On Wed, Oct 5, 2016 at 11:46 AM, Francesc Alted <> wrote:

> 2016-10-05 8:45 GMT+02:00 srean <>:
>> Good discussion, but was surprised by the absence of numexpr in the
>> discussion., given how relevant it (numexpr) is to the topic.
>> Is the goal to fold in the numexpr functionality (and beyond) into Numpy ?
> Yes, the question about merging numexpr into numpy has been something that
> periodically shows up in this list.  I think mostly everyone agree that it
> is a good idea, but things are not so easy, and so far nobody provided a
> good patch for this.  Also, the fact that numexpr relies on grouping an
> expression by using a string (e.g. (y = ne.evaluate("x**3 + tanh(x**2) +
> 4")) does not play well with the way in that numpy evaluates expressions,
> so something should be suggested to cope with this too.

As Francesc said, Numexpr is going to get most of its power through
grouping a series of operations so it can send blocks to the CPU cache and
run the entire series of operations on the cache before returning the block
to system memory.  If it was just used to back-end NumPy, it would only
gain from the multi-threading portion inside each function call. I'm not
sure how one would go about grouping successive numpy expressions without
modifying the Python interpreter?

I put a bit of effort into extending numexpr to use 4-byte word opcodes
instead of 1-byte.  Progress has been very slow, however, due to time
constraints, but I have most of the numpy data types (u[1-4], i[1-4],
f[4,8], c[8,16], S[1-4], U[1-4]).  On Tuesday I finished writing a Python
generator script that writes all the C-side opcode macros for opcodes.hpp.
Now I have about 900 opcodes, and this could easily grow into thousands if
more functions are added, so I also built a reverse lookup tree (based on
collections.defaultdict) for the Python-side of numexpr.


Robert McLeod, Ph.D.
Center for Cellular Imaging and Nano Analytics (C-CINA)
Biozentrum der Universität Basel
Mattenstrasse 26, 4058 Basel
Work: +41.061.387.3225 <>
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