Python Language The multiprocessing module


Example

from __future__ import print_function
import multiprocessing


def countdown(count):
    while count > 0:
        print("Count value", count)
        count -= 1
    return

if __name__ == "__main__":
    p1 = multiprocessing.Process(target=countdown, args=(10,))
    p1.start()

    p2 = multiprocessing.Process(target=countdown, args=(20,))
    p2.start()

    p1.join()
    p2.join()

Here, each function is executed in a new process. Since a new instance of Python VM is running the code, there is no GIL and you get parallelism running on multiple cores.

The Process.start method launches this new process and run the function passed in the target argument with the arguments args. The Process.join method waits for the end of the execution of processes p1 and p2.

The new processes are launched differently depending on the version of python and the plateform on which the code is running e.g.:

  • Windows uses spawn to create the new process.
  • With unix systems and version earlier than 3.3, the processes are created using a fork.
    Note that this method does not respect the POSIX usage of fork and thus leads to unexpected behaviors, especially when interacting with other multiprocessing libraries.
  • With unix system and version 3.4+, you can choose to start the new processes with either fork, forkserver or spawn using multiprocessing.set_start_method at the beginning of your program. forkserver and spawn methods are slower than forking but avoid some unexpected behaviors.

POSIX fork usage:

After a fork in a multithreaded program, the child can safely call only async-signal-safe functions until such time as it calls execve.
(see)

Using fork, a new process will be launched with the exact same state for all the current mutex but only the MainThread will be launched. This is unsafe as it could lead to race conditions e.g.:

  • If you use a Lock in MainThread and pass it to an other thread which is suppose to lock it at some point. If the fork occures simultaneously, the new process will start with a locked lock which will never be released as the second thread does not exist in this new process.

Actually, this kind of behavior should not occured in pure python as multiprocessing handles it properly but if you are interacting with other library, this kind of behavior can occures, leading to crash of your system (for instance with numpy/accelerated on macOS).