rfcp/RFCP-Phase-2.3-Performance-Optimization.md

RFCP Phase 2.3: Performance Optimization

Date: January 31, 2025
Type: Performance & Parallelization
Estimated: 8-12 hours
Priority: HIGH — enables practical use of Detailed preset
Depends on: Phase 2.2 (Offline Caching)

---

🎯 Goal

Make Detailed preset usable by parallelizing calculations across CPU cores and optionally GPU. Target: 10-50x speedup.

---

📊 Current Performance

Preset	Points	Current Time	Target Time
Fast	868	0.03s	0.03s ✅
Standard	868	13s	5s
Detailed	868	300s+ (timeout)	30s

Bottleneck Analysis:

[DOMINANT_PATH] Point #1: line_bldgs=646, refl_bldgs=302
- 868 points × 700 buildings × geometry = millions of operations
- Single-threaded Python
- 2 sec/point → 868 × 2 = 1736 sec theoretical

---

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Coverage Calculation                      │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Phase 1: OSM Fetch (async, cached)         → unchanged     │
│  Phase 2: Terrain Pre-load (async)          → unchanged     │
│  Phase 3: Point Calculation                 → PARALLELIZE   │
│                                                              │
│  ┌─────────────────────────────────────────────────────┐    │
│  │              ProcessPoolExecutor                     │    │
│  │  ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐   │    │
│  │  │ Core 1  │ │ Core 2  │ │ Core 3  │ │ Core N  │   │    │
│  │  │ pts 0-61│ │pts 62-123│ │pts 124..│ │ pts ... │   │    │
│  │  └─────────┘ └─────────┘ └─────────┘ └─────────┘   │    │
│  └─────────────────────────────────────────────────────┘    │
│                           │                                  │
│                           ▼                                  │
│  ┌─────────────────────────────────────────────────────┐    │
│  │              Optional: GPU Acceleration              │    │
│  │  - Path loss matrix calculation (NumPy → CuPy)      │    │
│  │  - Batch terrain lookups                             │    │
│  │  - Vectorized distance calculations                  │    │
│  └─────────────────────────────────────────────────────┘    │
│                                                              │
└─────────────────────────────────────────────────────────────┘

---

✅ Tasks

Task 2.3.1: Multiprocessing Infrastructure (3-4 hours)

Problem: Python GIL prevents true parallelism with threads. Need processes.

Create backend/app/services/parallel_coverage_service.py:

import os
import multiprocessing as mp
from concurrent.futures import ProcessPoolExecutor, as_completed
from typing import List, Dict, Any, Tuple
import time

# Shared data for worker processes (loaded once per process)
_worker_data = {}

def _init_worker(terrain_cache: Dict, buildings: List, spatial_index_data: Dict, settings_dict: Dict):
    """Initialize worker process with shared data."""
    global _worker_data
    _worker_data = {
        'terrain_cache': terrain_cache,
        'buildings': buildings,
        'spatial_index': rebuild_spatial_index(spatial_index_data),
        'settings': settings_dict,
    }
    # Import heavy modules inside worker to avoid pickle issues
    from app.services.terrain_service import TerrainService
    from app.services.los_service import LOSService
    from app.services.dominant_path_service import DominantPathService
    
    _worker_data['terrain_service'] = TerrainService()
    _worker_data['terrain_service']._tile_cache = terrain_cache
    _worker_data['los_service'] = LOSService(_worker_data['terrain_service'])
    _worker_data['dominant_path_service'] = DominantPathService(
        _worker_data['terrain_service'],
        _worker_data['los_service']
    )

def _calculate_point_worker(args: Tuple) -> Dict:
    """Worker function for single point calculation."""
    global _worker_data
    lat, lon, site_lat, site_lon, site_elevation, point_elevation = args
    
