title: numpy on multicore hardware
body: <p>What's the state of the art with regards to getting <code>numpy</code> to use mutliple cores (on Intel hardware) for things like inner and outer vector products, vector-matrix multiplications etc?</p>
  
  <p>I am happy to rebuild <code>numpy</code> if necessary, but at this point I am looking at ways to speed things up without changing my code.</p>
  
  <p>For reference, my <code>show_config()</code> is as follows, and I've never observed <code>numpy</code> to use more than one core:</p>
  
  <pre><code>atlas_threads_info:
      libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
      library_dirs = ['/usr/local/atlas-3.9.16/lib']
      language = f77
      include_dirs = ['/usr/local/atlas-3.9.16/include']
  
  blas_opt_info:
      libraries = ['ptf77blas', 'ptcblas', 'atlas']
      library_dirs = ['/usr/local/atlas-3.9.16/lib']
      define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')]
      language = c
      include_dirs = ['/usr/local/atlas-3.9.16/include']
  
  atlas_blas_threads_info:
      libraries = ['ptf77blas', 'ptcblas', 'atlas']
      library_dirs = ['/usr/local/atlas-3.9.16/lib']
      language = c
      include_dirs = ['/usr/local/atlas-3.9.16/include']
  
  lapack_opt_info:
      libraries = ['lapack', 'ptf77blas', 'ptcblas', 'atlas']
      library_dirs = ['/usr/local/atlas-3.9.16/lib']
      define_macros = [('ATLAS_INFO', '"\\"3.9.16\\""')]
      language = f77
      include_dirs = ['/usr/local/atlas-3.9.16/include']
  
  lapack_mkl_info:
    NOT AVAILABLE
  
  blas_mkl_info:
    NOT AVAILABLE
  
  mkl_info:
    NOT AVAILABLE
  </code></pre>