CODEWITHSUNDEEP
python-3.xperformancepython-multithreading
b3hn4m
b3hn4m

Reputation: 75

threading does not speed up the process in python

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.')

Upvotes: 0

Views: 569

Answers (1)

grmmgrmm
grmmgrmm

Reputation: 1408

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.

Upvotes: 1

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