Hi,
Some of our users have asked us about the possibility to add support in
PyTables for extended/quad precision float types, i.e. a 96-bit and
128-bit floats respectively.
I've checked that HDF5 does seem to support 96-bit floats (through
H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at least, I
haven't seem the way). Are there any plans to implement quad precision
support in HDF5 anytime soon?
Thanks,
···
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0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
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A Wednesday 19 December 2007, Francesc Altet escrigué:
Hi,
Some of our users have asked us about the possibility to add support
in PyTables for extended/quad precision float types, i.e. a 96-bit
and 128-bit floats respectively.
I've checked that HDF5 does seem to support 96-bit floats (through
H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at least, I
haven't seem the way). Are there any plans to implement quad
precision support in HDF5 anytime soon?
Ops, some investigation says that H5T_NATIVE_LDOUBLE gives extents of
96-bit in 32-bit platforms and 128-bit in 64-bit ones (at least, in
those that I've tested, an Intel Pentium4 and AMD Opteron). So, I
guess that, in order to map H5T_NATIVE_LDOUBLE to either a 96-bit o
128-bit floats, I should first query the length of it in the platform.
Of course, this kind of floats will not be portable between
platforms :-/
Cheers,
···
--
0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
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Hi Francesc,
A Wednesday 19 December 2007, Francesc Altet escrigué:
Hi,
Some of our users have asked us about the possibility to add support
in PyTables for extended/quad precision float types, i.e. a 96-bit
and 128-bit floats respectively.
I've checked that HDF5 does seem to support 96-bit floats (through
H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at least, I
haven't seem the way). Are there any plans to implement quad
precision support in HDF5 anytime soon?
Ops, some investigation says that H5T_NATIVE_LDOUBLE gives extents of
96-bit in 32-bit platforms and 128-bit in 64-bit ones (at least, in
those that I've tested, an Intel Pentium4 and AMD Opteron). So, I
guess that, in order to map H5T_NATIVE_LDOUBLE to either a 96-bit o
128-bit floats, I should first query the length of it in the platform.
Of course, this kind of floats will not be portable between
platforms :-/
You can also define your own floating-point number format with the H5Tset_fields() API routine. Datatype conversions are slower when this is done, but they will operate correctly.
Quincey
···
On Dec 19, 2007, at 9:08 AM, Francesc Altet wrote:
Cheers,
--
0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
"-"
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A Thursday 20 December 2007, Quincey Koziol escrigué:
Hi Francesc,
> A Wednesday 19 December 2007, Francesc Altet escrigué:
>> Hi,
>>
>> Some of our users have asked us about the possibility to add
>> support in PyTables for extended/quad precision float types, i.e.
>> a 96-bit and 128-bit floats respectively.
>>
>> I've checked that HDF5 does seem to support 96-bit floats (through
>> H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at least, I
>> haven't seem the way). Are there any plans to implement quad
>> precision support in HDF5 anytime soon?
>
> Ops, some investigation says that H5T_NATIVE_LDOUBLE gives extents
> of 96-bit in 32-bit platforms and 128-bit in 64-bit ones (at least,
> in those that I've tested, an Intel Pentium4 and AMD Opteron). So,
> I guess that, in order to map H5T_NATIVE_LDOUBLE to either a 96-bit
> o 128-bit floats, I should first query the length of it in the
> platform. Of course, this kind of floats will not be portable
> between platforms :-/
You can also define your own floating-point number format with the
H5Tset_fields() API routine. Datatype conversions are slower when
this is done, but they will operate correctly.
Very interesting. I wasn't aware that HDF5 had this level of
flexibility to declare user-defined floats. However, it's still open
the issue of which C-type container would be able to keep 128-bit
floats in 32-bit machines, which only have 96-bit floats, but one can
always trim the precision and warn the user about that.
Does HDF5 support the automatic conversion from/to user-defined floats
to/from native floating point for the machine, or one should implement
this support by hand?
Thanks!
