Best Practices for Geospatial Benchmarking under CPU/GPU Stress¶

# Geospatial benchmarks must stress-test:
# 1. CPU utilization (thread scaling, SIMD)
# 2. Memory pressure (large datasets, concurrent operations)
# 3. I/O bottlenecks (disk, network, GPU memory)
# 4. GPU acceleration (kernel execution, memory transfer)
# 5. Correctness (geometry precision, numeric accuracy)

# Example: Comprehensive benchmark design
class GeospatialBenchmark:
    def __init__(self, dataset_size: str, concurrency: int, gpu_enabled: bool):
        self.dataset_size = dataset_size  # "small", "medium", "large"
        self.concurrency = concurrency    # Number of concurrent operations
        self.gpu_enabled = gpu_enabled     # GPU acceleration enabled
        self.metrics = {
            "runtime": [],
            "throughput": [],
            "memory_peak": [],
            "gpu_utilization": [],
            "correctness": []
        }

# Raster → Vector: Polygonization at scale
def benchmark_raster_polygonization():
    """Benchmark large-scale raster polygonization"""
    import rasterio
    import geopandas as gpd
    from rasterio.features import shapes

    # Load large raster (e.g., 10GB+ GeoTIFF)
    with rasterio.open("large_dem.tif") as src:
        data = src.read(1)
        transform = src.transform
        crs = src.crs

    # Polygonize with different algorithms
    algorithms = ["rasterio", "gdal", "gpu-accelerated"]

    for algo in algorithms:
        start_time = time.time()

        if algo == "rasterio":
            # CPU-based polygonization
            shapes_gen = shapes(data, transform=transform)
            polygons = list(shapes_gen)
        elif algo == "gdal":
            # GDAL-based polygonization
            subprocess.run([
                "gdal_polygonize.py", 
                "large_dem.tif", 
                f"output_{algo}.shp"
            ])
        elif algo == "gpu-accelerated":
            # GPU-accelerated polygonization
            gpu_polygonize(data, transform, crs)

        runtime = time.time() - start_time
        print(f"{algo}: {runtime:.2f}s")

# LiDAR Point Cloud: Voxelization and filtering
def benchmark_point_cloud_processing():
    """Benchmark LiDAR point cloud processing"""
    import pdal
    import numpy as np

    # Large point cloud (millions of points)
    pipeline = {
        "pipeline": [
            {
                "type": "readers.las",
                "filename": "large_cloud.las"
            },
            {
                "type": "filters.voxelgrid",
                "length": 1.0  # 1m voxels
            },
            {
                "type": "filters.statistical",
                "knn": 8,
                "thresh": 1.0
            },
            {
                "type": "filters.outlier",
                "method": "statistical",
                "multiplier": 2.0
            }
        ]
    }

    # CPU processing
    start_time = time.time()
    pipeline_cpu = pdal.Pipeline(json.dumps(pipeline))
    pipeline_cpu.execute()
    cpu_runtime = time.time() - start_time

    # GPU processing (if available)
    if gpu_available():
        start_time = time.time()
        pipeline_gpu = pdal.Pipeline(json.dumps(pipeline))
        pipeline_gpu.execute()  # GPU-accelerated execution
        gpu_runtime = time.time() - start_time

        print(f"CPU: {cpu_runtime:.2f}s, GPU: {gpu_runtime:.2f}s")
        print(f"Speedup: {cpu_runtime/gpu_runtime:.2f}x")

# Isochrone Generation: Valhalla/OSRM
def benchmark_isochrone_generation():
    """Benchmark isochrone generation with routing engines"""
    import requests
    import geopandas as gpd
    from shapely.geometry import Point

    # Test points across different regions
    test_points = [
        (40.7128, -74.0060),  # NYC
        (51.5074, -0.1278),   # London
        (35.6762, 139.6503),  # Tokyo
    ]

