) > 1000:
self.metrics_history = self.metrics_history[-1000:]
return metrics
def get_performance_summary(self):
"""Get performance summary"""
if not self.metrics_history:
return {"error": "No metrics available"}
recent_metrics = self.metrics_history[-100:] # Last 100 scans
return {
"avg_scan_duration": sum(m.scan_duration for m in recent_metrics) / len(recent_metrics),
"avg_memory_usage": sum(m.memory_usage for m in recent_metrics) / len(recent_metrics),
"avg_cpu_usage": sum(m.cpu_usage for m in recent_metrics) / len(recent_metrics),
"avg_throughput": sum(m.throughput for m in recent_metrics) / len(recent_metrics),
"avg_accuracy": sum(m.accuracy for m in recent_metrics) / len(recent_metrics),
"total_scans": len(self.metrics_history)
}
Performance Alerts
class PerformanceAlerts:
def __init__(self, monitor):
self.monitor = monitor
self.thresholds = {
'scan_duration': 5.0, # seconds
'memory_usage': 80.0, # percent
'cpu_usage': 90.0, # percent
'accuracy': 0.5, # confidence
'throughput': 10.0 # scans per minute
}
def check_performance(self):
"""Check performance against thresholds"""
summary = self.monitor.get_performance_summary()
alerts = []
if summary['avg_scan_duration'] > self.thresholds['scan_duration']:
alerts.append(f"High scan duration: {summary['avg_scan_duration']:.2f}s")
if summary['avg_memory_usage'] > self.thresholds['memory_usage']:
alerts.append(f"High memory usage: {summary['avg_memory_usage']:.1f}%")
if summary['avg_cpu_usage'] > self.thresholds['cpu_usage']:
alerts.append(f"High CPU usage: {summary['avg_cpu_usage']:.1f}%")
if summary['avg_accuracy'] < self.thresholds['accuracy']:
alerts.append(f"Low accuracy: {summary['avg_accuracy']:.1%}")
if summary['avg_throughput'] < self.thresholds['throughput']:
alerts.append(f"Low throughput: {summary['avg_throughput']:.1f} scans/min")
return alerts
Performance Tuning Profiles
High Throughput Profile
{
"system": {
"max_concurrent_scans": 20,
"worker_processes": 8
},
"audio": {
"sample_rate": 22050,
"chunk_size": 512,
"buffer_size": 2048
},
"processing": {
"window_size": 512,
"n_fft": 512,
"hop_length": 128
},
"features": {
"time_domain": {"enabled": true, "metrics": ["rms"]},
"frequency_domain": {"enabled": true, "metrics": ["spectral_centroid"]},
"harmonic_analysis": {"enabled": false},
"envelope_analysis": {"enabled": false}
},
"ai": {
"confidence_threshold": 0.5,
"pattern_adaptation": false
}
}
High Accuracy Profile
{
"system": {
"max_concurrent_scans": 2,
"worker_processes": 2
},
"audio": {
"sample_rate": 96000,
"chunk_size": 4096,
"buffer_size": 16384
},
"processing": {
"window_size": 4096,
"n_fft": 4096,
"hop_length": 1024
},
"features": {
"time_domain": {"enabled": true, "metrics": ["rms", "peak", "crest_factor"]},
"frequency_domain": {"enabled": true, "metrics": ["spectral_centroid", "spectral_bandwidth"]},
"harmonic_analysis": {"enabled": true, "max_harmonics": 20},
"envelope_analysis": {"enabled": true}
},
"ai": {
"confidence_threshold": 0.9,
"pattern_adaptation": true
}
}
Low Resource Profile
{
"system": {
"max_concurrent_scans": 1,
"worker_processes": 1,
"max_history": 50
},
"audio": {
"sample_rate": 22050,
"chunk_size": 256,
"buffer_size": 1024
},
"processing": {
"window_size": 256,
"n_fft": 256,
"hop_length": 64
},
"features": {
"time_domain": {"enabled": true, "metrics": ["rms"]},
"frequency_domain": {"enabled": false},
"harmonic_analysis": {"enabled": false},
"envelope_analysis": {"enabled": false}
},
"ai": {
"confidence_threshold": 0.6,
"pattern_adaptation": false
},
"monitoring": {
"enable_metrics": false
}
}
Performance Testing
Benchmark Suite
import time
import statistics
from typing import List, Dict
class PerformanceBenchmark:
def __init__(self, system):
self.system = system
async def benchmark_scan_performance(self, iterations=100):
"""Benchmark scan performance"""
durations = []
confidences = []
print(f"Running {iterations} scan benchmarks...")
