threading does not speed up the process in python

Question

I am just learning the threading module of python, threading implementation of a test code as below takes more time compared to the sequential implementation. Am I missing an underlying concept of threading in python?

from time import sleep, perf_counter from threading import Thread def task(id): print(f'Starting the task {id}...') for i in range(1, 1000): for j in range(1, 1000): b=(i**2)/(i*j**3) print(f'The task {id} completed') ############## sequential ############## start_time = perf_counter() for n in range(1, 11): task(n) end_time = perf_counter() print(f'sequential took {end_time- start_time: f} second(s) to complete.') ##########E Multi-threading ########## start_time = perf_counter() threads = [] for n in range(1, 11): t = Thread(target=task, args=(n,)) threads.append(t) t.start() for t in threads: t.join() end_time = perf_counter() print(f'multi-threaded took {end_time- start_time: f} second(s) to complete.')

grmmgrmm · Accepted Answer · 2022-06-29 07:54:30Z

I think your threading solution looks nice and correct but there is a pitfal of using threading in python as it is still using a single core. Check this tutorial which I find really helpful to understand why: https://www.quantstart.com/articles/Parallelising-Python-with-Threading-and-Multiprocessing/

The essence from that resource:

The GIL is necessary because the Python interpreter is not thread safe. This means that there is a globally enforced lock when trying to safely access Python objects from within threads. At any one time only a single thread can acquire a lock for a Python object or C API. The interpreter will reacquire this lock for every 100 bytecodes of Python instructions and around (potentially) blocking I/O operations. Because of this lock CPU-bound code will see no gain in performance when using the Threading library, but it will likely gain performance increases if the Multiprocessing library is used.

In other words: Use multiprocessing instead.

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