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pythonmultiprocessingshared
Vedant7
Vedant7

Reputation: 71

Multiprocessing - Shared Array

So I'm trying to implement multiprocessing in python where I wish to have a Pool of 4-5 processes running a method in parallel. The purpose of this is to run a total of thousand Monte simulations (250-200 simulations per process) instead of running 1000. I want each process to write to a common shared array by acquiring a lock on it as soon as its done processing the result for one simulation, writing the result and releasing the lock. So it should be a three step process :

  1. Acquire lock
  2. Write result
  3. Release lock for other processes waiting to write to array.

Everytime I pass the array to the processes each process creates a copy of that array which I donot want as I want a common array. Can anyone help me with this by providing sample code?

Upvotes: 6

Views: 19375

Answers (2)

Julien
Julien

Reputation: 1910

Not tested, but something like that should work. The array and lock are shared between processes.

from multiprocessing import Process, Array, Lock

def f(array, lock, n): #n is the dedicated location in the array
    lock.acquire()
    array[n]=-array[n]
    lock.release()

if __name__ == '__main__':
    size=100
    arr=Array('i', [3,-7])
    lock=Lock()
    p = Process(target=f, args=(arr,lock,0))
    q = Process(target=f, args=(arr,lock,1))
    p.start()
    q.start()
    q.join()
    p.join()

    print(arr[:])

the documentation here https://docs.python.org/3.5/library/multiprocessing.html has plenty of examples to start with

Upvotes: 1

Dunes
Dunes

Reputation: 40703

Since you're only returning state from the child process to the parent process, then using a shared array and explicity locks is overkill. You can use Pool.map or Pool.starmap to accomplish exactly what you need. For example:

from multiprocessing import Pool

class Adder:
    """I'm using this class in place of a monte carlo simulator"""

    def add(self, a, b):
        return a + b

def setup(x, y, z):
    """Sets up the worker processes of the pool. 
    Here, x, y, and z would be your global settings. They are only included
    as an example of how to pass args to setup. In this program they would
    be "some arg", "another" and 2
    """
    global adder
    adder = Adder()

def job(a, b):
    """wrapper function to start the job in the child process"""
    return adder.add(a, b)

if __name__ == "__main__":   
    args = list(zip(range(10), range(10, 20)))
    # args == [(0, 10), (1, 11), ..., (8, 18), (9, 19)]

    with Pool(initializer=setup, initargs=["some arg", "another", 2]) as pool:
        # runs jobs in parallel and returns when all are complete
        results = pool.starmap(job, args)

    print(results) # prints [10, 12, ..., 26, 28]

Upvotes: 3

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