Async function

Is there any help possible with this question, does python need particular code to make full use of the multiple cores that are available?

Hi John,
Interesting question and I was curious to go around the internet and check the possibility of this.
Python threads cannot really take advantage of multiple cores which is by design of the C implementation of python that uses GIL (global interpreter lock).

The work around for this is a multiprocessing module of python.

A simple example:

import time
from threading import Thread
def square_numbers(numbers):
   result = []
   for number in numbers:
       result.append(number ** 2)
   return result
def square_numbers_multithread(numbers, num_threads=2):
   def worker(start, end):
       for i in range(start, end):
           result[i] = numbers[i] ** 2
   
   result = [0] * len(numbers)
   threads = []
   chunk_size = len(numbers) // num_threads
   
   for i in range(num_threads):
       start = i * chunk_size
       end = (i + 1) * chunk_size if i < num_threads - 1 else len(numbers)
       thread = Thread(target=worker, args=(start, end))
       threads.append(thread)
   for thread in threads:
       thread.start()
   for thread in threads:
       thread.join()
   return result
if __name__ == "__main__":
   numbers = list(range(1, 1000001))
   # Single-threaded
   start_time = time.time()
   square_numbers(numbers)
   end_time = time.time()
   print(f"Single-threaded execution time: {end_time - start_time} seconds")
   # Multithreaded
   start_time = time.time()
   square_numbers_multithread(numbers)
   end_time = time.time()
   print(f"Multithreaded execution time: {end_time - start_time} seconds")

Output:

Single-threaded execution time: 0.4774353504180908 seconds
Multithreaded execution time: 0.4126746654510498 seconds