[SciPy-Dev] Recent Numba updates of interest

[Stanley Seibert]

Hi all,

Following up from conversation in March about Numba's progress toward being
a dependency for SciPy-related projects, I wanted to give an update on some
recent progress on items of interest.  Some of these items will be not be
available until Numba 0.39, which is tagged now and will be officially
released in the next day or so:

   - ppc64le (POWER8 and 9) is now supported with the release of LLVM
   6.0.1.  (This may be of interest to those of you applying for time on the
   Summit and Sierra supercomputers in the US.)  Since there is no wheel
   standard for ppc64le, I would recommend using the conda packages in the
   numba channel on anaconda.org.

   - Testing Numba on ARMv7 (Raspberry Pi) support flushed out some bugs in
   NumPy on ARM (related to alignment) and OpenBLAS.  NumPy 1.16 should have
   the fixes that Numba needs to pass its unit tests on ARM.  Once that
   happens, we should have some Berryconda-compatible conda packages in the
   numba channel.

   - We've made a variety of changes to compiler error messages to hide
   giant tracebacks, provide suggestions and links to relevant documentation,
   and optionally colorize errors similar to gcc.  There's much more to do
   here, and we'll be adding more heuristics to make error messages more
   helpful.

   - Thanks to Matthias Bussonnier, you can now see an HTML annotated
   version of your function in a Jupyter notebook with the
   `inspect_types(pretty=True)` method.  We plan to use this as the core of an
   improved Numba annotation feature that will give you more info about how
   your function was optimized (or not) by ParallelAccelerator and LLVM in the
   future.

   - We have an open PR for support for accessing the attributes of a
   scipy.LowLevelCallable (https://github.com/numba/numba/pull/2999) in
   nopython mode, but we're not sure we understand how it is being used in
   practice.  Anyone who can comment on that PR with example code would be
   much appreciated.

   - There is now a documentation page on how to use and combine the
   various performance features in Numba:
   https://numba.pydata.org/numba-doc/dev/user/performance-tips.html

Those of you at SciPy 2018 this week can see Siu's 3 minute talk on Numba
in the Tools plenary session on Thursday, where he will speedrun all the
other Numba improvements that have happened lately.
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