    # Use pre-initialized services
    terrain = _worker_data['terrain_service']
    los = _worker_data['los_service']
    dominant = _worker_data['dominant_path_service']
    settings = _worker_data['settings']
    buildings = _worker_data['buildings']
    spatial_idx = _worker_data['spatial_index']
    
    # ... calculation logic (copy from _calculate_point_sync)
    
    return {
        'lat': lat,
        'lon': lon,
        'rsrp': rsrp,
        'distance': distance,
        # ... other fields
    }

class ParallelCoverageService:
    """Coverage calculation with multiprocessing."""
    
    def __init__(self):
        # Detect available cores
        self.num_workers = min(mp.cpu_count(), 14)  # Cap at 14
        print(f"[Coverage] Parallel mode: {self.num_workers} workers")
    
    async def calculate_parallel(
        self,
        sites: List,
        settings: CoverageSettings,
        terrain_cache: Dict,
        buildings: List,
        spatial_index_data: Dict,
    ) -> List[Dict]:
        """Calculate coverage using multiple processes."""
        
        # Prepare grid
        grid = self._generate_grid(sites, settings)
        total_points = len(grid)
        
        print(f"[Coverage] Starting parallel calculation: {total_points} points, {self.num_workers} workers")
        
        # Pre-compute point elevations
        point_elevations = {(lat, lon): elev for lat, lon, elev in grid_with_elevations}
        
        # Prepare arguments for workers
        work_items = [
            (lat, lon, site.lat, site.lon, site_elevation, point_elevations.get((lat, lon), 0))
            for lat, lon in grid
        ]
        
        # Run in process pool
        results = []
        start_time = time.time()
        
        with ProcessPoolExecutor(
            max_workers=self.num_workers,
            initializer=_init_worker,
            initargs=(terrain_cache, buildings, spatial_index_data, settings.dict())
        ) as executor:
            # Submit all tasks
            futures = {executor.submit(_calculate_point_worker, item): i 
                      for i, item in enumerate(work_items)}
            
            # Collect results with progress
            completed = 0
            for future in as_completed(futures):
                result = future.result()
                results.append(result)
                completed += 1
                
                if completed % (total_points // 10) == 0:
                    elapsed = time.time() - start_time
                    rate = completed / elapsed
                    eta = (total_points - completed) / rate
                    print(f"[Coverage] Progress: {completed}/{total_points} ({100*completed//total_points}%) - ETA: {eta:.1f}s")
        
        elapsed = time.time() - start_time
        print(f"[Coverage] Parallel calculation done: {elapsed:.1f}s ({elapsed/total_points*1000:.1f}ms/point)")
        
        return results

---

Task 2.3.2: Data Serialization for Workers (2-3 hours)

Problem: Each worker process needs access to terrain cache, buildings, spatial index. Can't share directly.

Solutions:

1. Shared Memory (Python 3.8+):

from multiprocessing import shared_memory
import numpy as np

# Create shared terrain cache
terrain_shm = shared_memory.SharedMemory(create=True, size=terrain_array.nbytes)
terrain_shared = np.ndarray(terrain_array.shape, dtype=terrain_array.dtype, buffer=terrain_shm.buf)
terrain_shared[:] = terrain_array[:]

2. Memory-mapped files:

import mmap
import numpy as np

# Save terrain to mmap file
terrain_mmap = np.memmap('terrain_cache.dat', dtype='int16', mode='w+', shape=(3601, 3601))
terrain_mmap[:] = terrain_data[:]
terrain_mmap.flush()

# Workers read from same file
worker_terrain = np.memmap('terrain_cache.dat', dtype='int16', mode='r', shape=(3601, 3601))

3. Pickle once, load in each worker:

# Main process saves data
import pickle
with open('worker_data.pkl', 'wb') as f:
    pickle.dump({'terrain': terrain_cache, 'buildings': buildings}, f)

# Worker loads once at init
def _init_worker(data_path):
    global _worker_data
    with open(data_path, 'rb') as f:
        _worker_data = pickle.load(f)

Recommendation: Start with pickle (simplest), optimize with mmap if needed.

