[Numpy-discussion] How a transition to C++ could work
Sun Feb 19 04:30:21 CST 2012
On Sun, Feb 19, 2012 at 2:14 AM, David Cournapeau <firstname.lastname@example.org> wrote:
> On Sun, Feb 19, 2012 at 9:52 AM, Mark Wiebe <email@example.com> wrote:
>> On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <firstname.lastname@example.org> wrote:
>>> > Date: Sun, 19 Feb 2012 01:18:20 -0600
>>> > From: Mark Wiebe <email@example.com>
>>> > Subject: [Numpy-discussion] How a transition to C++ could work
>>> > To: Discussion of Numerical Python <NumPy-Discussion@scipy.org>
>>> > Message-ID:
>>> > <CAMRnEmpVTmt=KduRpZKtgUi516oQtqD4vAzm746HmpqgpFXNqQ@mail.gmail.com>
>>> > Content-Type: text/plain; charset="utf-8"
>>> > The suggestion of transitioning the NumPy core code from C to C++ has
>>> > sparked a vigorous debate, and I thought I'd start a new thread to give
>>> > my
>>> > perspective on some of the issues raised, and describe how such a
>>> > transition could occur.
>>> > First, I'd like to reiterate the gcc rationale for their choice to
>>> > switch:
>>> > http://gcc.gnu.org/wiki/gcc-in-cxx#Rationale
>>> > In particular, these points deserve emphasis:
>>> > - The C subset of C++ is just as efficient as C.
>>> > - C++ supports cleaner code in several significant cases.
>>> > - C++ makes it easier to write cleaner interfaces by making it harder
>>> > to
>>> > break interface boundaries.
>>> > - C++ never requires uglier code.
>>> I think they're trying to solve a different problem.
>>> I thought the problem that numpy was trying to solve is "make inner loops
>>> of numerical algorithms very fast". C is great for this because you can
>>> write C code and picture precisely what assembly code will be generated.
>> What you're describing is also the C subset of C++, so your experience
>> applies just as well to C++!
>>> C++ removes some of this advantage -- now there is extra code generated by
>>> the compiler to handle constructors, destructors, operators etc which can
>>> make a material difference to fast inner loops. So you end up just writing
>>> "C-style" anyway.
>> This is in fact not true, and writing in C++ style can often produce faster
>> code. A classic example of this is C qsort vs C++ std::sort. You may be
>> thinking of using virtual functions in a class hierarchy, where a tradeoff
>> between performance and run-time polymorphism is being done. Emulating the
>> functionality that virtual functions provide in C will give similar
>> performance characteristics as the C++ language feature itself.
>>> On the other hand, if your problem really is "write lots of OO code with
>>> virtual methods and have it turned into machine code" (probably like the
>>> GCC guys) then maybe C++ is the way to go.
>> Managing the complexity of the dtype subsystem, the ufunc subsystem, the
>> nditer component, and other parts of NumPy could benefit from C++ Not in a
>> stereotypical "OO code with virtual methods" way, that is not how typical
>> modern C++ is done.
>>> Some more opinions on C++:
>>> Sorry if this all seems a bit negative about C++. It's just been my
>>> experience that C++ adds complexity while C keeps things nice and simple.
>> Yes, there are lots of negative opinions about C++ out there, it's true.
>> Just like there are negative opinions about C, Java, C#, and any other
>> language which has become popular. My experience with regard to complexity
>> and C vs C++ is that C forces the complexity of dealing with resource
>> lifetimes out into all the code everyone writes, while C++ allows one to
>> encapsulate that sort of complexity into a class which is small and more
>> easily verifiable. This is about code quality, and the best quality C++ code
>> I've worked with has been way easier to program in than the best quality C
>> code I've worked with.
> While I actually believe this to be true (very good C++ can be easier
> to read/use than very good C). Good C is also much more common than
> good C++, at least in open source.
> On the good C++ codebases you have been working on, could you rely on
> everybody being a very good C++ programmer ? Because this will most
> likely never happen for numpy. This is the crux of the argument from
> an organizational POV: the variance in C++ code quality is much more
> difficult to control. I have seen C++ code that is certainly much
> poorer and more complex than numpy, to a point where not much could be
> done to save the codebase.
Can this possibly be extended to the following: How will Mark's
(extensive) experience about performance and long-term consequences of
design decisions be communicated to future developers? We not only
want new numpy developers, we want them to write good code without
unintentional performance regressions. It seems like something more
than just code guidelines would be required.
There's also the issue that c++ compilation error messages can be
awful and disheartening. Are there ways of making them not as bad by
following certain coding styles, or is that baked in? (I know clang is
moving towards making them much better, though.)
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