[Numpy-discussion] Intel random number package
jtaylor.debian at googlemail.com
Wed Oct 26 12:10:36 EDT 2016
On 10/26/2016 06:00 PM, Julian Taylor wrote:
> On 10/26/2016 10:59 AM, Ralf Gommers wrote:
>> On Wed, Oct 26, 2016 at 8:33 PM, Julian Taylor
>> <jtaylor.debian at googlemail.com <mailto:jtaylor.debian at googlemail.com>>
>> On 26.10.2016 06:34, Charles R Harris wrote:
>> > Hi All,
>> > There is a proposed random number package PR now up on github:
>> > https://github.com/numpy/numpy/pull/8209
>> <https://github.com/numpy/numpy/pull/8209>. It is from
>> > oleksandr-pavlyk <https://github.com/oleksandr-pavlyk
>> <https://github.com/oleksandr-pavlyk>> and implements
>> > the number random number package using MKL for increased speed.
>> I think
>> > we are definitely interested in the improved speed, but I'm not
>> > numpy is the best place to put the package. I'd welcome any
>> comments on
>> > the PR itself, as well as any thoughts on the best way organize
>> or use
>> > of this work. Maybe scikit-random
>> Note that this thread is a continuation of
>> I'm not a fan of putting code depending on a proprietary library
>> into numpy.
>> This should be a standalone package which may provide the same
>> as numpy.
>> I don't really see a problem with that in principle. Numpy can use Intel
>> MKL (and Accelerate) as well if it's available. It needs some thought
>> put into the API though - a ``numpy.random_intel`` module is certainly
>> not what we want.
> For me there is a difference between being able to optionally use a
> proprietary library as an alternative to free software libraries if the
> user wishes to do so and offering functionality that only works with
> non-free software.
> We are providing a form of advertisement for them by allowing it (hey if
> you buy this black box that you cannot modify or use freely you get this
> neat numpy feature!).
> I prefer for the full functionality of numpy to stay available with a
> stack of community owned software, even if it may be less powerful that
But then if this is really just the same random numbers numpy already
provides just faster, it is probably acceptable in principle. I haven't
actually looked at the PR yet.
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