numba @jit slower that pure python?

Question

so i need to improve the execution time for a script that i have been working on. I started working with numba jit decorator to try parallel computing however it throws me

KeyError: "Does not support option: 'parallel'"

so i decided to test the nogil if it unlocks the whole capabilities from my cpu but it was slower than pure python i dont understand why this happened, and if someone can help me or guide me i will be very grateful

import numpy as np
from numba import *
@jit(['float64[:,:],float64[:,:]'],'(n,m),(n,m)->(n,m)',nogil=True)
def asd(x,y):
    return x+y
u=np.random.random(100)
w=np.random.random(100)

%timeit asd(u,w)
%timeit u+w

10000 loops, best of 3: 137 µs per loop The slowest run took 7.13 times longer than the fastest. This could mean that an intermediate result is being cached 1000000 loops, best of 3: 1.75 µs per loop

numba @jit slower that pure python?

1 Answers