A conceptual question on python gil what does it actually mean

The Global Interpreter Lock (GIL) in Python is a mechanism used in the CPython interpreter to synchronize access to Python objects, preventing multiple native threads from executing Python bytecodes at once. This means that even if you have a multi-core processor, only one thread can execute Python bytecodes at a time.

Solution 1: Using multiprocessing

One way to overcome the limitations of the GIL is to use the multiprocessing module in Python. This module allows you to create separate processes, each with its own Python interpreter and memory space. By using processes instead of threads, you can take advantage of multiple cores and bypass the GIL.

import multiprocessing

def my_function():
    # Your code here

if __name__ == '__main__':
    processes = []
    for i in range(multiprocessing.cpu_count()):
        p = multiprocessing.Process(target=my_function)

    for p in processes:

In this solution, we create multiple processes, each running the same function. Each process has its own Python interpreter and memory space, allowing them to execute Python bytecodes in parallel.

Solution 2: Using threading

Another way to work around the GIL is to use the threading module in Python. Although threads in Python cannot run in parallel due to the GIL, they can still be useful for tasks that are I/O bound or involve waiting for external resources.

import threading

def my_function():
    # Your code here

if __name__ == '__main__':
    threads = []
    for i in range(10):
        t = threading.Thread(target=my_function)

    for t in threads:

In this solution, we create multiple threads, each running the same function. Although the threads cannot execute Python bytecodes in parallel, they can still be useful for tasks that involve waiting for I/O or external resources.

Solution 3: Using asynchronous programming

Asynchronous programming is another way to work around the limitations of the GIL. By using libraries such as asyncio or Twisted, you can write non-blocking code that allows multiple tasks to run concurrently.

import asyncio

async def my_function():
    # Your code here

if __name__ == '__main__':
    loop = asyncio.get_event_loop()
    tasks = []
    for i in range(10):
        task = asyncio.ensure_future(my_function())


In this solution, we define an asynchronous function and use the asyncio library to create tasks that can run concurrently. The event loop manages the execution of these tasks, allowing them to run in parallel.

Among these three options, the best choice depends on the specific requirements of your application. If your code is CPU-bound and you need true parallelism, solution 1 using multiprocessing is the way to go. If your code is I/O-bound or involves waiting for external resources, solution 2 using threading can be a good option. Finally, if you are working with asynchronous I/O and want to write non-blocking code, solution 3 using asynchronous programming is the most suitable.

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13 Responses

    1. Nah, Solution 1 is outdated and lacks innovation. Its time to move on and embrace new ideas. Solution 3 brings fresh perspectives and could lead to better outcomes. Lets not get stuck in the past, my friend. 😉

    1. I couldnt disagree more. Multiprocessing is the way to go! It offers superior performance and scalability compared to threading. Plus, its a tried and tested approach. Why settle for excitement when you can have efficiency? 💪🚀

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