Summary - Multiprocessing

Combine Process, Pool and Queue to write a parallel worker.

Recap: Multiprocessing

In this group you learned process-based parallelism. You started new processes with Process and managed their lifecycle with start and join.

You passed data between processes with Queue. You did parallel mapping with Pool. You used a shared list with Manager. Now you will combine these into a parallel worker: a Process, a Queue and a computation together.

Exercise Write a parallel worker

Import with from multiprocessing import Process, Queue. Define a worker(q, val) function that does q.put(val * 2). Create a queue with q = Queue(). Create a process with Process(target=worker, args=(q, 21)), call .start() and .join(). Print q.get().

Show example solution

from multiprocessing import Process, Queue

def worker(q, val):
    q.put(val * 2)

q = Queue()
p = Process(target=worker, args=(q, 21))
p.start()
p.join()
print(q.get())

What you learned

In this group you learned process-based parallelism in Python. You saw new processes with Process and lifecycle management with start and join.

You used Queue for inter-process data, Pool for parallel mapping and Manager for shared objects. In the summary you combined these into a parallel worker. This knowledge is critical for speeding up CPU-bound work.

Test yourself

Which tool for which job?
- Process for parallel mapping
- Queue for inter-process data
- Manager for function calls
- Pool to start a single process
Show answer

Queue for inter-process data — answer: Queue for inter-process data. Pool does parallel mapping, Process starts a single process.
Match all multiprocessing concepts

Process

New process
Queue

Data queue
Pool

Process pool
Manager

Shared object

Final blank

from multiprocessing import Process, ____
q = Queue()

Show answer

from multiprocessing import Process, Queue
q = Queue()

Next lesson: threading.Thread

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