title: Julia matrix multiplication is slower than numpy's
body: <p>I am trying to do some matrix multiplication in Julia to benchmark it against numpy's.</p>
  
  <p>My Julia code is the following:</p>
  
  <pre><code>function myFunc()
    A = randn(10000, 10000)
    B = randn(10000, 10000)
    return A*B
  end
  
  myFunc()
  </code></pre>
  
  <p>And the python version is:</p>
  
  <pre><code>A = np.random.rand(10000,10000)
  B = np.random.rand(10000,10000)
  A*B
  </code></pre>
  
  <p>The Python version takes under 100ms to execute. The Julia version takes over 13s!! Seeing as they are using pretty much the same BLAS technololgy under the hood, what seems to be the problem with the Julia version?!</p>