I am using `multiprocessing`

module to parallelize the script but not sure if it is using all the available cores. Is there a straightforward way to find it?

```
import numpy as np
import multiprocessing
import time
def fibonacci(n):
sequence = [0, 1]
while len(sequence) < n:
next_number = sequence[-1] + sequence[-2]
sequence.append(next_number)
return np.array(sequence) # Convert the sequence to a NumPy array
if __name__ == '__main__':
start_time = time.time()
pool = multiprocessing.Pool()
last_result = None
n = 100
for i in range(0, n):
result = fibonacci(i)
#print([result])
U = time.time() - start_time
print("Elapsed time =", U)
```

### >Solution :

The main code to use all cores may look something like:

```
if __name__ == '__main__':
start_time = time.time()
with multiprocessing.Pool() as pool:
results = pool.map(fibonacci, range(0, n))
result = results[-1]
U = time.time() - start_time
print("Elapsed time =", U)
```

Be aware that this is not necessarily faster than the single process. Due to the overhead of multiprocessing it may need some fine tuning (especially by setting a chunk size for `map`

).