Hello everyone,

I've done some work on making a new version of Numexpr that would fix some
of the limitations of the original virtual machine with regards to data
types and operation/function count. Basically I re-wrote the Python and C
sides to use 4-byte words, instead of null-terminated strings, for
operations and passing types.  This means the number of operations and
types isn't significantly limited anymore.

Francesc Alted suggested I should come here and get some advice from the
community. I wrote a short proposal on the Wiki here:

https://github.com/pydata/numexpr/wiki/Numexpr-3.0-Branch-Overview

One can see my branch here:

https://github.com/robbmcleod/numexpr/tree/numexpr-3.0

If anyone has any comments they'd be welcome. Questions from my side for
the group:

1.) Numpy casting: I downloaded the Numpy source and after browsing it
seems the best approach is probably to just use
numpy.core.numerictypes.find_common_type?

2.) Can anyone foresee any issues with casting build-in Python types (i.e.
float and integer) to their OS dependent numpy equivalents? Numpy already
seems to do this.

3.) Is anyone enabling the Intel VML library? There are a number of
comments in the code that suggest it's not accelerating the code. It also
seems to cause problems with bundling numexpr with cx_freeze.

4.) I took a stab at converting from distutils to setuputils but this seems
challenging with numpy as a dependency. I wonder if anyone has tried
monkey-patching so that setup.py build_ext uses distutils and then pass the
interpreter.pyd/so as a data file, or some other such chicanery?

(I was going to ask about attaching a debugger, but I just noticed:
https://wiki.python.org/moin/DebuggingWithGdb   )

Ciao,

Robert

-- 
Robert McLeod, Ph.D.
Center for Cellular Imaging and Nano Analytics (C-CINA)
Biozentrum der Universit├Ąt Basel
Mattenstrasse 26, 4058 Basel
Work: +41.061.387.3225
robert.mcl...@unibas.ch
robert.mcl...@bsse.ethz.ch <robert.mcl...@ethz.ch>
robbmcl...@gmail.com
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