[Numpy-discussion] Proposed Roadmap Overview
Fri Feb 17 20:16:31 CST 2012
On Fri, Feb 17, 2012 at 6:04 PM, Charles R Harris
> On Fri, Feb 17, 2012 at 6:54 PM, Matthew Brett <firstname.lastname@example.org>
>> On Fri, Feb 17, 2012 at 4:58 PM, Charles R Harris
>> <email@example.com> wrote:
>> > On Fri, Feb 17, 2012 at 4:44 PM, David Cournapeau <firstname.lastname@example.org>
>> > wrote:
>> >> I don't think c++ has any significant advantage over c for high
>> >> performance libraries. I am not convinced by the number of people
>> >> argument
>> >> either: it is not my experience that c++ is easier to maintain in a
>> >> open
>> >> source context, where the level of people is far from consistent. I
>> >> doubt
>> >> many people did not contribute to numoy because it is in c instead if
>> >> c++.
>> >> While this is somehow subjective, there are reasons that c is much more
>> >> common than c++ in that context.
>> > I think C++ offers much better tools than C for the sort of things in
>> > Numpy.
>> > The compiler will take care of lots of things that now have to be hand
>> > crafted and I wouldn't be surprised to see the code size shrink by a
>> > significant factor.
>> >> I would much rather move most part to cython to solve subtle ref
>> >> counting
>> >> issues, typically.
>> > Not me, I'd rather write most stuff in C/C++ than Cython, C is cleaner
>> > ;)
>> > Cython good for the Python interface, but once past that barrier C is
>> > easier, and C++ has lots of useful things.
>> Maybe a straw poll of the number of recent contributors to numpy who know:
>> would help resolve this.
>> I suspect using C++ would reduce the number of people who feel able to
>> contribute, compared to:
>> Simplifying the C code
>> Rewriting in Cython
>> Unless there is some reason to think that neither of these approaches
>> would work in the particular case of numpy?
> How about a different variation. How many people writing Python would
> happily give up the following:
> 1) lists
> 2) dictionaries
> 3) default types
> 4) classes
> 5) automatic dellocation of memory
You gain some things and lose a lot of potential developers. Cython
of course does give you access to classes, much of the automatic
deallocation. Lists and dictionaries are fast in python used from
Cython, as they are in Python.
@Dag. @David, @anyone - have you ever had time to look and see what
could be done with Cython in the numpy core?
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