如何在python多处理中实现reduce操作？

Question

I'm an expert parallel programmer in OpenMP and C++. Now I'm trying to understand parallelism in python and the multiprocessing library.

In particular, I'm trying to parallelize this simple code, which randomly increment an array for 100 times:

from random import randint
import multiprocessing as mp
import numpy as np

def random_add(x):
    x[randint(0,len(x)-1)]  += 1

if __name__ == "__main__":
    print("Serial")
    x = np.zeros(8)
    for i in range(100):
        random_add(x)
    print(x)

    print("Parallel")
    x = np.zeros(8)    
    processes = [mp.Process(target = random_add, args=(x,)) for i in range(100)]
    for p in processes:
        p.start()
    print(x)

However,this is the following output:

Serial
[  9.  18.  11.  15.  16.   8.  10.  13.]
Parallel
[ 0.  0.  0.  0.  0.  0.  0.  0.]

Why this happens? Well, I think I have an explanation: since we are in multiprocessing (and not multi-threading), each process as his own section of memory, ie, each spawned process has his own x , which is destroyed once random_add(x) is terminated. As conclusion, the x in the main program is never really updated.

Is this correct? And if so, how can I solve this problem? In a few words, I need a global reduce operation which sum the results of all the random_add calls, obtaining the same result of the serial version.

Answer 1

You should use shared memory objects in your case:

from random import randint
import multiprocessing as mp

def random_add(x):
    x[randint(0,len(x)-1)]  += 1

if __name__ == "__main__":
    print("Serial")
    x = [0]*8
    for i in range(100):
        random_add(x)
    print(x)

    print("Parallel")
    x = mp.Array('i', range(8))
    processes = [mp.Process(target = random_add, args=(x,)) for i in range(100)]
    for p in processes:
        p.start()
    print(x[:])

I've changed numpy array to ordinal list for the purpose of clearness of code