On 09.05.2014 12:42, David Cournapeau wrote:
On Fri, May 9, 2014 at 1:51 AM, Matthew Brett <matthew.brett@gmail.com <mailto:matthew.brett@gmail.com>> wrote:
Hi,
On Mon, Apr 28, 2014 at 3:29 PM, David Cournapeau <cournape@gmail.com <mailto:cournape@gmail.com>> wrote: > > > > On Sun, Apr 27, 2014 at 11:50 PM, Matthew Brett <matthew.brett@gmail.com <mailto:matthew.brett@gmail.com>> > wrote: >> >> Aha, >> >> On Sun, Apr 27, 2014 at 3:19 PM, Matthew Brett <matthew.brett@gmail.com <mailto:matthew.brett@gmail.com>> >> wrote: >> > Hi, >> > >> > On Sun, Apr 27, 2014 at 3:06 PM, Carl Kleffner <cmkleffner@gmail.com <mailto:cmkleffner@gmail.com>> >> > 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 in import_array() should do it