    # Valhalla routing
    def valhalla_isochrone(lat, lon, time_limit=30):
        url = "http://localhost:8002/isochrone"
        params = {
            "locations": [{"lat": lat, "lon": lon}],
            "costing": "auto",
            "contours": [{"time": time_limit}]
        }

        start_time = time.time()
        response = requests.post(url, json=params)
        runtime = time.time() - start_time

        return response.json(), runtime

    # OSRM routing
    def osrm_isochrone(lat, lon, time_limit=30):
        url = f"http://localhost:5000/isochrone/v1/driving/{lon},{lat}"
        params = {"contours": [time_limit]}

        start_time = time.time()
        response = requests.get(url, params=params)
        runtime = time.time() - start_time

        return response.json(), runtime

    # Benchmark both engines
    for lat, lon in test_points:
        valhalla_result, valhalla_time = valhalla_isochrone(lat, lon)
        osrm_result, osrm_time = osrm_isochrone(lat, lon)

        print(f"Point ({lat}, {lon}):")
        print(f"  Valhalla: {valhalla_time:.2f}s")
        print(f"  OSRM: {osrm_time:.2f}s")

# Massive Spatial Joins: Polygons intersecting with raster tiles
def benchmark_spatial_joins():
    """Benchmark large-scale spatial joins"""
    import geopandas as gpd
    import pandas as pd
    from shapely.geometry import Point, Polygon
    import numpy as np

    # Generate test data
    def generate_test_data(n_polygons=1000000, n_points=5000000):
        # Large polygon dataset
        polygons = []
        for i in range(n_polygons):
            # Random polygon
            coords = np.random.uniform(-180, 180, (4, 2))
            poly = Polygon(coords)
            polygons.append({
                "id": i,
                "geometry": poly,
                "attribute": f"polygon_{i}"
            })

        # Large point dataset
        points = []
        for i in range(n_points):
            point = Point(np.random.uniform(-180, 180, 2))
            points.append({
                "id": i,
                "geometry": point,
                "attribute": f"point_{i}"
            })

        return gpd.GeoDataFrame(polygons), gpd.GeoDataFrame(points)

    # Generate test data
    polygons_gdf, points_gdf = generate_test_data()

    # Benchmark spatial join
    start_time = time.time()
    result = gpd.sjoin(points_gdf, polygons_gdf, how="inner", predicate="within")
    join_time = time.time() - start_time

    print(f"Spatial join: {join_time:.2f}s")
    print(f"Result count: {len(result)}")
    print(f"Throughput: {len(result)/join_time:.0f} joins/sec")

# cuSpatial: GPU-accelerated spatial operations
def benchmark_cuspatial_operations():
    """Benchmark GPU-accelerated spatial operations"""
    import cuspatial
    import cudf
    import numpy as np

    # Generate large datasets
    n_points = 10_000_000
    n_polygons = 100_000

    # Create test data
    points_x = np.random.uniform(-180, 180, n_points)
    points_y = np.random.uniform(-90, 90, n_points)

    # Convert to cuDF
    points_cudf = cudf.DataFrame({
        "x": points_x,
        "y": points_y
    })

    # Benchmark point-in-polygon
    start_time = time.time()
    result = cuspatial.point_in_polygon(
        points_cudf["x"], points_cudf["y"],
        polygon_x, polygon_y, polygon_offsets
    )
    gpu_time = time.time() - start_time

    print(f"GPU point-in-polygon: {gpu_time:.2f}s")
    print(f"Throughput: {n_points/gpu_time:.0f} points/sec")

# CPU stress testing: Thread scaling and SIMD utilization
def benchmark_cpu_scaling():
    """Benchmark CPU scaling across thread counts"""
    import multiprocessing as mp
    import time
    import psutil

    def spatial_operation(data_chunk):
        """CPU-intensive spatial operation"""
        # Simulate complex spatial computation
        result = []
        for item in data_chunk:
            # Point-in-polygon test
            result.append(complex_spatial_test(item))
        return result

    # Test different thread counts
    thread_counts = [1, 2, 4, 8, 16, 32, 64, 128]
    data_size = 1_000_000

    for threads in thread_counts:
        # Create data chunks
        chunk_size = data_size // threads
        data_chunks = [generate_test_data(chunk_size) for _ in range(threads)]

