[Numpy-discussion] NumPy 1.12.0 release

[Neal Becker]

Matthew Brett wrote:

> 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

OK, so at least for BLAS things should be pretty well optimized.