Sturla Molden, 03.06.2014 17:13: > Stefan Behnel wrote: > >> Thus my proposal to compile the modules in CPython with Cython, rather than >> duplicating their code or making/keeping them CPython specific. I think >> reducing the urge to reimplement something in C is a good thing. > > For algorithmic and numerical code, Numba has already proven that Python > can be JIT compiled comparable to -O2 in C. For non-algorthmic code, the > speed determinants are usually outside Python (e.g. the network > connection). Numba is becoming what the "dead swallow" should have been. > The question is rather should the standard library use a JIT compiler like > Numba? Cython is great for writing C extensions while avoiding all the > details of the Python C API. But for speeding up algorithmic code, Numba is > easier to use.
I certainly agree that a JIT compiler can do much better optimisations on Python code than a static compiler, especially data driven optimisations. However, Numba comes with major dependencies, even runtime dependencies. >From previous discussions on this list, I gathered that there are major objections against adding such a large dependency to CPython since it can also just be installed as an external package if users want to have it. Static compilation, on the other hand, is a build time thing that adds no dependencies that CPython doesn't have already. Distributions can even package up the compiled .so files separately from the original .py/.pyc files, if they feel like it, to make them selectively installable. So the argument in favour is mostly a pragmatic one. If you can have 2-5x faster code essentially for free, why not just go for it? Stefan _______________________________________________ Python-Dev mailing list Python-Dev@python.org https://mail.python.org/mailman/listinfo/python-dev Unsubscribe: https://mail.python.org/mailman/options/python-dev/archive%40mail-archive.com
