[SciPy-Dev] [Numpy-discussion] NumPy 1.12.0 release

[Matthew Brett]

On Tue, Jan 17, 2017 at 3:47 PM, Neal Becker <ndbecker2 at gmail.com> wrote:
> Matthew Brett wrote:
>
>> Hi,
>>
>> On Tue, Jan 17, 2017 at 5:56 AM, Neal Becker <ndbecker2 at gmail.com> wrote:
>>> Charles R Harris wrote:
>>>
>>>> Hi All,
>>>>
>>>> I'm pleased to announce the NumPy 1.12.0 release. This release supports
>>>> Python 2.7 and 3.4-3.6. Wheels for all supported Python versions may be
>>>> downloaded from PiPY
>>>> <https://pypi.python.org/pypi?%3Aaction=pkg_edit&name=numpy>, the
>>>> tarball and zip files may be downloaded from Github
>>>> <https://github.com/numpy/numpy/releases/tag/v1.12.0>. The release notes
>>>> and files hashes may also be found at Github
>>>> <https://github.com/numpy/numpy/releases/tag/v1.12.0> .
>>>>
>>>> NumPy 1.12.0rc 2 is the result of 418 pull requests submitted by 139
>>>> contributors and comprises a large number of fixes and improvements.
>>>> Among
>>>> the many improvements it is difficult to  pick out just a few as
>>>> standing above the others, but the following may be of particular
>>>> interest or indicate areas likely to have future consequences.
>>>>
>>>> * Order of operations in ``np.einsum`` can now be optimized for large
>>>> speed improvements.
>>>> * New ``signature`` argument to ``np.vectorize`` for vectorizing with
>>>> core dimensions.
>>>> * The ``keepdims`` argument was added to many functions.
>>>> * New context manager for testing warnings
>>>> * Support for BLIS in numpy.distutils
>>>> * Much improved support for PyPy (not yet finished)
>>>>
>>>> Enjoy,
>>>>
>>>> Chuck
>>>
>>> I've installed via pip3 on linux x86_64, which gives me a wheel.  My
>>> question is, am I loosing significant performance choosing this pre-built
>>> binary vs. compiling myself?  For example, my processor might have some
>>> more features than the base version used to build wheels.
>>
>> I guess you are thinking about using this built wheel on some other
>> machine?   You'd have to be lucky for that to work; the wheel depends
>> on the symbols it found at build time, which may not exist in the same
>> places on your other machine.
>>
>> If it does work, the speed will primarily depend on your BLAS library.
>>
>> The pypi wheels should be pretty fast; they are built with OpenBLAS,
>> which is at or near top of range for speed, across a range of
>> platforms.
>>
>> Cheers,
>>
>> Matthew
>
> I installed using pip3 install, and it installed a wheel package.  I did not
> build it - aren't wheels already compiled packages?  So isn't it built for
> the common denominator architecture, not necessarily as fast as one I built
> myself on my own machine?  My question is, on x86_64, is this potential
> difference large enough to bother with not using precompiled wheel packages?

Ah - my guess is that you'd be hard pressed to make a numpy that is as
fast as the precompiled wheel.   The OpenBLAS library included in
numpy selects the routines for your CPU at run-time, so they will
generally be fast on your CPU.   You might be able to get equivalent
or even better performance with a ATLAS BLAS library recompiled on
your exact machine, but that's quite a serious investment of time to
get working, and you'd have to benchmark to find if you were really
doing any better.

Cheers,

Matthew