        # Measure performance
        start_time = time.time()
        with mp.Pool(threads) as pool:
            results = pool.map(spatial_operation, data_chunks)
        runtime = time.time() - start_time

        # Calculate metrics
        throughput = data_size / runtime
        cpu_utilization = psutil.cpu_percent(interval=1)

        print(f"Threads: {threads:3d}, Runtime: {runtime:.2f}s, "
              f"Throughput: {throughput:.0f} ops/sec, "
              f"CPU: {cpu_utilization:.1f}%")

# GPU stress testing: Memory transfer and kernel execution
def benchmark_gpu_operations():
    """Benchmark GPU operations with memory transfer analysis"""
    import cupy as cp
    import cuspatial
    import time

    def measure_gpu_operation(data_size):
        """Measure GPU operation with memory transfer"""
        # Generate test data
        points_x = cp.random.uniform(-180, 180, data_size)
        points_y = cp.random.uniform(-90, 90, data_size)

        # Measure host-to-device transfer
        start_time = time.time()
        points_x_gpu = cp.asarray(points_x)
        points_y_gpu = cp.asarray(points_y)
        h2d_time = time.time() - start_time

        # Measure GPU computation
        start_time = time.time()
        result = cuspatial.point_in_polygon(
            points_x_gpu, points_y_gpu,
            polygon_x_gpu, polygon_y_gpu, polygon_offsets_gpu
        )
        cp.cuda.Stream.null.synchronize()  # Wait for GPU
        gpu_time = time.time() - start_time

        # Measure device-to-host transfer
        start_time = time.time()
        result_cpu = cp.asnumpy(result)
        d2h_time = time.time() - start_time

        return h2d_time, gpu_time, d2h_time

    # Test different data sizes
    data_sizes = [1_000_000, 10_000_000, 100_000_000]

    for size in data_sizes:
        h2d_time, gpu_time, d2h_time = measure_gpu_operation(size)
        total_time = h2d_time + gpu_time + d2h_time

        print(f"Data size: {size:,}")
        print(f"  H2D transfer: {h2d_time:.3f}s")
        print(f"  GPU computation: {gpu_time:.3f}s")
        print(f"  D2H transfer: {d2h_time:.3f}s")
        print(f"  Total time: {total_time:.3f}s")
        print(f"  GPU efficiency: {gpu_time/total_time:.1%}")

# CPU vs GPU comparison
def benchmark_cpu_vs_gpu():
    """Compare CPU and GPU performance"""
    import time
    import numpy as np
    import cupy as cp
    import cuspatial

    # Test data
    n_points = 10_000_000
    points_x = np.random.uniform(-180, 180, n_points)
    points_y = np.random.uniform(-90, 90, n_points)

    # CPU benchmark
    start_time = time.time()
    cpu_result = cpu_point_in_polygon(points_x, points_y, polygons)
    cpu_time = time.time() - start_time

    # GPU benchmark
    start_time = time.time()
    gpu_result = cuspatial.point_in_polygon(
        cp.asarray(points_x), cp.asarray(points_y),
        polygon_x_gpu, polygon_y_gpu, polygon_offsets_gpu
    )
    gpu_time = time.time() - start_time

    # Calculate speedup
    speedup = cpu_time / gpu_time

    print(f"CPU time: {cpu_time:.2f}s")
    print(f"GPU time: {gpu_time:.2f}s")
    print(f"Speedup: {speedup:.2f}x")
    print(f"GPU efficiency: {gpu_time/cpu_time:.1%}")

