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multiprocessingThis article will explain different ways to perform parallel function execution using the module in Python .

multiprocessingThe module provides functionality to perform parallel function execution using multiple inputs and distribute the input data among different processes.

We can execute functions with different input values ​​in parallel by using the following method in Python.

Use pool.map()the method to execute parallel functions

pool.map(function, iterable)The method returns an iterator that applies the provided as input to each item of functionthe input iterable. So if we want to execute in parallel with different inputs 函数, we can use pool.map()the method.

The following sample code demonstrates how to use pool.map()the method to parallelize function execution in Python.

from multiprocessing import Pool


def myfunc(x):
    return 5 + x


if __name__ == "__main__":
    with Pool(3) as p:
        print(p.map(myfunc, [1, 2, 3]))

Output:

[6, 7, 8]

If the input functionhas multiple parameters, we can use pool.map()the method and partial()function to execute the function in parallel.

The following example demonstrates how to use Python pool.map()to execute functions in parallel with multiple arguments.

from multiprocessing import Pool
from functools import partial


def multiply(x, y):
    print(x * y)


if __name__ == "__main__":
    with Pool(3) as p:
        p.map(partial(multiply, 5), [1, 2, 3])

Output:

5
10
15

As can be seen in the above example, the disadvantage of this approach is that we cannot change the value of the first parameter.

Use pool.starmap()the method to execute parallel functions with multiple parameters

If we want to execute a function with multiple arguments in parallel, we can use pool.starmap(function, iterable)the method.

Like pool.map(function, iterable)the method, pool.starmap(function, iterable)the method returns an iterator that functionapplies the provided as input to iterableeach item of . However, it expects each input item of iterableto be arranged as an input functionargument iterable.

By using pool.starmap()the method, we can functionprovide different values ​​for all the parameters of , unlike pool.map()the method.

We can pool.starmap()perform parallel function execution with multiple arguments in Python using method in the following way.

from multiprocessing import Pool


def print_name(name, lname):
    print("full name =", name, lname)


if __name__ == "__main__":
    with Pool(3) as p:
        p.starmap(print_name, [("Thomas", "Scott"), ("Ali", "Khan")])

Output:

full name = Thomas Scott
full name = Ali Khan