[Numpy-discussion] Odd-looking long double on windows 32 bit
David Cournapeau
cournape at gmail.com
Tue Nov 15 01:08:07 EST 2011
On Mon, Nov 14, 2011 at 9:01 PM, Matthew Brett <matthew.brett at gmail.com> wrote:
> Hi,
>
> On Sun, Nov 13, 2011 at 5:03 PM, Charles R Harris
> <charlesr.harris at gmail.com> wrote:
>>
>>
>> On Sun, Nov 13, 2011 at 3:56 PM, Matthew Brett <matthew.brett at gmail.com>
>> wrote:
>>>
>>> Hi,
>>>
>>> On Sun, Nov 13, 2011 at 1:34 PM, Charles R Harris
>>> <charlesr.harris at gmail.com> wrote:
>>> >
>>> >
>>> > On Sun, Nov 13, 2011 at 2:25 PM, Matthew Brett <matthew.brett at gmail.com>
>>> > wrote:
>>> >>
>>> >> Hi,
>>> >>
>>> >> On Sun, Nov 13, 2011 at 8:21 AM, Charles R Harris
>>> >> <charlesr.harris at gmail.com> wrote:
>>> >> >
>>> >> >
>>> >> > On Sun, Nov 13, 2011 at 12:57 AM, Matthew Brett
>>> >> > <matthew.brett at gmail.com>
>>> >> > wrote:
>>> >> >>
>>> >> >> Hi,
>>> >> >>
>>> >> >> On Sat, Nov 12, 2011 at 11:35 PM, Matthew Brett
>>> >> >> <matthew.brett at gmail.com>
>>> >> >> wrote:
>>> >> >> > Hi,
>>> >> >> >
>>> >> >> > Sorry for my continued confusion here. This is numpy 1.6.1 on
>>> >> >> > windows
>>> >> >> > XP 32 bit.
>>> >> >> >
>>> >> >> > In [2]: np.finfo(np.float96).nmant
>>> >> >> > Out[2]: 52
>>> >> >> >
>>> >> >> > In [3]: np.finfo(np.float96).nexp
>>> >> >> > Out[3]: 15
>>> >> >> >
>>> >> >> > In [4]: np.finfo(np.float64).nmant
>>> >> >> > Out[4]: 52
>>> >> >> >
>>> >> >> > In [5]: np.finfo(np.float64).nexp
>>> >> >> > Out[5]: 11
>>> >> >> >
>>> >> >> > If there are 52 bits of precision, 2**53+1 should not be
>>> >> >> > representable, and sure enough:
>>> >> >> >
>>> >> >> > In [6]: np.float96(2**53)+1
>>> >> >> > Out[6]: 9007199254740992.0
>>> >> >> >
>>> >> >> > In [7]: np.float64(2**53)+1
>>> >> >> > Out[7]: 9007199254740992.0
>>> >> >> >
>>> >> >> > If the nexp is right, the max should be higher for the float96
>>> >> >> > type:
>>> >> >> >
>>> >> >> > In [9]: np.finfo(np.float64).max
>>> >> >> > Out[9]: 1.7976931348623157e+308
>>> >> >> >
>>> >> >> > In [10]: np.finfo(np.float96).max
>>> >> >> > Out[10]: 1.#INF
>>> >> >> >
>>> >> >> > I see that long double in C is 12 bytes wide, and double is the
>>> >> >> > usual
>>> >> >> > 8
>>> >> >> > bytes.
>>> >> >>
>>> >> >> Sorry - sizeof(long double) is 12 using mingw. I see that long
>>> >> >> double
>>> >> >> is the same as double in MS Visual C++.
>>> >> >>
>>> >> >> http://en.wikipedia.org/wiki/Long_double
>>> >> >>
>>> >> >> but, as expected from the name:
>>> >> >>
>>> >> >> In [11]: np.dtype(np.float96).itemsize
>>> >> >> Out[11]: 12
>>> >> >>
>>> >> >
>>> >> > Hmm, good point. There should not be a float96 on Windows using the
>>> >> > MSVC
>>> >> > compiler, and the longdouble types 'gG' should return float64 and
>>> >> > complex128
>>> >> > respectively. OTOH, I believe the mingw compiler has real float96
>>> >> > types
>>> >> > but
>>> >> > I wonder about library support. This is really a build issue and it
>>> >> > would be
>>> >> > good to have some feedback on what different platforms are doing so
>>> >> > that
>>> >> > we
>>> >> > know if we are doing things right.
>>> >>
>>> >> Is it possible that numpy is getting confused by being compiled with
>>> >> mingw on top of a visual studio python?
>>> >>
>>> >> Some further forensics seem to suggest that, despite the fact the math
>>> >> suggests float96 is float64, the storage format it in fact 80-bit
>>> >> extended precision:
>>> >>
>>> >
>>> > Yes, extended precision is the type on Intel hardware with gcc, the
>>> > 96/128
>>> > bits comes from alignment on 4 or 8 byte boundaries. With MSVC, double
>>> > and
>>> > long double are both ieee double, and on SPARC, long double is ieee quad
>>> > precision.
>>>
>>> Right - but I think my researches are showing that the longdouble
>>> numbers are being _stored_ as 80 bit, but the math on those numbers is
>>> 64 bit.
>>>
>>> Is there a reason than numpy can't do 80-bit math on these guys? If
>>> there is, is there any point in having a float96 on windows?
>>
>> It's a compiler/architecture thing and depends on how the compiler
>> interprets the long double c type. The gcc compiler does do 80 bit math on
>> Intel/AMD hardware. MSVC doesn't, and probably never will. MSVC shouldn't
>> produce float96 numbers, if it does, it is a bug. Mingw uses the gcc
>> compiler, so the numbers are there, but if it uses the MS library it will
>> have to convert them to double to do computations like sin(x) since there
>> are no microsoft routines for extended precision. I suspect that gcc/ms
>> combo is what is producing the odd results you are seeing.
>
> I think we might be talking past each other a bit.
>
> It seems to me that, if float96 must use float64 math, then it should
> be removed from the numpy namespace, because
If we were to do so, it would break too much code.
>
> a) It implies higher precision than float64 but does not provide it
> b) It uses more memory to no obvious advantage
There is an obvious advantage: to handle memory blocks which use long
double, created outside numpy (or even python).
Otherwise, while gcc indeed supports long double, the fact that the C
runtime doesn't really mean it is hopeless to reach any kind of
consistency. And I will reiterate what I said before about long
double: if you care about your code behaving consistency across
platforms, just forget about long double.
cheers,
David
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