···
On Dec 19, 2007, at 9:08 AM, Francesc Altet wrote:
--
0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
"-"
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Hi Francesc,
A Thursday 20 December 2007, Quincey Koziol escrigué:
Hi Francesc,
A Wednesday 19 December 2007, Francesc Altet escrigué:
Hi,
Some of our users have asked us about the possibility to add
support in PyTables for extended/quad precision float types, i.e.
a 96-bit and 128-bit floats respectively.
I've checked that HDF5 does seem to support 96-bit floats (through
H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at least, I
haven't seem the way). Are there any plans to implement quad
precision support in HDF5 anytime soon?
Ops, some investigation says that H5T_NATIVE_LDOUBLE gives extents
of 96-bit in 32-bit platforms and 128-bit in 64-bit ones (at least,
in those that I've tested, an Intel Pentium4 and AMD Opteron). So,
I guess that, in order to map H5T_NATIVE_LDOUBLE to either a 96-bit
o 128-bit floats, I should first query the length of it in the
platform. Of course, this kind of floats will not be portable
between platforms :-/
You can also define your own floating-point number format with the
H5Tset_fields() API routine. Datatype conversions are slower when
this is done, but they will operate correctly.
Very interesting. I wasn't aware that HDF5 had this level of
flexibility to declare user-defined floats. However, it's still open
the issue of which C-type container would be able to keep 128-bit
floats in 32-bit machines, which only have 96-bit floats, but one can
always trim the precision and warn the user about that.
Does HDF5 support the automatic conversion from/to user-defined floats
to/from native floating point for the machine, or one should implement
this support by hand?
HDF5's datatype conversions will automatically convert between any two floating-point formats. It's a _lot_ more efficient for the predefined ones though... 
Quincey
···
On Dec 20, 2007, at 10:03 AM, Francesc Altet wrote:
On Dec 19, 2007, at 9:08 AM, Francesc Altet wrote:
Thanks!
--
0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
"-"
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Hi Quincey,
A Thursday 20 December 2007, escriguéreu:
Hi Francesc,
> A Thursday 20 December 2007, Quincey Koziol escrigué:
>> Hi Francesc,
>>
>>> A Wednesday 19 December 2007, Francesc Altet escrigué:
>>>> Hi,
>>>>
>>>> Some of our users have asked us about the possibility to add
>>>> support in PyTables for extended/quad precision float types,
>>>> i.e. a 96-bit and 128-bit floats respectively.
>>>>
>>>> I've checked that HDF5 does seem to support 96-bit floats
>>>> (through H5T_NATIVE_LDOUBLE symbol), but not 128-bit ones (or at
>>>> least, I haven't seem the way). Are there any plans to implement
>>>> quad precision support in HDF5 anytime soon?
>>>
>>> Ops, some investigation says that H5T_NATIVE_LDOUBLE gives
>>> extents of 96-bit in 32-bit platforms and 128-bit in 64-bit ones
>>> (at least, in those that I've tested, an Intel Pentium4 and AMD
>>> Opteron). So, I guess that, in order to map H5T_NATIVE_LDOUBLE
>>> to either a 96-bit o 128-bit floats, I should first query the
>>> length of it in the platform. Of course, this kind of floats will
>>> not be portable between platforms :-/
>>
>> You can also define your own floating-point number format with
>> the H5Tset_fields() API routine. Datatype conversions are slower
>> when this is done, but they will operate correctly.
>
> Very interesting. I wasn't aware that HDF5 had this level of
> flexibility to declare user-defined floats. However, it's still
> open the issue of which C-type container would be able to keep
> 128-bit floats in 32-bit machines, which only have 96-bit floats,
> but one can always trim the precision and warn the user about that.
>
> Does HDF5 support the automatic conversion from/to user-defined
> floats to/from native floating point for the machine, or one should
> implement this support by hand?
HDF5's datatype conversions will automatically convert between any
two floating-point formats. It's a _lot_ more efficient for the
predefined ones though...
Oh, despite of the slowness, that's excellent news!
···
On Dec 20, 2007, at 10:03 AM, Francesc Altet wrote:
>> On Dec 19, 2007, at 9:08 AM, Francesc Altet wrote:
--
0,0< Francesc Altet http://www.carabos.com/
V V Cárabos Coop. V. Enjoy Data
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