title: How to make numba @jit use all cpu cores (parallelize numba @jit)
body: <p>I am using numbas <code>@jit</code> decorator for adding two numpy arrays in python. The performance is so high if I use <code>@jit</code> compared with <code>python</code>. </p>
  
  <p>However it is <strong>not utilizing all CPU cores</strong> even if I pass in <code>@numba.jit(nopython = True, parallel = True, nogil = True)</code>. </p>
  
  <p>Is there any way to to make use of all CPU cores with numba <code>@jit</code>.</p>
  
  <p>Here is my code:</p>
  
  <pre><code>import time                                                
  import numpy as np                                         
  import numba                                               
  
  SIZE = 2147483648 * 6                                      
  
  a = np.full(SIZE, 1, dtype = np.int32)                     
  
  b = np.full(SIZE, 1, dtype = np.int32)                     
  
  c = np.ndarray(SIZE, dtype = np.int32)                     
  
  @numba.jit(nopython = True, parallel = True, nogil = True) 
  def add(a, b, c):                                          
      for i in range(SIZE):                                  
          c[i] = a[i] + b[i]                                 
  
  start = time.time()                                        
  add(a, b, c)                                               
  end = time.time()                                          
  
  print(end - start)                                        
  </code></pre>