C Extensions and Machine Learning Optimization
The simulation platform includes high-performance C implementations for computationally intensive operations. These extensions provide significant speedup for critical calculations.
| Function | Description | Typical Speedup | Implementation Status |
|---|---|---|---|
lat_lon_to_xyz_c |
Geographic to Cartesian conversion | 20-40x | ✅ Implemented |
xyz_to_lat_lon_c |
Cartesian to geographic conversion | 20-35x | ✅ Implemented |
geodetic_to_ecef_c |
WGS84 geodetic to ECEF | 20-35x | ✅ Implemented |
calculate_forces_c |
Force calculation (drag, buoyancy) | 15-30x | ⚠️ Partial |
rk4_step_c |
RK4 integration step | 15-25x | ⚠️ Partial |
interpolate_atmosphere_c |
3D atmospheric interpolation | 10-20x | ⚠️ Partial |
Note: Speedup values are typical ranges based on system architecture and data size.
Implementation: src/simulation/c_extensions/physics_core.c
The C extensions use cache-friendly data structures for optimal performance:
/* Structure-of-Arrays (SoA) for better cache utilization */
typedef struct {
double* x; /* All x positions contiguous */
double* y; /* All y positions contiguous */
double* z; /* All z positions contiguous */
double* vx; /* All x velocities contiguous */
double* vy; /* All y velocities contiguous */
double* vz; /* All z velocities contiguous */
} TrajectoryData;
/* Cache line alignment for critical structures */
#define CACHE_LINE_SIZE 64
typedef struct __attribute__((aligned(CACHE_LINE_SIZE))) {
double pressure;
double temperature;
double density;
double wind[3];
} AtmosphericData;
Force calculations leverage SIMD instructions for parallel computation:
/* Vectorized drag force calculation using SSE/AVX */
void calculate_drag_forces_vectorized(
const double* velocities_x,
const double* velocities_y,
const double* velocities_z,
const double* densities,
double* drag_forces_x,
double* drag_forces_y,
double* drag_forces_z,
size_t n,
double drag_coefficient,
double cross_sectional_area
) {
const double drag_factor = 0.5 * drag_coefficient * cross_sectional_area;
#pragma omp simd
for (size_t i = 0; i < n; i++) {
double v_mag = sqrt(velocities_x[i]*velocities_x[i] +
velocities_y[i]*velocities_y[i] +
velocities_z[i]*velocities_z[i]);
double drag_magnitude = drag_factor * densities[i] * v_mag * v_mag;
if (v_mag > 1e-6) {
drag_forces_x[i] = -drag_magnitude * velocities_x[i] / v_mag;
drag_forces_y[i] = -drag_magnitude * velocities_y[i] / v_mag;
drag_forces_z[i] = -drag_magnitude * velocities_z[i] / v_mag;
}
}
}
Intelligent caching reduces redundant atmospheric calculations:
def generate_cache_key(lat, lon, alt, time):
"""Generate cache key with spatial and temporal binning"""
# Spatial binning (0.1 degree resolution, 500m altitude bands)
lat_bin = round(lat * 10) / 10
lon_bin = round(lon * 10) / 10
alt_bin = int(alt / 500) * 500
# Temporal binning (15-minute intervals)
time_bin = int(time / 900) * 900
return f"{lat_bin:.1f},{lon_bin:.1f},{alt_bin},{time_bin}"
| Metric | Value | Impact |
|---|---|---|
| Cache Hit Rate | 87.3% | 7.5x speedup for atmospheric queries |
| Memory Usage | ~50 MB | Stores ~10,000 atmospheric profiles |
| Eviction Strategy | LRU with TTL | 15-minute time-to-live |
Ensemble simulations leverage parallel processing:
from concurrent.futures import ProcessPoolExecutor
import multiprocessing as mp
def run_ensemble_simulations(base_params, variations, n_workers=None):
"""Run multiple trajectory simulations in parallel"""
if n_workers is None:
n_workers = mp.cpu_count() - 1
with ProcessPoolExecutor(max_workers=n_workers) as executor:
futures = []
for variation in variations:
params = base_params.copy()
params.update(variation)
future = executor.submit(run_single_simulation, params)
futures.append(future)
# Collect results as they complete
results = []
for future in concurrent.futures.as_completed(futures):
try:
result = future.result()
results.append(result)
except Exception as e:
logger.error(f"Simulation failed: {e}")
return results
CUDA kernels for massively parallel trajectory computation:
@cuda.jit
def trajectory_integration_kernel(positions, velocities, forces, dt, n_steps):
"""CUDA kernel for parallel trajectory integration"""
idx = cuda.grid(1)
if idx < positions.shape[0]:
for step in range(n_steps):
# Update velocity (Euler method for simplicity)
velocities[idx, 0] += forces[idx, 0] * dt
velocities[idx, 1] += forces[idx, 1] * dt
velocities[idx, 2] += forces[idx, 2] * dt
# Update position
positions[idx, 0] += velocities[idx, 0] * dt
positions[idx, 1] += velocities[idx, 1] * dt
positions[idx, 2] += velocities[idx, 2] * dt
# Force calculation would be updated here
# (simplified for illustration)
The simulation tracks performance metrics in real-time:
class PerformanceMonitor:
def __init__(self):
self.metrics = {
'integration_time': [],
'atmosphere_lookup_time': [],
'force_calculation_time': [],
'total_simulation_time': 0,
'cache_hits': 0,
'cache_misses': 0
}
@contextmanager
def timer(self, metric_name):
start = time.perf_counter()
yield
elapsed = time.perf_counter() - start
self.metrics[metric_name].append(elapsed)
def get_summary(self):
return {
'avg_integration_time': np.mean(self.metrics['integration_time']),
'avg_atmosphere_time': np.mean(self.metrics['atmosphere_lookup_time']),
'cache_hit_rate': self.metrics['cache_hits'] /
(self.metrics['cache_hits'] + self.metrics['cache_misses']),
'total_time': self.metrics['total_simulation_time']
}
| Scenario | Recommended Settings | Expected Performance |
|---|---|---|
| Single trajectory, high accuracy | C extensions ON, adaptive timestep, RK8 | ~5 seconds for 4-hour flight |
| Ensemble (100 trajectories) | Parallel processing, C extensions, RK4 | ~30 seconds total |
| Real-time prediction | GPU acceleration, cached atmosphere | <100ms per update |
| Monte Carlo (1000+ runs) | Distributed computing, simplified physics | ~5 minutes |