下面的代码为数组的每一行创建一个线程，但不确定它的效率如何。

import numpy as np
import threading

def thread_function(index, array):
  # aforementioned time-consuming calculation, resulting in 'a'
  a = np.ones(10)    # placeholder for calculation

  array[index, :] = a

if __name__ == "__main__":
  A = np.zeros((10000, 10))
  threads = []

  for i in range(10000):
    threads.append(threading.Thread(target=thread_function, args=(i, A)))
    threads[i].start()

  for i in range(10000):
    threads[i].join()

  print(A)

https://repl.it/@RobertClarke64/Python-Multithreading
实际上，它不是特别快，但当计算需要很长时间时，希望比连续运行每个计算要快得多。

为什么不利用GPU的并行能力来实现：

import numpy as np
from numba import vectorize

@vectorize(["float32(float32, float32)"], target='cuda')
def f(a,b):

  for i in range(10000):
    a=4*b
  return a

a = np.ones((10000,10), dtype=np.float32)
b = np.random.rand(10000,10).astype('f')

f(a,b)