As far as I understand, the goal is not to handle arbitrary Python code, because this would become too difficult as is not necessary when you have a simple math oriented function which you want to speed up. The idea is to create something similar to http://www.enthought.com/~ischnell/paper.html which only handles some restricted Python code, but using LLVM (instead of C) as the target language.
- Ilan On Tue, Mar 13, 2012 at 1:39 PM, Nathaniel Smith <n...@pobox.com> wrote: > On Tue, Mar 13, 2012 at 5:18 PM, Travis Oliphant <tra...@continuum.io> wrote: >> Cython and Numba certainly overlap. However, Cython requires: >> >> 1) learning another language > > So is the goal for numba to actually handle arbitrary Python code with > correct semantics, i.e., it's actually a compiled implementation of > Python-the-language? (I feel like most of where Cython-the-language > differs from Python-the-language is that Cython adds extensions for > stuff where getting speed out of Python-the-language would just be too > hard. Dynamic type inference for numpy arrays is definitely a good > start, but you can't even, say, promote a Python integer to a C > integer without changing semantics...) > >> 2) creating an extension module --- loading bit-code files and >> dynamically executing (even on a different machine from the one that >> initially created them) can be a powerful alternative for run-time >> compilation and distribution of code. > > Totally agreed on this point, the workflow for Cython could definitely > be smoother. > > -- Nathaniel > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