# Comprehensive metrics collection
class BenchmarkMetrics:
    def __init__(self):
        self.metrics = {
            "runtime": {},
            "throughput": {},
            "memory": {},
            "gpu": {},
            "io": {},
            "correctness": {}
        }

    def measure_runtime(self, operation_name, func, *args, **kwargs):
        """Measure operation runtime"""
        start_time = time.time()
        result = func(*args, **kwargs)
        runtime = time.time() - start_time

        self.metrics["runtime"][operation_name] = runtime
        return result, runtime

    def measure_throughput(self, operation_name, data_size, runtime):
        """Calculate throughput metrics"""
        throughput = data_size / runtime
        self.metrics["throughput"][operation_name] = throughput
        return throughput

    def measure_memory(self, operation_name):
        """Measure memory usage"""
        import psutil
        process = psutil.Process()
        memory_info = process.memory_info()

        self.metrics["memory"][operation_name] = {
            "rss": memory_info.rss,  # Resident Set Size
            "vms": memory_info.vms,  # Virtual Memory Size
            "peak": memory_info.rss  # Peak memory usage
        }

    def measure_gpu_metrics(self, operation_name):
        """Measure GPU utilization and memory"""
        import subprocess

        # Get GPU metrics
        result = subprocess.run([
            "nvidia-smi", "--query-gpu=utilization.gpu,memory.used,memory.total",
            "--format=csv,noheader,nounits"
        ], capture_output=True, text=True)

        if result.returncode == 0:
            gpu_util, mem_used, mem_total = result.stdout.strip().split(", ")
            self.metrics["gpu"][operation_name] = {
                "utilization": int(gpu_util),
                "memory_used": int(mem_used),
                "memory_total": int(mem_total),
                "memory_percent": int(mem_used) / int(mem_total) * 100
            }

    def measure_io_metrics(self, operation_name):
        """Measure I/O performance"""
        import subprocess

        # Get I/O statistics
        result = subprocess.run([
            "iostat", "-x", "1", "1"
        ], capture_output=True, text=True)

        if result.returncode == 0:
            # Parse iostat output
            lines = result.stdout.strip().split('\n')
            for line in lines:
                if 'nvme' in line:  # NVMe drive
                    parts = line.split()
                    self.metrics["io"][operation_name] = {
                        "read_iops": float(parts[3]),
                        "write_iops": float(parts[4]),
                        "read_throughput": float(parts[5]),
                        "write_throughput": float(parts[6])
                    }
                    break

    def validate_correctness(self, operation_name, result, expected):
        """Validate operation correctness"""
        if isinstance(result, np.ndarray) and isinstance(expected, np.ndarray):
            # Numeric precision validation
            tolerance = 1e-6
            is_close = np.allclose(result, expected, rtol=tolerance)
            self.metrics["correctness"][operation_name] = {
                "numeric_precision": is_close,
                "max_error": np.max(np.abs(result - expected)),
                "mean_error": np.mean(np.abs(result - expected))
            }
        else:
            # Geometry validation
            self.metrics["correctness"][operation_name] = {
                "geometry_valid": result.is_valid if hasattr(result, 'is_valid') else True,
                "result_count": len(result) if hasattr(result, '__len__') else 1
            }

# Scaling curve analysis
def analyze_scaling_behavior():
    """Analyze scaling behavior across different dimensions"""
    import matplotlib.pyplot as plt
    import numpy as np

    # Thread scaling
    thread_counts = [1, 2, 4, 8, 16, 32, 64, 128]
    throughputs = []

    for threads in thread_counts:
        throughput = benchmark_with_threads(threads)
        throughputs.append(throughput)

    # Calculate scaling efficiency
    efficiency = [t/throughputs[0] for t in throughputs]

    # Plot scaling curves
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

    # Throughput scaling
    ax1.plot(thread_counts, throughputs, 'bo-')
    ax1.set_xlabel('Thread Count')
    ax1.set_ylabel('Throughput (ops/sec)')
    ax1.set_title('Thread Scaling')
    ax1.grid(True)

    # Efficiency scaling
    ax2.plot(thread_counts, efficiency, 'ro-')
    ax2.set_xlabel('Thread Count')
    ax2.set_ylabel('Efficiency')
    ax2.set_title('Scaling Efficiency')
    ax2.grid(True)

    plt.tight_layout()
    plt.savefig('scaling_analysis.png')
    plt.show()

