On Wed, Jan 18, 2017 at 11:43 AM, Julian Taylor < jtaylor.deb...@googlemail.com> wrote:
> The version of gcc used will make a large difference in some places. > E.g. the AVX2 integer ufuncs require something around 4.5 to work and in > general the optimization level of gcc has improved greatly since the > clang competition showed up around that time. centos 5 has 4.1 which is > really ancient. > I though the wheels used newer gccs also on centos 5? > I don't know if it is mandatory for many wheels, but it is possilbe to build w/ gcc 4.8 at least, and still binary compatibility with centos 5.X and above, though I am not sure about the impact on speed. It has been quite some time already that building numpy/scipy with gcc 4.1 causes troubles with errors and even crashes anyway, so you definitely want to use a more recent compiler in any case. David > On 18.01.2017 08:27, Nathan Goldbaum wrote: > > I've seen reports on the anaconda mailing list of people seeing similar > > speed ups when they compile e.g. Numpy with a recent gcc. Anaconda has > > the same issue as manylinux in that they need to use versions of GCC > > available on CentOS 5. > > > > Given the upcoming official EOL for CentOS5, it might make sense to > > think about making a pep for a CentOS 6-based manylinux2 docker image, > > which will allow compiling with a newer GCC. > > > > On Tue, Jan 17, 2017 at 9:15 PM Jerome Kieffer <jerome.kief...@esrf.fr > > <mailto:jerome.kief...@esrf.fr>> wrote: > > > > On Tue, 17 Jan 2017 08:56:42 -0500 > > > > Neal Becker <ndbeck...@gmail.com <mailto:ndbeck...@gmail.com>> > wrote: > > > > > > > > > I've installed via pip3 on linux x86_64, which gives me a wheel. > My > > > > > question is, am I loosing significant performance choosing this > > pre-built > > > > > binary vs. compiling myself? For example, my processor might have > > some more > > > > > features than the base version used to build wheels. > > > > > > > > Hi, > > > > > > > > I have done some benchmarking (%timeit) for my code running in a > > > > jupyter-notebook within a venv installed with pip+manylinux wheels > > > > versus ipython and debian packages (on the same computer). > > > > I noticed the debian installation was ~20% faster. > > > > > > > > I did not investigate further if those 20% came from the manylinux (I > > > > suspect) or from the notebook infrastructure. > > > > > > > > HTH, > > > > -- > > > > Jérôme Kieffer > > > > > > > > _______________________________________________ > > > > NumPy-Discussion mailing list > > > > NumPy-Discussion@scipy.org <mailto:NumPy-Discussion@scipy.org> > > > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion >
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