On Sun, Oct 16, 2011 at 8:04 AM, Matthew Brett <[email protected]> wrote: > Hi, > > On Sat, Oct 15, 2011 at 11:04 PM, Nadav Horesh <[email protected]> wrote: >> On 32 bit systems it consumes 96 bits (3 x 32). and hence float96 >> On 64 bit machines it consumes 128 bits (2x64). >> The variable size is set for an efficient addressing, while the calculation >> in hardware is carried in the 80 bits FPU (x87) registers. > > Right - but the problem here is that it is very confusing. There is > something called binary128 in the IEEE standard, and what numpy has is > not that. float16, float32 and float64 are all IEEE standards called > binary16, binary32 and binary64.
This one is easy: few CPU support the 128 bits float specified in IEEE standard (the only ones I know are the expensive IBM ones). Then there are the cases where it is implemented in software (SPARC use the double-pair IIRC). So you would need binar80, binary96, binary128, binary128_double_pair, etc... That would be a nightmare to support, and also not portable: what does binary80 become on ppc ? What does binary96 become on 32 bits Intel ? Or on windows (where long double is the same as double for visual studio) ? binary128 should only be thought as a (bad) synonym to np.longdouble. David _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
