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pythonmultithreadingiteration
Anthony
Anthony

Reputation: 834

Simple multithread for loop in Python

I searched everywhere and I don't find any simple example of iterating a loop with multithreading.

For example, how can I multithread this loop?

for item in range(0, 1000):
    print(item)

Is there any way to cut it in like 4 threads, so each thread has 250 iterations?

Upvotes: 21

Views: 56274

Answers (3)

MisterMiyagi
MisterMiyagi

Reputation: 50096

Since Python 3.2, the concurrent.futures standard library provides primitives to concurrently map a function across iterables. Since map and for are closely related, this allows to easily convert a for loop into a multi-threaded/multi-processed loop:

from concurrent.futures import ThreadPoolExecutor

with ThreadPoolExecutor() as executor:
    executor.map(print, range(0, 1000))

Upvotes: 6

janbrohl
janbrohl

Reputation: 2656

Easiest way is with multiprocessing.dummy (which uses threads instead of processes) and a Pool

import multiprocessing.dummy as mp 

def do_print(s):
    print s

if __name__=="__main__":
    p=mp.Pool(4)
    p.map(do_print,range(0,10)) # range(0,1000) if you want to replicate your example
    p.close()
    p.join()

Maybe you want to try real multiprocessing, too if you want to better utilize multiple CPUs but there are several caveats and guidelines to follow then.

Possibly other methods of Pool would better suit your needs - depending on what you are actually trying to do.

Upvotes: 27

ForceBru
ForceBru

Reputation: 44838

You'll have to do the splitting manually:

import threading

def ThFun(start, stop):
    for item in range(start, stop):
        print item

for n in range(0, 1000, 100):
    stop = n + 100 if n + 100 <= 1000 else 1000
    threading.Thread(target = ThFun, args = (n, stop)).start()

This code uses multithreading, which means that everything will be run within a single Python process (i.e. only one Python interpreter will be launched).

Multiprocessing, discussed in the other answer, means running some code in several Python interpreters (in several processes, not threads). This may make use of all the CPU cores available, so this is useful when you're focusing on the speed of your code (print a ton of numbers until the terminal hates you!), not simply on parallel processing. 1

1. multiprocessing.dummy turns out to be a wrapper around the threading module. multiprocessing and multiprocessing.dummy have the same interface, but the first module does parallel processing using processes, while the latter - using threads.

Upvotes: 10

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