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")
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Output:

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