Multiprocessing Basics

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
import time

# CPU-bound task
def compute(n):
    """Simulate heavy computation."""
    total = sum(i * i for i in range(n))
    return total

# Sequential
start = time.time()
results = [compute(100_000) for _ in range(4)]
print(f"Sequential: {time.time() - start:.3f}s")

# With ThreadPool (limited by GIL for CPU)
start = time.time()
with ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(compute, [100_000] * 4))
print(f"ThreadPool: {time.time() - start:.3f}s")

# When to use what:
print("\n=== Threading vs Multiprocessing ===")
print("Threading  → I/O-bound → network, file, database")
print("Multiprocessing → CPU-bound → math, image, data")
print("asyncio    → I/O-bound → many concurrent connections")

# ProcessPoolExecutor (runs in separate processes)
# start = time.time()
# with ProcessPoolExecutor(max_workers=4) as executor:
#     results = list(executor.map(compute, [100_000] * 4))
# print(f"ProcessPool: {time.time() - start:.3f}s")

# Note: ProcessPoolExecutor requires __name__ == '__main__'
# guard and cannot be demonstrated in some environments