this is from:
http://gcc.gnu.org/onlinedocs/gcc/X86-Built-in-Functions.html
// ifunc resolvers fire before constructors, explicitly call the
init function.
__builtin_cpu_init ();
if (__builtin_cpu_supports ("ssse2"))
<code>
else
<code>
Cheers,
Carl
2014-05-09 13:06 GMT+02:00 David Cournapeau <[email protected]>:
>
>
>
> On Fri, May 9, 2014 at 11:49 AM, Julian Taylor <
> [email protected]> wrote:
>
>> On 09.05.2014 12:42, David Cournapeau wrote:
>> >
>> >
>> >
>> > On Fri, May 9, 2014 at 1:51 AM, Matthew Brett <[email protected]
>> > <mailto:[email protected]>> wrote:
>> >
>> > Hi,
>> >
>> > On Mon, Apr 28, 2014 at 3:29 PM, David Cournapeau
>> > <[email protected] <mailto:[email protected]>> wrote:
>> > >
>> > >
>> > >
>> > > On Sun, Apr 27, 2014 at 11:50 PM, Matthew Brett
>> > <[email protected] <mailto:[email protected]>>
>> > > wrote:
>> > >>
>> > >> Aha,
>> > >>
>> > >> On Sun, Apr 27, 2014 at 3:19 PM, Matthew Brett
>> > <[email protected] <mailto:[email protected]>>
>> > >> wrote:
>> > >> > Hi,
>> > >> >
>> > >> > On Sun, Apr 27, 2014 at 3:06 PM, Carl Kleffner
>> > <[email protected] <mailto:[email protected]>>
>> > >> > wrote:
>> > >> >> A possible option is to install the toolchain inside
>> > site-packages and
>> > >> >> to
>> > >> >> deploy it as PYPI wheel or wininst packages. The PATH to the
>> > toolchain
>> > >> >> could
>> > >> >> be extended during import of the package. But I have no idea,
>> > whats the
>> > >> >> best
>> > >> >> strategy to additionaly install ATLAS or other third party
>> > libraries.
>> > >> >
>> > >> > Maybe we could provide ATLAS binaries for 32 / 64 bit as part
>> > of the
>> > >> > devkit package. It sounds like OpenBLAS will be much easier to
>> > build,
>> > >> > so we could start with ATLAS binaries as a default, expecting
>> > OpenBLAS
>> > >> > to be built more often with the toolchain. I think that's how
>> > numpy
>> > >> > binary installers are built at the moment - using old binary
>> > builds of
>> > >> > ATLAS.
>> > >> >
>> > >> > I'm happy to provide the builds of ATLAS - e.g. here:
>> > >> >
>> > >> > https://nipy.bic.berkeley.edu/scipy_installers/atlas_builds
>> > >>
>> > >> I just found the official numpy binary builds of ATLAS:
>> > >>
>> > >> https://github.com/numpy/vendor/tree/master/binaries
>> > >>
>> > >> But - they are from an old version of ATLAS / Lapack, and only
>> > for 32-bit.
>> > >>
>> > >> David - what say we update these to latest ATLAS stable?
>> > >
>> > >
>> > > Fine by me (not that you need my approval !).
>> > >
>> > > How easy is it to build ATLAS targetting a specific CPU these days
>> > ? I think
>> > > we need to at least support nosse and sse2 and above.
>> >
>> > I'm getting crashes trying to build SSE2-only ATLAS on 32-bits, I
>> > think Clint will have some time to help out next week.
>> >
>> > I did some analysis of SSE2 prevalence here:
>> >
>> > https://github.com/numpy/numpy/wiki/Window-versions
>> >
>> > Firefox crash reports now have about 1 percent of machines without
>> > SSE2. I suspect that people running new installs of numpy will have
>> > slightly better machines on average than Firefox users, but it's
>> only
>> > a guess.
>> >
>> > I wonder if we could add a CPU check on numpy import to give a
>> polite
>> > 'install from the exe' message for people without SSE2.
>> >
>> >
>> > We could, although you unfortunately can't do it easily from ctypes only
>> > (as you need some ASM).
>> >
>> > I can take a quick look at a simple cython extension that could be
>> > imported before anything else, and would raise an ImportError if the
>> > wrong arch is detected.
>> >
>>
>> assuming mingw is new enough
>>
>> #ifdef __SSE2___
>> raise_if(!__builtin_cpu_supports("sse"))
>> #endof
>>
>
> We need to support it for VS as well, but it looks like win32 API has a
> function to do it:
> http://msdn.microsoft.com/en-us/library/ms724482%28VS.85%29.aspx
>
> Makes it even easier.
>
> David
>
>>
>> in import_array() should do it
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>>
>
>
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