title: numpy tensordot and longfloat - speed issues
body: <p>I make intensive use of numpy's tensordot for big tensors. Now, I need to increase the precision of my calculations - that's why I had to replace the 64 bit float by the 96 bit longfloat in my calculations. Unfortunately, this had a tremendous impact on the performance. While I intuitively expected a speed loss of a factor four for this new type, I had to realize that the real speed loss is of a factor 200. It seems that the BLAS library respectively the MKL (I have access to several servers) are not able to handle this type well.</p>
  
  <p>Do you know a way to have fast tensor or alternatively matrix multiplication with high precision? I've looked into Cython, but my simple approaches to obtain similar performances as BLAS and MKL failed. I'm not an expert in C - so maybe there is a suitable library. But as far as I know, they all use BLAS or MKL, as well. Therefore, I expect the some speed loss.</p>
  
  <p>On the other hand, if the speed loss can not be avoided, is there a Cython method which really uses 128 bit and not just 96, as the so-called "float128" of numpy does?</p>