---

Task 2.3.3: Integrate Parallel Service (2 hours)

Update coverage_service.py:

class CoverageService:
    def __init__(self):
        self.parallel_service = ParallelCoverageService()
        self.use_parallel = True  # Can be toggled
        self.parallel_threshold = 100  # Use parallel for > 100 points
    
    async def calculate(self, sites, settings):
        grid = self._generate_grid(sites, settings)
        
        # Decide execution mode
        if self.use_parallel and len(grid) > self.parallel_threshold:
            return await self._calculate_parallel(sites, settings, grid)
        else:
            return await self._calculate_sequential(sites, settings, grid)
    
    async def _calculate_parallel(self, sites, settings, grid):
        # Phase 1: OSM fetch (same as before)
        buildings, streets, water, vegetation = await self._fetch_osm_grid_aligned(...)
        
        # Phase 2: Terrain pre-load (same as before)
        await self.terrain.ensure_tiles_for_bbox(...)
        terrain_cache = self.terrain._tile_cache.copy()
        
        # Phase 3: Parallel point calculation
        spatial_index_data = self._serialize_spatial_index(spatial_idx)
        
        results = await self.parallel_service.calculate_parallel(
            sites=sites,
            settings=settings,
            terrain_cache=terrain_cache,
            buildings=buildings,
            spatial_index_data=spatial_index_data,
        )
        
        return results

---

Task 2.3.4: GPU Acceleration (Optional) (3-4 hours)

Only if NVIDIA GPU detected. Use CuPy for NumPy-like GPU operations.

Create backend/app/services/gpu_service.py:

import os

# Check for GPU
GPU_AVAILABLE = False
try:
    import cupy as cp
    GPU_AVAILABLE = cp.cuda.runtime.getDeviceCount() > 0
    if GPU_AVAILABLE:
        print(f"[GPU] CUDA available: {cp.cuda.runtime.getDeviceProperties(0)['name'].decode()}")
except ImportError:
    pass

class GPUService:
    """GPU-accelerated calculations using CuPy."""
    
    def __init__(self):
        self.enabled = GPU_AVAILABLE
    
    def calculate_path_loss_batch(
        self,
        distances: np.ndarray,  # (N,) array of distances in meters
        frequency_mhz: float,
        tx_height: float,
        rx_height: float,
    ) -> np.ndarray:
        """Calculate Okumura-Hata path loss for all points at once."""
        
        if self.enabled:
            import cupy as cp
            d = cp.asarray(distances)
        else:
            d = distances
        
        # Okumura-Hata formula (vectorized)
        d_km = d / 1000.0
        f = frequency_mhz
        hb = tx_height
        hm = rx_height
        
        # Urban area correction
        a_hm = (1.1 * np.log10(f) - 0.7) * hm - (1.56 * np.log10(f) - 0.8)
        
        # Path loss
        L = (46.3 + 33.9 * np.log10(f) - 13.82 * np.log10(hb) - a_hm +
             (44.9 - 6.55 * np.log10(hb)) * np.log10(d_km))
        
        if self.enabled:
            return cp.asnumpy(L)
        return L
    
    def calculate_distances_batch(
        self,
        site_lat: float,
        site_lon: float,
        point_lats: np.ndarray,
        point_lons: np.ndarray,
    ) -> np.ndarray:
        """Calculate distances from site to all points (Haversine)."""
        
        if self.enabled:
            import cupy as cp
            lat1 = cp.radians(site_lat)
            lon1 = cp.radians(site_lon)
            lat2 = cp.radians(cp.asarray(point_lats))
            lon2 = cp.radians(cp.asarray(point_lons))
        else:
            lat1 = np.radians(site_lat)
            lon1 = np.radians(site_lon)
            lat2 = np.radians(point_lats)
            lon2 = np.radians(point_lons)
        
        dlat = lat2 - lat1
        dlon = lon2 - lon1
        
        a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2
        c = 2 * np.arcsin(np.sqrt(a))
        
