Re: [Numpy-discussion] How a transition to C++ could work
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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. 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. 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. Some more opinions on C++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/ 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. Looking forward to seeing some more concrete examples. Cheers Ben
On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <ben_w_123@yahoo.co.uk> wrote:
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/
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. Looking forward to seeing some more concrete examples.
In the interests of starting small, here's one that I mentioned in the other thread: Consider a regression like this: http://mail.scipy.org/pipermail/numpy-discussion/2011-July/057831.html Fixing this in C would require switching all the relevant usages of NPY_MAXARGS to use a dynamic memory allocation. This brings with it the potential of easily introducing a memory leak, and is a lot of work to do. In C++, this functionality could be placed inside a class, where the deterministic construction/destruction semantics eliminate the risk of memory leaks and make the code easier to read at the same time. Cheers, Mark
Cheers
Ben _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Sun, Feb 19, 2012 at 9:52 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <ben_w_123@yahoo.co.uk> wrote:
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/
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. cheers, David
On Sun, Feb 19, 2012 at 2:14 AM, David Cournapeau <cournape@gmail.com> wrote:
On Sun, Feb 19, 2012 at 9:52 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <ben_w_123@yahoo.co.uk> wrote:
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/
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.) -Chris
cheers,
David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Sun, Feb 19, 2012 at 4:30 AM, Christopher Jordan-Squire <cjordan1@uw.edu>wrote:
On Sun, Feb 19, 2012 at 9:52 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <ben_w_123@yahoo.co.uk> wrote:
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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
between performance and run-time polymorphism is being done. Emulating
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
On Sun, Feb 19, 2012 at 2:14 AM, David Cournapeau <cournape@gmail.com> wrote: tradeoff the 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++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/
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.
I've tried to set a bit of an example to start with the NEPs I've written. The NEPs for both the nditer and the NA functionality are very long and detailed. Some documents giving general code tours of NumPy would be very helpful, however, and this kind of document could communicate both the current code and what direction it might evolve in the future. It might be worth creating a performance test suite to protect against performance regressions. Wes McKinney has made some noise in that direction. ( http://wesmckinney.com/blog/?p=373)
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.)
Yes, this is a problem. Clang has already made this a lot better than the status quo if you have the good fortune of using it. There are ways of making them not as bad, the boost library developers for example have put a lot of thought into this issue, and came up with the boost static assert library as one mechanism to help improve error messages. C++11 introduces static_assert as a language feature motivated by that experience. Cheers, Mark
-Chris
cheers,
David _______________________________________________ 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
Den 19.02.2012 11:30, skrev Christopher Jordan-Squire:
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 are more examples of crappy than good C++ out there. There are tons of litterature on how to write crappy C++. And most programmers do not have the skill or knowledge to write good C++. My biggest issue with C++ is the variability of skills among programmers. It will result in code that are: - unncessesary complex - ugly looking - difficult to understand - verbose and long - inefficient - full of subtile errors - impossible to debug - impossible to maintain - not scalable with hardware - dependent on one particular compiler It is easier to achive this with C++ than C. But it is also easier to avoid. Double-edged sword. It will take more than guidelines. Sturla
On Sun, Feb 19, 2012 at 4:14 AM, David Cournapeau <cournape@gmail.com>wrote:
On Sun, Feb 19, 2012 at 3:10 AM, Ben Walsh <ben_w_123@yahoo.co.uk> wrote:
Date: Sun, 19 Feb 2012 01:18:20 -0600 From: Mark Wiebe <mwwiebe@gmail.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
between performance and run-time polymorphism is being done. Emulating
On Sun, Feb 19, 2012 at 9:52 AM, Mark Wiebe <mwwiebe@gmail.com> wrote: tradeoff 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++: http://gigamonkeys.wordpress.com/2009/10/16/coders-c-plus-plus/
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?
Not initially, but I designed the coding standards and taught the programmers I hired how to write good C++ code.
Because this will most likely never happen for numpy.
This is the role I see good coding standards and consistent code review playing. Programmers who don't know how to write good C++ code can be taught. There are also good books to read, like "C++ Coding Standards," "Effective C++", and others that can help people learn proper technique.
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.
That's a consequence of the power C++ provides. It assumes the programmer knows what he or she is doing, and provides the tools to make things great or shoot oneself in the foot. I'd like to use that power to make NumPy better, in a way which uses high quality modern C++ style. I'm willing to help anyone contributing C-level code to NumPy to learn this style. I'd rather not have to write any more C code, where it's easy to get a crash because the C compiler allowed an implicit type conversion to slip through when I typed the wrong thing. -Mark
cheers,
David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Feb 19, 2012 2:41 AM, "Mark Wiebe" <mwwiebe@gmail.com> wrote:
This is the role I see good coding standards and consistent code review
playing. Programmers who don't know how to write good C++ code can be taught. There are also good books to read, like "C++ Coding Standards," "Effective C++", and others that can help people learn proper technique. I recommended this book (one in the list avove) to anyone who is not afraid of C++ yet: http://search.barnesandnoble.com/Effective-C/Scott-Meyers/e/9780321334879 With great power comes great responsibility. Stéfan
Den 19.02.2012 10:52, skrev Mark Wiebe:
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.
I agree with Mark here. C++ usually produces the faster code. C++ has abstractions that makes it easier to write more efficient code. C++ provides more and better information to the compiler (e.g. strict aliasing rules). C++ compilers are also getting insanely good at optimisation, usually better than C compilers. But C++ also makes it easy to write sluggish bloatware, so the effect on performance is not predictable. Sturla
On Feb 19, 2012, at 10:38 AM, Sturla Molden <sturla@molden.no> wrote: Den 19.02.2012 10:52, skrev Mark Wiebe: 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. I agree with Mark here. C++ usually produces the faster code. C++ has abstractions that makes it easier to write more efficient code. C++ provides more and better information to the compiler (e.g. strict aliasing rules). C++ compilers are also getting insanely good at optimisation, usually better than C compilers. But C++ also makes it easy to write sluggish bloatware, so the effect on performance is not predictable. Just to add, with respect to acceptable compilation times, a judicious choice of C++ features is critical. Sturla _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Den 19.02.2012 16:45, skrev Adam Klein:
Just to add, with respect to acceptable compilation times, a judicious choice of C++ features is critical.
I use Python to avoid recompiling my code all the time. I don't recompile NumPy every time I use it. (I know you are thinking about development, but you have the wrong perspective.) Sturla
On Sun, Feb 19, 2012 at 4:53 PM, Sturla Molden <sturla@molden.no> wrote:
Den 19.02.2012 16:45, skrev Adam Klein:
Just to add, with respect to acceptable compilation times, a judicious choice of C++ features is critical.
I use Python to avoid recompiling my code all the time. I don't recompile NumPy every time I use it.
(I know you are thinking about development, but you have the wrong perspective.)
No he doesn't. Perspectives aren't wrong, just different. I compile both numpy and scipy on a regular (almost daily) basis, and long compile times are very annoying. Ralf
participants (8)
-
Adam Klein -
Ben Walsh -
Christopher Jordan-Squire -
David Cournapeau -
Mark Wiebe -
Ralf Gommers -
Sturla Molden -
Stéfan van der Walt