why is Numba parallel is slower than normal python loop?

Following is normal python loop (I copied example from official doc - https://numba.readthedocs.io/en/stable/user/parallel.html)

def two_d_array_reduction_prod(n):
    shp = (13, 17)
    result1 = 2 * np.ones(shp, np.int_)
    tmp = 2 * np.ones_like(result1)

    for i in range(n):
        result1 *= tmp

    return result1

I called function like:

two_d_array_reduction_prod(50000)

It takes around 0.7482060070033185.

Numba parallel code

@nb.njit(parallel=True)
def two_d_array_reduction_prod(n):
    shp = (13, 17)
    result1 = 2 * np.ones(shp, np.int_)
    tmp = 2 * np.ones_like(result1)

    for i in nb.prange(n):
        result1 *= tmp

    return result1

I called function like:

two_d_array_reduction_prod(50000)

It takes 3.9858204890042543

My environment:

1. Amazon Linux 2, x86_64 processor
2. 8 CPUs
3. 32G memory

I can't replicate this. Using parallel=True gives a slight performance improvement, but any method is significantly faster compared to pure Python for me.

Using:

from numba import njit, prange
import numpy as np

def two_d_array_reduction_prod(n):
    shp = (13, 17)
    result1 = 2 * np.ones(shp, np.int_)
    tmp = 2 * np.ones_like(result1)

    for i in prange(n): # or for i in range(n):
        result1 *= tmp

    return result1

two_d_array_reduction_prod_numba = nb.njit(parallel=False)(two_d_array_reduction_prod)

Even with parallel=False with prange or using parallel=False with range I get over 3x improvement. All these timings are done with a warm-up, pre-compiling the Numba function first.