Use multiprocessings 'Pool' together with 'RawArray'

Whenever I try to use shared memory with pythons 'multiprocessing' module to fill a huge array in parallel I use something like:

import numpy as np
from multiprocessing import Process, RawArray

def tf(x, arr):
    arr = np.reshape( np.frombuffer( arr, dtype=np.float32 ), -1 ).reshape((10, 10, 10))
    arr[x] = np.random.random((10, 10))
    
mpa = RawArray('f', 1000)
ncpu = 4

procs = []
for i in range(10):
    procs.append(Process(target=tf, args=(i, mpa)))
    procs[-1].start()                                    
    if len(procs) == ncpu:
        procs[0].join()
        procs.pop(0)

for p in procs:
    p.join()
   
arr = np.reshape( np.frombuffer( mpa, dtype=np.uint32 ), -1).reshape((10, 10, 10))

to ensure that only as many processes are active as I have cpus. If I try to use 'Pool' and 'apply_async' the array is not altered for some reason. So I wonder if it is possible to either use 'Pool' or any other intended way to manage the amount of active processes.

The above code is working but is not the most efficient since I only if the process I added first is finished to decide if I should add another process.