2009/10/21 Neal Becker <[email protected]> > ... > > I once wrote a module that replaces the built in transcendental > > functions of numpy by optimized versions from Intels vector math > > library. If someone is interested, I can publish it. In my experience it > > was of little use since real world problems are limited by memory > > bandwidth. Therefore extending numexpr with optimized transcendental > > functions was the better solution. Afterwards I discovered that I could > > have saved the effort of the first approach since gcc is able to use > > optimized functions from Intels vector math library or AMD's math core > > library, see the doc's of -mveclibabi. You just need to recompile numpy > > with proper compiler arguments. > > > > I'm interested. I'd like to try AMD rather than intel, because AMD is > easier to obtain. I'm running on intel machine, I hope that doesn't matter > too much. > > What exactly do I need to do? > I once tried to recompile numpy with AMD's AMCL. Unfortunately I lost the settings after an upgrade. What I remember: install AMCL, (and read the docs ;-) ), mess with the compiler args (-mveclibabi and related), link with the AMCL. Then you get faster pow/sin/cos/exp. The transcendental functions of AMCL also work with Intel processors with the same performance. I did not try the Intel SVML, which belongs to the Intel compilers. This is different to the first approach, which is a small wrapper for Intels VML, put into a python module and which can inject it's ufuncs (via numpy.set_numeric_ops) into numpy. If you want I can send the package per private email.
> I see that numpy/site.cfg has an MKL section. I'm assuming I should not > touch that, but just mess with gcc flags? > This is for using the lapack provided by Intels MKL. These settings are not related to the above mentioned compiler options. > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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