This sounds terrific. Is there a technique to get such a performance
improvement with Accelerate?
We tried:
pip install numpy==1.24.3 --no-binary numpy
with and without a ~/.numpy-site.cfg file that sets
[accelerate]
libraries = Accelerate, vecLib
on Intel and M2 Macs on macOS 13.3. While np.show_config() reports that
numpy is linked to Accelerate, a large dot product runs maybe twice as fast
as with OpenBLAS, and our full bio simulation's performance is within
measurement noise.
Trying the --no-use-pep517 argument had no obvious impact.
Best,
Jerry
On Thu, Mar 23, 2023 at 3:52 AM Ralf Gommers <ralf.gomm...@gmail.com> wrote:
>
>
> On Thu, Mar 23, 2023 at 10:43 AM Clemens Brunner <
> clemens.brun...@gmail.com> wrote:
>
>> Thanks Ralf, this sounds great! Just making sure I understand, this means
>> that for macOS 13, we have to enable Accelerate by building NumPy from
>> source.
>
>
> Indeed. Either that, or use a packaging system that's more capable in this
> regard - conda-forge for example will give you runtime switching of BLAS
> and LAPACK libraries out of the box (
> https://conda-forge.org/docs/maintainer/knowledge_base.html#switching-blas-implementation).
> Several Linux distros support this too, via mechanisms like
> `update-alternatives` - but that won't help you much on macOS of course.
>
> Once macOS 13.3 is out, building SciPy from source will also link to
>> Accelerate. Finally, Accelerate support will be enabled by default in
>> binary wheels for NumPy and SciPy once macOS 14 is out (presumably some
>> time in October this year). Correct?
>>
>
> Yes, if I had to guess now then I'd say that this will be the default in
> NumPy 2.0 at the end of the year.
>
> Cheers,
> Ralf
>
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