title: Numpy dot product very slow using ints
body: <p>sorry for so many questions. I am running Mac OSX 10.6 on Intel core 2 Duo. I am running some benchmarks for my research and I have run into another thing that baffles me. </p>
  
  <p>If I run</p>
  
  <p><code>python -mtimeit -s 'import numpy as np; a = np.random.randn(1e3,1e3)' 'np.dot(a,a)'</code></p>
  
  <p>I get the following output: <code>10 loops, best of 3: 142 msec per loop</code></p>
  
  <p>However, if I run </p>
  
  <p><code>python -mtimeit -s 'import numpy as np; a = np.random.randint(10,size=1e6).reshape(1e3,1e3)' 'np.dot(a,a)'</code></p>
  
  <p>I get the following output: <code>10 loops, best of 3: 7.57 sec per loop</code></p>
  
  <p>Then I ran </p>
  
  <p><code>python -mtimeit -s 'import numpy as np; a = np.random.randn(1e3,1e3)' 'a*a'</code> And then </p>
  
  <p><code>python -mtimeit -s 'import numpy as np; a = np.random.randint(10,size=1e6).reshape(1e3,1e3)' 'a*a'</code> </p>
  
  <p>Both ran at about 7.6 msec per loop so it is not the multiplication. Adding had similar speeds as well, so neither of these should be affecting the dot-product, right?
  So why is it over 50 times slower to calculate the dot product using ints than using floats? </p>