Python Tutorial - 27. Multiprocessing Introduction
Why Stack Overflow recommends it
Stack Overflow users link it as a quick starter for running Python code in parallel with the multiprocessing module, in both answers and questions.
Recommended by 3 people across 2 different months, 2017 to 2018.
What it is
A short tutorial from the codebasics channel that introduces Python's multiprocessing module through a worked example. It also ends with a comparison of multiprocessing and multithreading, and is the 27th part of a numbered Python series.
What people said
Recommends Juan Antonio · 2018-01 · Stack Overflow · on “python - What is the fastest way to replace lines of a file from a dictionary?”
You can use multiprocessing [LINK] python package that simplify threading (among others) to speed up your code. Here [LINK] you can see a good post where is talking about and some videos [this video] .
Recommends Victor Pira · 2018-01 · Stack Overflow · on “What is wrong with this multiprocessing example?”
Based on this pretty useful tutorial [this video] I have tried to make a simple implementation of Python multiprocessing to measure its effectivity. The modules multi1 , multi2 , multi3 contain an ODE integration and exporting the calculated values in a csv (it does not matter, they are here for a script to do something). import multiprocessing import multi1 import multi2 import multi3 import time t0 = time.time() if __name__ == '__main__': p1 = multiprocessing.Process(target = multi1.main(), args=()) p2 = multiprocessing.Process(target = multi2.main(), args=()) p3 = multiprocessing.Process(target = multi3.main(), args=()) p1.start() p2.start() p3.start() p1.join() p2.join() p3.join() t1 = time.t
Recommends zafrin · 2018-06 · Stack Overflow · on “Better parallel processing python lib without using Hadoop in python”
Pool() from multiprocessing may be what you are looking for. Bellow is an example from somethng I have worked on today. There are other ways to this. Youtube has some good stuff. Check outh videos on this series [this video] . from multiprocessing import Pool def process(c_id): try: # A single process that you want to do # Ex. do things and save it in file except: # something else def main(): pool = Pool() while True: ids = get_new_set_of_ids() # has to be an itterator having the same size as your number of cores pool.map(process, ids) # map() function maps the processes with the number of cores
Cites Eric · 2017-05 · Stack Overflow · on “Create a parallel 'for' loop?”
The multiprocessing [LINK] module in Python is both Unix and Windows compatible and offers remote and local concurrency. The Global Interpreter Lock [LINK] uses subprocesses instead of threads. Processes are spawned by creating a ‘Process’ object and then calling its start() [LINK] method. The process follows the API of threading.Thread [LINK] . The below example demonstrates how to execute two different functions, each with a for loop, in parallel by creating a process for each. As an interesting note, each process its own address space (virtual memory). So, program variables are not shared between processes. To illustrate this, I’ve created two global variables and set them to the opposite