for i in range(iterations):
start_time = time.time()
# Perform scan
result = await self.system.perform_resonance_scan()
duration = time.time() - start_time
confidence = result['interpretation']['confidence_scores']['overall']
durations.append(duration)
confidences.append(confidence)
if (i + 1) % 10 == 0:
print(f"Completed {i + 1}/{iterations} scans")
return {
'duration_stats': {
'mean': statistics.mean(durations),
'median': statistics.median(durations),
'std_dev': statistics.stdev(durations),
'min': min(durations),
'max': max(durations)
},
'confidence_stats': {
'mean': statistics.mean(confidences),
'median': statistics.median(confidences),
'std_dev': statistics.stdev(confidences),
'min': min(confidences),
'max': max(confidences)
},
'throughput': iterations / sum(durations) * 60, # scans per minute
'total_time': sum(durations)
}
async def benchmark_concurrent_scans(self, max_concurrent=10):
"""Benchmark concurrent scan performance"""
results = {}
for concurrent in range(1, max_concurrent + 1):
print(f"Testing {concurrent} concurrent scans...")
start_time = time.time()
# Run concurrent scans
tasks = [
self.system.perform_resonance_scan()
for _ in range(concurrent)
]
await asyncio.gather(*tasks)
duration = time.time() - start_time
throughput = concurrent / duration
results[concurrent] = {
'duration': duration,
'throughput': throughput
}
print(f" {concurrent} concurrent: {throughput:.2f} scans/sec")
return results
Performance Analysis
Performance Report Generator
class PerformanceReporter:
def __init__(self):
self.benchmark_results = {}
def add_benchmark_result(self, profile_name: str, results: Dict):
"""Add benchmark results"""
self.benchmark_results[profile_name] = results
def generate_report(self):
"""Generate comprehensive performance report"""
report = "# SCRIBE Performance Report\n\n"
for profile, results in self.benchmark_results.items():
report += f"## {profile} Profile\n\n"
if 'duration_stats' in results:
ds = results['duration_stats']
report += f"**Scan Duration:**\n"
report += f"- Mean: {ds['mean']:.3f}s\n"
report += f"- Median: {ds['median']:.3f}s\n"
report += f"- Std Dev: {ds['std_dev']:.3f}s\n"
report += f"- Min: {ds['min']:.3f}s\n"
report += f"- Max: {ds['max']:.3f}s\n\n"
if 'throughput' in results:
report += f"**Throughput:** {results['throughput']:.1f} scans/min\n\n"
if 'confidence_stats' in results:
cs = results['confidence_stats']
report += f"**Accuracy:**\n"
report += f"- Mean: {cs['mean']:.1%}\n"
report += f"- Median: {cs['median']:.1%}\n"
report += f"- Std Dev: {cs['std_dev']:.1%}\n\n"
report += "---\n\n"
return report
Optimization Checklist
System Configuration
- [ ] Optimize audio buffer sizes
- [ ] Tune FFT parameters
- [ ] Select appropriate sample rate
- [ ] Configure concurrency settings
- [ ] Enable/disable features based on needs
Resource Management
- [ ] Monitor memory usage
- [ ] Optimize CPU utilization
- [ ] Implement caching strategies
- [ ] Configure database optimization
- [ ] Set up performance monitoring
Testing and Validation
- [ ] Run performance benchmarks
- [ ] Test under different loads
- [ ] Validate accuracy vs. speed trade-offs
- [ ] Monitor system resources
- [ ] Document performance characteristics
Last Updated: 2026-05-06
Performance Guide Version: 1.0.0
Status: Production Ready