On Thu, Jul 24, 2014 at 8:27 AM, Jaime Fernández del Río < jaime.f...@gmail.com> wrote:
> On Thu, Jul 24, 2014 at 4:56 AM, Julian Taylor < > jtaylor.deb...@googlemail.com> wrote: > >> In practice one of the better methods is pairwise summation that is >> pretty much as fast as a naive summation but has an accuracy of >> O(logN) ulp. >> This is the method numpy 1.9 will use this method by default (+ its >> even a bit faster than our old implementation of the naive sum): >> https://github.com/numpy/numpy/pull/3685 >> >> but it has some limitations, it is limited to blocks fo the buffer >> size (8192 elements by default) and does not work along the slow axes >> due to limitations in the numpy iterator. >> > > For what it's worth, I see the issue on a 64-bit Windows numpy 1.8, but > cannot on a 32-bit Windows numpy master: > > >>> np.__version__ > '1.8.0' > >>> np.ones(100000000, dtype=np.float32).mean() > 0.16777216 > > >>> np.__version__ > '1.10.0.dev-Unknown' > >>> np.ones(100000000, dtype=np.float32).mean() > 1.0 > > Interesting. Might be compiler related as there are many choices for floating point instructions/registers in i386. The i386 version may effectively be working in double precision. Chuck
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