Weirdo multiprocessing: Shared objects taking more time in sharing smaller data than larger data between multiple processes.

Bug report

Bug description:

server.py

from multiprocessing.managers import BaseManager
from queue import Queue
queue = Queue()
class QueueManager(BaseManager): pass
QueueManager.register('get_queue', callable=lambda:queue)
m = QueueManager(address=('', 5000), authkey=b'abracadabra')
s = m.get_server()
s.serve_forever()

consumer.py

import time

from multiprocessing.managers import BaseManager

class QueueManager(BaseManager): pass

QueueManager.register('get_queue')
m = QueueManager(address=('', 5000), authkey=b'abracadabra')
m.connect()
queue = m.get_queue()

while True:
    t = time.time()
    x = queue.get()

producerA.py

from multiprocessing.managers import BaseManager
import time
import numpy as np

class QueueManager(BaseManager): pass

QueueManager.register('get_queue')

m = QueueManager(address=('', 5000), authkey=b'abracadabra')
m.connect()
queue = m.get_queue()

out_img = np.zeros((128, 128, 3), dtype=np.uint8)

for i in range(100):
    t = time.time()
    queue.put(
    {
        'type' : 'not working',
        'data': out_img
    }    
    )
    print('put took', (time.time() - t)*1000)

producerB.py

from multiprocessing.managers import BaseManager
import time
import numpy as np

class QueueManager(BaseManager): pass

QueueManager.register('get_queue')

m = QueueManager(address=('', 5000), authkey=b'abracadabra')
m.connect()
queue = m.get_queue()

out_img = np.zeros((256, 256, 3), dtype=np.uint8)

for i in range(100):
    t = time.time()
    queue.put(
    {
        'type' : 'not working',
        'data': out_img
    }    
    )
    print('put took', (time.time() - t)*1000)

steps to reproduce the issue

• run server.py
• run consumer.py
• run producerA.py and check the put time and compare it with producerB.py put time.

put time in producerA.py is higher than producerB.py, however the size of data being send through shared queue objects is more in producerB.py( 256x256 ) than in producerA.py (128x128).
I don't have much context if this is related to numpy or cpython multiprocessing, but this is definitely shouldn't be the case.
Ideally it should take more time in producerB.py beacuase the size of data is relatively higher relative to producerA.py.

This is my first bug report, I'm hoping to learn something...

Thanks everyone...

CPython versions tested on:

3.10

Operating systems tested on:

Linux

Comece executando server.py e consumer.py e, em seguida, compare os tempos de producerA.py e producerB.py na configuração informada de Linux e CPython 3.10. Rastreie o comportamento pela multiprocessing manager queue e pelos caminhos de serialização do NumPy no CPython. O relatório não nomeia nenhum arquivo-fonte nem alvo de teste, portanto a conclusão exige uma causa confirmada e uma resolução apropriada para o projeto.