title: Intel MKL speeds up numpy.dot, but why doesn't it speed up scipy.linalg.eig?
body: <p>I want to find a fast way to do eigen-decomposition using Numpy/Scipy. I found this post <a href="https://stackoverflow.com/questions/6684238/whats-the-fastest-way-to-find-eigenvalues-vectors-in-python/6688540#6688540">whats the fastest way to find eigenvalues/vectors in python?</a> and followed the advices to rebuild Numpy/Scipy with Intel MKL (in an newly created Python virtual environment).</p>
  
  <p>Then I had a simple performance test on both the old virtual environment with ordinarily built Numpy/Scipy, and the new virtual environment with MKL-built Numpy/Scipy. Specifically I tested numpy.dot and scipy.linalg.eig, hoping the MKL version will be faster.</p>
  
  <p>The results show that MKL-built Numpy is faster than normal Numpy, while MKL-built Scipy is slower than normal Scipy:</p>
  
  <pre><code>Numpy: two (3000 * 3000) matrix dot product
  
  MKL:1s / Normal: 2s
  
  Scipy: eigen-decompose (1000 * 1000) matrix
  
  MKL 1.5s / Normal: 1.5s
  </code></pre>
  
  <p>Why doesn't MKL speed up Scipy? Is there any other way to speed up Scipy's eigen-decomposition?</p>