numpy.random.normal() in multithreaded environment

Question

I was trying to parallelize the generation of normally-distributed random arrays with the numpy.random.normal() function but it looks like the calls from the threads are executed sequentially instead.

import numpy as np

start = time.time()

active_threads = 0
for i in range(100000):
    t = threading.Thread(target=lambda x : np.random.normal(0,2,4000), args = [0])
    t.start()

    while active_threads >= 12:
        time.sleep(0.1)
        continue

end = time.time()
print(str(end-start))

If i measure the time for a 1 thread process i get the same result as the 12 thread one. I know this kind of parallelization suffers from a lot of overhead, but even then it should still get some time off with the multithread version.