All 15 Concurrency (asyncio/threading/multiprocessing) questions
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What is the basic pattern for running an async function using asyncio.run()? How does the Global Interpreter Lock (GIL) affect CPU-bound versus I/O-bound multithreaded programs differently? How do you run multiple coroutines concurrently using asyncio.gather()? How do you protect shared mutable state from race conditions when using multiple threads, using threading.Lock? What is a race condition, and how does it differ from a deadlock? How does asyncio's event loop schedule and switch between multiple coroutines, and what makes this cooperative rather than preemptive? How do you safely run a blocking, synchronous function from within an asyncio coroutine without freezing the event loop? How do you share data between processes using multiprocessing, given that separate processes don't share memory the way threads do? How would you implement a producer-consumer pattern combining a thread-safe queue.Queue with multiple worker threads? How do you correctly cancel a running asyncio task, and how should the task itself handle the resulting CancelledError?
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Python › Concurrency (asyncio/threading/multiprocessing)

How does multiprocessing.Pool let you parallelize a CPU-bound function across multiple worker processes?

Intermediate

Answer

Pool.map() (or apply_async() for more control) distributes calls of a function across a pool of worker PROCESSES (each with its own Python interpreter, bypassing the GIL entirely), running them in true parallel on separate CPU cores, and collects the results back in the main process.
from multiprocessing import Pool

def square(n):
    return n * n

if __name__ == "__main__":
    with Pool(processes=4) as pool:
        results = pool.map(square, range(10))
    print(results)  # [0, 1, 4, 9, ..., 81], computed across 4 parallel processes
Real-world example Parallelizing a CPU-intensive batch computation (like image resizing or numerical simulation) across all available CPU cores.

Common follow-ups: Why must code using multiprocessing.Pool typically be guarded by 'if __name__ == "__main__":' on Windows?

The Global Interpreter Lock (GIL)
All 15 Concurrency (asyncio/threading/multiprocessing) questions