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 Universität 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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