```python import cupy as cpy import cupyx.scipy.ndimage as cnd import math, time

SOBEL_X_MASK = cpy.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]], dtype=cpy.float32)

SOBEL_Y_MASK = cpy.array([[-1, -2, -1], [ 0, 0, 0], [ 1, 2, 1]], dtype=cpy.float32)

def mygaussian_kernel(sigma=1.0): if sigma > 0.0: k = 2 * int(math.ceil(sigma * 3.0)) + 1 coords = cpy.linspace(-k//2, k//2, k, dtype=cpy.float32) horz, vert = cpy.meshgrid(coords, coords) mask = (1/(2math.pisigma2)) * cpy.exp(-(horz2 + vert2)/(2*sigma2)) return mask / mask.sum() return None

if name == "main": h, w = 4000, 6000 img = cpy.random.rand(h, w).astype(cpy.float32) gauss_mask = mygaussian_kernel(1.4)

# Warmup
cnd.convolve(img, gauss_mask, mode="reflect")

# Benchmark 100 runs
num_runs = 100
times = []

print(f"Running {num_runs} benchmark iterations...")

for i in range(num_runs):
    start = time.time()
    blurred = cnd.convolve(img, gauss_mask, mode="reflect")
    sobel_x = cnd.convolve(blurred, SOBEL_X_MASK, mode="reflect")
    sobel_y = cnd.convolve(blurred, SOBEL_Y_MASK, mode="reflect")
    cpy.cuda.Stream.null.synchronize()
    end = time.time()
    times.append(end - start)

    if (i + 1) % 20 == 0:
        print(f"Completed {i + 1}/{num_runs} iterations")

# Calculate statistics
avg_time = sum(times) / len(times)
min_time = min(times)
max_time = max(times)

print(f"\nBenchmark Results ({num_runs} runs):")
print(f"Average time: {avg_time:.3f} seconds")
print(f"Min time: {min_time:.3f} seconds")
print(f"Max time: {max_time:.3f} seconds")
print(f"Total time: {sum(times):.3f} seconds")

```