Joblib Simple Example Parallel Example Slower Than Simple

from math import sqrt from joblib import Parallel, delayed import time if __name__ == '__main__': st= time.time() #[sqrt(i ** 2) for i in range(100000)] #this part in no

Solution 1:

Parallel processes (which joblib creates) require copying data. Imagine it this way: you have two people who each carry a rock to their house, shine it, then bring it back. That's loads slower than one person shining them on-the-spot.

All the time is wasted in transit, rather than being spent on the actual calculation. You will only benefit from parallel processes for more substantial computational tasks.

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If you care about speeding up this specific operation: Use numpy's vectorized math operations. On my machine, parallel: 1.13 s, serial: 54.6 ms, numpy: 3.74 ms.

    a = np.arange(100000, dtype=np.int)
    np.sqrt(a ** 2)

Don't worry about libraries like Cython or Numba; they won't speed up this already performant operation.