I am using Joblib to run parallel jobs in my Python application. During profiling, I noticed that the slowest process was {built-in method time.sleep}. Interestingly, this issue disappears when I remove Joblib parallel processing. Could you explain why {built-in method time.sleep} becomes a bottleneck with Joblib parallel processing?

Here is a simplified version of my code:

from joblib import Parallel, delayed

def my_function(x):
    # Some computation
    return x * x

results = Parallel(n_jobs=2)(delayed(my_function)(i) for i in range(10))

Profiling Output

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
    17153  209.317    0.012  209.317    0.012 {built-in method time.sleep}
      588    0.835    0.001    0.835    0.001 {method 'poll' of 'select.poll' objects}