Python 多处理产生不稳定的结果

Question

Can anyone help me understand why this simple example of trying to speed up a for loop using python's multiprocessing module produces unstable results? I use a Manager.List to store the values from the child processes.

Clearly I'm doing at least one thing wrong. What would be the correct way to do this?

import numpy as np
import multiprocessing
from matplotlib import pyplot as plt

from functools import partial
from multiprocessing import Manager


def run_parallel(x_val, result):

    val = np.arctan(x_val)

    result.append(val)


def my_func(x_array, parallel=False):

    if not parallel:
        result = []
        for k in x_array:
            result.append(np.arctan(k))

        return result

    else:
        manager = Manager()
        m_result = manager.list()

        pool = multiprocessing.Pool(4)
        pool.map(partial(run_parallel, result=m_result), x_array)

        return list(m_result)


test_x = np.linspace(0.1,1,50)
serial = my_func(test_x,parallel=False)
parallel = my_func(test_x,parallel=True)

plt.figure()
plt.plot(test_x, serial, label='serial')
plt.plot(test_x,parallel, label='parallel')
plt.legend(loc='best')
plt.show()

The output I'm getting looks like this

and it looks different every time this runs.

Answer 1

I added some print functions and it turned out that the order of elements from x_array is arbitrary... That's why it looks so weird. I think you should keep argument and value of arctan pairs and then order it by argument value

EDIT

I read more and it turned out that map returns values in order... This works as you wanted:

import numpy as np
import multiprocessing
from matplotlib import pyplot as plt

from functools import partial
from multiprocessing import Manager


def run_parallel(x_val, result):

    val = np.arctan(x_val)
    return val


def my_func(x_array, parallel=False):
    if not parallel:
        result = []
        for k in x_array:
            result.append(np.arctan(k))

        return result

    else:
        manager = Manager()
        m_result = manager.list()

        pool = multiprocessing.Pool(4)
        x = pool.map(partial(run_parallel, result=m_result), x_array)
        return list(x)


test_x = np.linspace(0.1,1,50)
parallel = my_func(test_x,parallel=True)

plt.figure()
plt.plot(test_x,parallel, label='parallel')
plt.legend(loc='best')
plt.show()