I have a function that has a numba @njit wrapper around it to make it faster, I’ve set parallel=True to make it run faster. And now want to measure the time it takes, using time.process_time(), however the time i get is way of.

I have a function that has a numba @njit wrapper around it to make it faster, I’ve set parallel=True to make it run faster. And now want to measure the time it takes, using time.process_time(), however the time i get is way of.

I have a function that has a numba @njit wrapper around it to make it faster, I’ve set parallel=True to make it run faster. And now want to measure the time it takes, using time.process_time(), however the time i get is way of.

import numpy as np from numba import njit @njit(parallel=True) def f(N): x = np.array([1.0], dtype=np.float64) for i in range(N): y = x * 1.0 return y print(f(10000)) Running this code, Python crashes and outputs “Segmentation fault (core dumped)” If “N” is less than 5000, this code can give the correct result “[1.]”. Also If I […]