Memory usage keep growing with Python's multiprocessing.pool

Question

Here's the program:

#!/usr/bin/python

import multiprocessing

def dummy_func(r):
    pass

def worker():
    pass

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=16)
    for index in range(0,100000):
        pool.apply_async(worker, callback=dummy_func)

    # clean up
    pool.close()
    pool.join()

I found memory usage (both VIRT and RES) kept growing up till close()/join(), is there any solution to get rid of this? I tried maxtasksperchild with 2.7 but it didn't help either.

I have a more complicated program that calles apply_async() ~6M times, and at ~1.5M point I've already got 6G+ RES, to avoid all other factors, I simplified the program to above version.

EDIT:

Turned out this version works better, thanks for everyone's input:

#!/usr/bin/python

import multiprocessing

ready_list = []
def dummy_func(index):
    global ready_list
    ready_list.append(index)

def worker(index):
    return index

if __name__ == '__main__':
    pool = multiprocessing.Pool(processes=16)
    result = {}
    for index in range(0,1000000):
        result[index] = (pool.apply_async(worker, (index,), callback=dummy_func))
        for ready in ready_list:
            result[ready].wait()
            del result[ready]
        ready_list = []

    # clean up
    pool.close()
    pool.join()

I didn't put any lock there as I believe main process is single threaded (callback is more or less like a event-driven thing per docs I read).

I changed v1's index range to 1,000,000, same as v2 and did some tests - it's weird to me v2 is even ~10% faster than v1 (33s vs 37s), maybe v1 was doing too many internal list maintenance jobs. v2 is definitely a winner on memory usage, it never went over 300M (VIRT) and 50M (RES), while v1 used to be 370M/120M, the best was 330M/85M. All numbers were just 3~4 times testing, reference only.

Answer 1

I had memory issues recently, since I was using multiple times the multiprocessing function, so it keep spawning processes, and leaving them in memory.

Here's the solution I'm using now:

def myParallelProcess(ahugearray):
    from multiprocessing import Pool
    from contextlib import closing
    with closing(Pool(15)) as p:
        res = p.imap_unordered(simple_matching, ahugearray, 100)
    return res