On Feb 29, 2012, at 11:52 AM, Pierre Haessig wrote:
Hi,
Le 29/02/2012 16:22, Paweł Biernat a écrit :
Is there any way to interact with Fortran's real(16) (supported by gcc and Intel's ifort) data type from numpy? By real(16) I mean the binary128 type as in IEEE 754. (In C this data type is experimentally supported as __float128 (gcc) and _Quad (Intel's icc).) I googled a bit this "__float128". It seems a fairly new addition (GCC 4.6, released March 2011). The related point in the changelog [1] is :
"GCC now ships with the LGPL-licensed libquadmath library, which provides quad-precision mathematical functions for targets with a __float128 datatype. __float128 is available for targets on 32-bit x86, x86-64 and Itanium architectures. The libquadmath library is automatically built on such targets when building the Fortran compiler."
Great find!
It seems this __float128 is newcomer in the "picture of data types" that Matthew just mentioned. As David says, arithmetic with such a 128 bits data type is probably not "hardwired" in most processors (I mean Intel & friends) which are limited to 80 bits ("long doubles") so it may be a bit slow. However, this GCC implementation with libquadmath seems to create some level of abstraction. Maybe this is one acceptably good way for a real "IEEE float 128" dtype in numpy ?
That would be really nice. The problem here is two-folded: * Backwards-compatibility. float128 should represent a different data-type than before, so we probably should find a new name (and charcode!) for quad-precision. Maybe quad128? * Compiler-dependency. The new type will be only available on platforms that has GCC 4.6 or above. Again, using the new name for this should be fine. On platforms/compilers not supporting the quad128 thing, it should not be defined. Uh, I foresee many portability problems for people using this, but perhaps it is worth the mess. -- Francesc Alted