Joblib simple example parallel example slower than simple

问题

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 non parellel
    Parallel(n_jobs=2)(delayed(sqrt)(i ** 2) for i in range(100000))
    print time.time()-st

now the non parelle part runs in 0.4 sec while parallel part runs for 18 sec .. I am confused why would this happen

回答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.

---

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.

来源：https://stackoverflow.com/questions/44084513/joblib-simple-example-parallel-example-slower-than-simple