On Tue, Jan 17, 2017 at 3:47 PM, Neal Becker <ndbeck...@gmail.com> wrote: > Matthew Brett wrote: > >> Hi, >> >> On Tue, Jan 17, 2017 at 5:56 AM, Neal Becker <ndbeck...@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 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
