On Feb 23, 2012, at 6:06 AM, Francesc Alted wrote: > On Feb 23, 2012, at 5:43 AM, Nathaniel Smith wrote: > >> On Thu, Feb 23, 2012 at 11:40 AM, Francesc Alted <[email protected]> >> wrote: >>> Exactly. I'd update this to read: >>> >>> float96 96 bits. Only available on 32-bit (i386) platforms. >>> float128 128 bits. Only available on 64-bit (AMD64) platforms. >> >> Except float96 is actually 80 bits. (Usually?) Plus some padding… > > Good point. The thing is that they actually use 96 bit for storage purposes > (this is due to alignment requirements). > > Another quirk related with this is that MSVC automatically maps long double > to 64-bit doubles: > > http://msdn.microsoft.com/en-us/library/9cx8xs15.aspx > > Not sure on why they did that (portability issues?).
Hmm, yet another quirk (this time in NumPy itself). On 32-bit platforms: In [16]: np.longdouble Out[16]: numpy.float96 In [17]: np.finfo(np.longdouble).eps Out[17]: 1.084202172485504434e-19 while on 64-bit ones: In [8]: np.longdouble Out[8]: numpy.float128 In [9]: np.finfo(np.longdouble).eps Out[9]: 1.084202172485504434e-19 i.e. NumPy is saying that the eps (machine epsilon) is the same on both platforms, despite the fact that one uses 80-bit precision and the other 128-bit precision. For the 80-bit, the eps should be (): In [5]: 1 / 2**63. Out[5]: 1.0842021724855044e-19 [http://en.wikipedia.org/wiki/Extended_precision] which is correctly stated by NumPy, while for 128-bit (quad precision), eps should be: In [6]: 1 / 2**113. Out[6]: 9.62964972193618e-35 [http://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format] If nobody objects, I'll file a bug about this. -- Francesc Alted _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
