Python多处理：跨进程共享变量的值递增

Question

Here's a minimal example of what I want to achieve.

The global INIT_* values should be incremented by each process and made available for another function ( print_locked in this example):

import multiprocessing as mp

INIT_NODE = mp.Value('i', 1000)
INIT_WAY = mp.Value('i', 1000)
INIT_REL = mp.Value('i', 1000)


def print_locked(a, b, c):
    print(a.value, b.value, c.value)


def process_results(a):
    with mp.Lock():
        INIT_NODE.value += 20000000
        INIT_WAY.value += 10000000
        INIT_REL.value += 1000000

    print_locked(INIT_NODE, INIT_WAY, INIT_REL)


def main():
    """Entry point for the program."""

    a = range(5)
    pool = mp.Pool(4)
    pool.map(process_results, a)


if __name__ == '__main__':
    main()

However, it seems that each process increments its own version of the initial values, which is exactly what I want to avoid.

Example output:

20001000 20001000 2001000
20001000 20001000 2001000
40001000 30001000 3001000
60001000 40001000 4001000
80001000 50001000 5001000

Answer 1

mp.Lock() create lock object but it does not lock the block of code. To lock critical region of code you should first call acquire method of mp.Lock object and call release method of the object after the critical region.

INIT_NODE = mp.Value('i', 1000)
INIT_WAY = mp.Value('i', 1000)
INIT_REL = mp.Value('i', 1000)
lock = mp.Lock()


def process_results(a):
    lock.acquire()

    INIT_NODE.value += 20000000
    INIT_WAY.value += 10000000
    INIT_REL.value += 1000000
    print_locked(INIT_NODE, INIT_WAY, INIT_REL)
    lock.release()

Now the output of this program is the same for all run

20001000 10001000 1001000
40001000 20001000 2001000
60001000 30001000 3001000
80001000 40001000 4001000
100001000 50001000 5001000