CODEWITHSUNDEEP
pythonnumba
HighwayJohn
HighwayJohn

Reputation: 921

Nested Numba function performance

currently I am trying to improve the performance of my python code. To do so I successfully use numba. In order to improve the structure of my code I create functions. Now I have noticed to my surprise that if I split the code into different numba functions, the code is significantly slower than if I put the whole code in one function with a numba decorator. An example would be:

@nb.njit
def fct_4(a, b):
    x = a ^ b
    setBits = 0
    while x > 0:
        setBits += x & 1
        x >>= 1
    return setBits


@nb.njit
def fct_3(c, set_1, set_2):
    h = 2
    if c not in set_1 and c not in set_2:
        if fct_4(0, c) <= h:
            set_1.add(c)
        else:
            set_2.add(c)


@nb.njit
def fct_2(c, set_1, set_2):
    fct_3(c, set_1, set_2)


@nb.njit
def fct_1(set_1, set_2):
    for x1 in range(1000):
        c = 2
        fct_2(c, set_1, set_2)

is slower than

@nb.njit
def fct_1(set_1, set_2):
    for x1 in range(1000):
        c = 2       
        h = 2
        if c not in set_1 and c not in set_2:
            if fct_4(0, c) <= h:
                set_1.add(c)
            else:
                set_2.add(c)

with

@nb.njit
def main_fct(set_1, set_2):
    for i in range(50):
        for x in range(1000):
            fct_1(set_1, set_2)

set_1 = {0}
set_2 = {47}

start = timeit.default_timer()
main_fct(set_1, set_2)
stop = timeit.default_timer()

(2.70 seconds vs 0.46 seconds). I thought this shouldn't make a difference. Could you enlighten me?

Upvotes: 1

Views: 1374

Answers (1)

smitsy
smitsy

Reputation: 81

Since python is a dynamically typed language, its function call overhead is quite high.

On top of that you are looping over the function calls, so the execution time incurred in calling the function and checking the arguments is multiplied 1000 times.

Upvotes: 1

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