[Numpy-discussion] Efficiency of Numpy wheels and simple way to benchmark Numpy installation?

[Nathaniel Smith]

Performance is an incredibly multi-dimensional thing. Modern computers are
incredibly complex, with layers of interacting caches, different
microarchitectural features (do you have AVX2? does your cpu's branch
predictor interact in a funny way with your workload?), compiler
optimizations that vary from version to version, ... and different parts of
numpy are affected differently by an these things.

So, the only really reliable answer to a question like this is, always,
that you need to benchmark the application you actually care about in the
contexts where it will actually run (or as close as you can get to that).

That said, as a general rule of thumb, the main difference between
different numpy builds is which BLAS library they use, which primarily
affects the speed of numpy's linear algebra routines. The wheels on pypi
use either OpenBLAS (on Windows and Linux), or Accelerate (in MacOS. The
conda packages provided as part of the Anaconda distribution normally use
Intel's MKL.

All three of these libraries are generally pretty good. They're all serious
attempts to make a blazing fast linear algebra library, and much much
faster than naive implementations. Generally MKL has a reputation for being
somewhat faster than the others, when there's a difference. But again,
whether this happens, or is significant, for *your* app is impossible to
say without trying it.

-n

On Sun, May 27, 2018, 08:32 PIERRE AUGIER <
pierre.augier at univ-grenoble-alpes.fr> wrote:

> Hello,
>
> I don't know if it is a good place to ask such questions. As advised here
> https://www.scipy.org/scipylib/mailing-lists.html#stackoverflow, I first
> posted a question on stackoverflow:
>
>
> https://stackoverflow.com/questions/50475989/efficiency-of-numpy-wheels-and-simple-benchmark-for-numpy-installations
>
> Since I got no feedback, I try here. My questions are:
>
> - When we care about performance, is it a good practice to rely on wheels
> (especially for Numpy)? Will it be slower than using (for example) a conda
> built Numpy?
>
> - Are there simple commands to benchmark Numpy installations and get a
> good idea of their overall performance?
>
> I explain a little bit more in the stackoverflow question...
>
> Pierre Augier
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