Hi Sylvain,
> On 16. Aug 2018, at 13:29, Sylvain Corlay <sylvain.cor...@gmail.com> wrote:
>
> Actually, xtensor-python does a lot more in terms of numpy bindings, as it
> uses the C APIs of numpy directly for a number of things.
>
> Plus, the integration into the xtensor expression system lets you do things
> such as view / broadcasting / newaxis / ufuncs directly from the C++ side
> (and all that is in the cheat sheets).
ok, good, but my point was different. The page in question is about Python as a
glue language. The other solutions on that site are general purpose binding
solutions for any kind of C++ code, while xtensor-python is xtensor-specific.
xtensor in turn is a library that mimics the numpy API in C++.
> The docs say:
> "Xtensor operations are continuously benchmarked, and are significantly
> improved at each new version. Current performances on statically dimensioned
> tensors match those of the Eigen library. Dynamically dimension tensors for
> which the shape is heap allocated come at a small additional cost."
>
> I couldn't find these benchmark results online, though, could you point me to
> the right page? Google only produced an outdated SO post where numpy
> performed better than xtensor.
>
>
> That is because we run the benchmarks on our own hardware. Since xtensor is
> explicitly SIMD accelerated for a variety of architectures including e.g.
> avx512, it is hard to have a consistent environment to run the benchmarks. We
> have a I9 machine that runs the benchmarks with various options, and manually
> run them on raspberry pis for the neon acceleration benchmarks (continuous
> testing of neon instruction sets are tested with an emulator on travisci in
> the xsimd project).
Ok, but you can still put the results for everyone to see and judge by
themselves on a web page. Just state on what kind of machine you ran the code.
It is ok if the results on my machine differ, I am still interested in the
results that you get on your machines and since you generate them anyway, I
don't see why not.
> [tensor] is clearly a very overloaded term.
I agree that vector is a very overloaded term (the STL vector is particularly
to blame). But until recently, tensor used to be a well-defined technical term
which exclusively referred to a specific mathematical concept
https://en.wikipedia.org/wiki/Tensor_(disambiguation)
https://medium.com/@quantumsteinke/whats-the-difference-between-a-matrix-and-a-tensor-4505fbdc576c
<https://medium.com/@quantumsteinke/whats-the-difference-between-a-matrix-and-a-tensor-4505fbdc576c>
https://www.quora.com/What-is-a-tensor <https://www.quora.com/What-is-a-tensor>
Then Google started to popularise the term wrongly in Tensorflow and now
another well-defined technical term gets watered down. xtensor is going with
the tensorflow, sad.
Best regards,
Hans
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