read-only numpy array in threading python

Question

I have multiple threads that uses numpy array.

import threading
import numpy as np
import time


shared_array = np.ones((5, 5))

def run(shared_array, nb_iters):
   k = shared_array**2
   for i in range(nb_iters):
      k+=2

def multi_thread(): 
    jobs = []
    for _ in range(5):
        thread = threading.Thread(target=run, args=(shared_array, 1000000))
        jobs.append(thread)

    for j in jobs:
        j.start()
    for j in jobs:
        j.join()

t0 = time.time()
multi_thread()
print(time.time() - t0)
#result: 6.502177000045776

t0 = time.time()
# we used 1000000 iterations for each thread => total nb of iterations = 5 * 1000000
run(shared_array, 1000000 * 5) 
print(time.time() - t0)
#result: 6.6372435092926025

the problem is after adding the numpy array as an argument, the execution time of 5 parallel threads is equal to a sequential execution!

so I want to know how to make a program (similar to this one) parallel,

Answer 1

That's a poor example. Python has an internal lock (the global interpreter lock, GIL) that means only one thread at a time can be executing Python code. When you go into numpy, that can run in parallel, but because your array is so small, you are spending almost no time in numpy, so you aren't getting any parallelism to speak of.