With Python, to simulate a genuine parallel-processing (making use of all the cores)

[mnadamod]

With Python, to simulate a genuine parallel-processing (making use of all the cores), who could assist me with a working example code? which I couldn't manage to find out, Thank you!

[0xmicrosoft15]

We have a couple of options for parallel processing in Python:

• multiprocessing module (built-in, best for CPU-bound tasks)
• concurrent.futures.ProcessPoolExecutor (higher-level interface)
• joblib library (great for simple parallel loops)
• dask library (for larger-than-memory computations)

An example of the reliable way to utilize all cores for CPU-bound tasks:
`
import multiprocessing
import time

def cpu_intensive_task(n):
"""Example CPU-bound task (calculates nth Fibonacci number)"""
if n <= 1:
return n
else:
return cpu_intensive_task(n-1) + cpu_intensive_task(n-2)

if name == 'main':
# Get number of available CPU cores
num_cores = multiprocessing.cpu_count()
print(f"Using {num_cores} CPU cores")

# Create list of tasks
tasks = [35, 35, 35, 35, 35, 35, 35, 35]  # 8 tasks (adjust based on your cores)

# Time sequential execution
start = time.time()
results_seq = [cpu_intensive_task(n) for n in tasks]
seq_time = time.time() - start
print(f"Sequential time: {seq_time:.2f} seconds")

# Time parallel execution
start = time.time()
with multiprocessing.Pool(processes=num_cores) as pool:
    results_par = pool.map(cpu_intensive_task, tasks)
par_time = time.time() - start
print(f"Parallel time: {par_time:.2f} seconds")
print(f"Speedup factor: {seq_time/par_time:.2f}x")

# Verify results match
assert results_seq == results_par

`

FYI, CPU-bound vs I/O-bound tasks:
For CPU-bound tasks: Use multiprocessing
For I/O-bound tasks: Use multiprocessing or threading

Global Interpreter Lock (GIL):
Python threads can't truly run in parallel due to GIL
Multiprocessing creates separate Python processes (no GIL limitation)

I can share with you a few useful links and resources if you're further interested in.

[mnadamod]

Thanks again for your great & kind support!

Will reach out to you again soon, as I may have other questions.