# ❌ WRONG: Synthetic trivial data
def bad_benchmark():
    """Don't benchmark with trivial data"""
    # All points in same bounding box
    points = [(0, 0), (0.1, 0.1), (0.2, 0.2)]
    polygon = Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])

    # This doesn't stress-test the algorithm
    result = point_in_polygon(points, polygon)

# ✅ CORRECT: Real-world data
def good_benchmark():
    """Benchmark with real-world data"""
    # Load actual geospatial dataset
    points = load_real_points("osm_points.parquet")
    polygons = load_real_polygons("admin_boundaries.shp")

    # This stress-tests the algorithm
    result = point_in_polygon(points, polygons)

# ❌ WRONG: No error handling
def bad_benchmark():
    """Don't ignore error conditions"""
    result = spatial_operation(data)
    return result  # What if it fails?

# ✅ CORRECT: Comprehensive error handling
def good_benchmark():
    """Handle error conditions"""
    try:
        result = spatial_operation(data)
        return result
    except MemoryError:
        print("Out of memory - reducing data size")
        return benchmark_with_smaller_data(data)
    except Exception as e:
        print(f"Operation failed: {e}")
        return None

# Benchmark execution checklist
def benchmark_execution_checklist():
    """Comprehensive benchmark execution checklist"""

    # Pre-execution
    print("✓ Environment validated")
    print("✓ Data loaded and validated")
    print("✓ Monitoring started")
    print("✓ Baseline measurements taken")

    # Execution
    print("✓ Cold runs completed")
    print("✓ Warm runs completed")
    print("✓ Thread scaling tested")
    print("✓ GPU acceleration tested")
    print("✓ Memory pressure tested")
    print("✓ I/O bottlenecks identified")

    # Post-execution
    print("✓ Results validated")
    print("✓ Correctness verified")
    print("✓ Performance regression checked")
    print("✓ Report generated")
    print("✓ Artifacts archived")

# Prefect workflow for automated benchmarking
from prefect import flow, task
from prefect.task_runners import SequentialTaskRunner

@task
def setup_environment():
    """Setup benchmark environment"""
    # Clear caches
    subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"])

    # Configure system
    subprocess.run(["nvidia-smi", "--query-gpu=utilization.gpu"])

    return "Environment ready"

@task
def run_benchmark(benchmark_name, dataset_size, concurrency):
    """Run individual benchmark"""
    # Execute benchmark
    result = execute_benchmark(benchmark_name, dataset_size, concurrency)

    # Collect metrics
    metrics = collect_metrics()

    return {"benchmark": benchmark_name, "result": result, "metrics": metrics}

@flow(task_runner=SequentialTaskRunner)
def benchmark_workflow():
    """Complete benchmark workflow"""
    # Setup
    env_status = setup_environment()

    # Run benchmarks
    benchmarks = [
        ("raster_polygonization", "large", 64),
        ("point_cloud_processing", "large", 32),
        ("spatial_joins", "large", 128),
        ("gpu_acceleration", "large", 8)
    ]

    results = []
    for benchmark_name, dataset_size, concurrency in benchmarks:
        result = run_benchmark(benchmark_name, dataset_size, concurrency)
        results.append(result)

    # Generate report
    generate_report(results)

    return results

# Run workflow
if __name__ == "__main__":
    benchmark_workflow()

# Quick benchmark template
import time
import psutil
import subprocess

def quick_benchmark(operation, data):
    """Quick benchmark template"""
    # Clear caches
    subprocess.run(["echo", "3", ">", "/proc/sys/vm/drop_caches"])

    # Measure memory before
    memory_before = psutil.virtual_memory().used

    # Run operation
    start_time = time.time()
    result = operation(data)
    runtime = time.time() - start_time

    # Measure memory after
    memory_after = psutil.virtual_memory().used
    memory_used = memory_after - memory_before

    # Calculate throughput
    throughput = len(data) / runtime

    print(f"Runtime: {runtime:.2f}s")
    print(f"Throughput: {throughput:.0f} ops/sec")
    print(f"Memory: {memory_used / 1024**2:.1f} MB")

    return result, runtime, throughput, memory_used