[Numpy-discussion] float128 in fact float80

Matthew Brett matthew.brett@gmail....
Sun Oct 16 02:33:22 CDT 2011


Hi,

On Sun, Oct 16, 2011 at 12:28 AM, David Cournapeau <cournape@gmail.com> wrote:
> On Sun, Oct 16, 2011 at 8:04 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
>> Hi,
>>
>> On Sat, Oct 15, 2011 at 11:04 PM, Nadav Horesh <nadavh@visionsense.com> 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.

What would be the nightmare to support - the different names for the
different precisions?

How many do we support in fact?   Apart from float80?

Is there some reason the support burden is less by naming lots of
different precisions the same?

See you,

Matthew


More information about the NumPy-Discussion mailing list