        R = 6371000  # Earth radius in meters
        distances = R * c
        
        if self.enabled:
            return cp.asnumpy(distances)
        return distances


gpu_service = GPUService()

Add to requirements.txt (optional):

cupy-cuda12x>=12.0.0  # For CUDA 12.x
# or cupy-cuda11x>=11.0.0  # For CUDA 11.x

---

Task 2.3.5: Settings UI for Parallel/GPU (1 hour)

Add to frontend Settings panel:

// Performance settings
<div className="settings-section">
  <h4>Performance</h4>
  
  <label>
    <input 
      type="checkbox" 
      checked={settings.useParallel}
      onChange={(e) => updateSettings({ useParallel: e.target.checked })}
    />
    Use parallel processing ({cpuCores} cores)
  </label>
  
  {gpuAvailable && (
    <label>
      <input 
        type="checkbox" 
        checked={settings.useGPU}
        onChange={(e) => updateSettings({ useGPU: e.target.checked })}
      />
      Use GPU acceleration ({gpuName})
    </label>
  )}
  
  <div className="worker-count">
    <label>Worker processes:</label>
    <input 
      type="number" 
      min={1} 
      max={cpuCores}
      value={settings.workerCount}
      onChange={(e) => updateSettings({ workerCount: e.target.value })}
    />
  </div>
</div>

Add API endpoint for system info:

@router.get("/api/system/info")
async def get_system_info():
    import multiprocessing as mp
    
    gpu_info = None
    try:
        import cupy as cp
        if cp.cuda.runtime.getDeviceCount() > 0:
            props = cp.cuda.runtime.getDeviceProperties(0)
            gpu_info = {
                'name': props['name'].decode(),
                'memory_mb': props['totalGlobalMem'] // (1024 * 1024),
            }
    except:
        pass
    
    return {
        'cpu_cores': mp.cpu_count(),
        'gpu': gpu_info,
        'parallel_enabled': True,
        'gpu_enabled': gpu_info is not None,
    }

---

🧪 Testing

# Run performance test
cd installer
.\test-coverage.bat

# Expected results after optimization:
# Fast: 0.03s (unchanged)
# Standard: ~5s (was 13s)
# Detailed: ~30s (was 300s+ timeout)

Benchmark script:

# test_parallel.py
import asyncio
import time
from app.services.coverage_service import coverage_service

async def benchmark():
    settings = CoverageSettings(
        radius=5000,
        resolution=300,
        preset='detailed',
    )
    
    site = Site(lat=50.45, lon=30.52, ...)
    
    # Warm up
    await coverage_service.calculate([site], settings)
    
    # Benchmark
    times = []
    for i in range(3):
        start = time.time()
        result = await coverage_service.calculate([site], settings)
        elapsed = time.time() - start
        times.append(elapsed)
        print(f"Run {i+1}: {elapsed:.1f}s, {len(result)} points")
    
    print(f"Average: {sum(times)/len(times):.1f}s")

asyncio.run(benchmark())

---

✅ Success Criteria

• Multiprocessing uses all available CPU cores
• Detailed preset completes in <60s for 5km radius
• No memory leaks with large calculations
• GPU acceleration works if NVIDIA card present
• Settings UI shows core count and GPU status
• Progress indicator updates during calculation

---

📊 Expected Performance

Preset	Before	After (14 cores)	After (14 cores + GPU)
Fast	0.03s	0.03s	0.03s
Standard	13s	~2s	~1s
Detailed	300s+	~25s	~10s

---

🔜 Next: Phase 2.4

• R-tree spatial index (replace grid-based)
• Simplified building geometry for distant points
• Level-of-detail (LOD) system
• Streaming results (show partial coverage while calculating)

---

Ready for Claude Code 🚀