title: Numpy octuple precision floats and 128 bit ints. Why and how?
body: <p>This is mostly a question out of curiosity.  I noticed that the numpy test suite <a href="https://github.com/numpy/numpy/blob/master/numpy/core/tests/test_extint128.py" rel="noreferrer">contains tests for 128 bit integers</a>, and the <a href="https://github.com/numpy/numpy/blob/master/numpy/core/numerictypes.py" rel="noreferrer"><code>numerictypes</code> module</a> refers to <code>int128</code>, <code>float256</code> (<a href="https://en.wikipedia.org/wiki/Octuple-precision_floating-point_format" rel="noreferrer">octuple precision</a>?), and other types that don't seem to map to numpy dtypes on my machine.</p>
  
  <p>My machine is 64bit, yet I can use quadruple 128bit floats (<a href="https://stackoverflow.com/q/9062562/1461210">but not really</a>).  I suppose that if it's possible to emulate quadruple floats in software, one can theoretically also emulate octuple floats and 128bit ints.  On the other hand, until just now I had never heard of either 128bit ints or octuple precision floating point before.  Why is there a reference to 128bit ints and 256bit floats in numpy's <code>numerictypes</code> module if there are no corresponding <code>dtype</code>s, and how can I use those?</p>