[Numpy-discussion] Proposed Roadmap Overview

Ralf Gommers ralf.gommers@googlemail....
Fri Feb 17 14:38:36 CST 2012


On Fri, Feb 17, 2012 at 8:31 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:

> On Fri, Feb 17, 2012 at 11:00 AM, Christopher Jordan-Squire <
> cjordan1@uw.edu> wrote:
>
>> On Fri, Feb 17, 2012 at 10:21 AM, Mark Wiebe <mwwiebe@gmail.com> wrote:
>> > On Fri, Feb 17, 2012 at 11:52 AM, Eric Firing <efiring@hawaii.edu>
>> wrote:
>> >>
>> >> On 02/17/2012 05:39 AM, Charles R Harris wrote:
>> >> >
>> >> >
>> >> > On Fri, Feb 17, 2012 at 8:01 AM, David Cournapeau <
>> cournape@gmail.com
>> >> > <mailto:cournape@gmail.com>> wrote:
>> >> >
>> >> >     Hi Travis,
>> >> >
>> >> >     On Thu, Feb 16, 2012 at 10:39 PM, Travis Oliphant
>> >> >     <travis@continuum.io <mailto:travis@continuum.io>> wrote:
>> >> >      > Mark Wiebe and I have been discussing off and on (as well as
>> >> >     talking with Charles) a good way forward to balance two competing
>> >> >     desires:
>> >> >      >
>> >> >      >        * addition of new features that are needed in NumPy
>> >> >      >        * improving the code-base generally and moving towards
>> a
>> >> >     more maintainable NumPy
>> >> >      >
>> >> >      > I know there are load voices for just focusing on the second
>> of
>> >> >     these and avoiding the first until we have finished that.  I
>> >> >     recognize the need to improve the code base, but I will also be
>> >> >     pushing for improvements to the feature-set and user experience
>> in
>> >> >     the process.
>> >> >      >
>> >> >      > As a result, I am proposing a rough outline for releases over
>> the
>> >> >     next year:
>> >> >      >
>> >> >      >        * NumPy 1.7 to come out as soon as the serious bugs
>> can be
>> >> >     eliminated.  Bryan, Francesc, Mark, and I are able to help triage
>> >> >     some of those.
>> >> >      >
>> >> >      >        * NumPy 1.8 to come out in July which will have as many
>> >> >     ABI-compatible feature enhancements as we can add while improving
>> >> >     test coverage and code cleanup.   I will post to this list more
>> >> >     details of what we plan to address with it later.    Included for
>> >> >     possible inclusion are:
>> >> >      >        * resolving the NA/missing-data issues
>> >> >      >        * finishing group-by
>> >> >      >        * incorporating the start of label arrays
>> >> >      >        * incorporating a meta-object
>> >> >      >        * a few new dtypes (variable-length string,
>> >> >     varialbe-length unicode and an enum type)
>> >> >      >        * adding ufunc support for flexible dtypes and possibly
>> >> >     structured arrays
>> >> >      >        * allowing generalized ufuncs to work on more kinds of
>> >> >     arrays besides just contiguous
>> >> >      >        * improving the ability for NumPy to receive
>> JIT-generated
>> >> >     function pointers for ufuncs and other calculation opportunities
>> >> >      >        * adding "filters" to Input and Output
>> >> >      >        * simple computed fields for dtypes
>> >> >      >        * accepting a Data-Type specification as a class or
>> JSON
>> >> > file
>> >> >      >        * work towards improving the dtype-addition mechanism
>> >> >      >        * re-factoring of code so that it can compile with a
>> C++
>> >> >     compiler and be minimally dependent on Python data-structures.
>> >> >
>> >> >     This is a pretty exciting list of features. What is the rationale
>> >> > for
>> >> >     code being compiled as C++ ? IMO, it will be difficult to do so
>> >> >     without preventing useful C constructs, and without removing
>> some of
>> >> >     the existing features (like our use of C99 complex). The subset
>> that
>> >> >     is both C and C++ compatible is quite constraining.
>> >> >
>> >> >
>> >> > I'm in favor of this myself, C++ would allow a lot code cleanup and
>> make
>> >> > it easier to provide an extensible base, I think it would be a
>> natural
>> >> > fit with numpy. Of course, some C++ projects become tangled messes of
>> >> > inheritance, but I'd be very interested in seeing what a good C++
>> >> > designer like Mark, intimately familiar with the numpy code base,
>> could
>> >> > do. This opportunity might not come by again anytime soon and I
>> think we
>> >> > should grab onto it. The initial step would be a release whose code
>> that
>> >> > would compile in both C/C++, which mostly comes down to removing C++
>> >> > keywords like 'new'.
>> >> >
>> >> > I did suggest running it by you for build issues, so please raise any
>> >> > you can think of. Note that MatPlotLib is in C++, so I don't think
>> the
>> >> > problems are insurmountable. And choosing a set of compilers to
>> support
>> >> > is something that will need to be done.
>> >>
>> >> It's true that matplotlib relies heavily on C++, both via the Agg
>> >> library and in its own extension code.  Personally, I don't like this;
>> I
>> >> think it raises the barrier to contributing.  C++ is an order of
>> >> magnitude more complicated than C--harder to read, and much harder to
>> >> write, unless one is a true expert. In mpl it brings reliance on the
>> CXX
>> >> library, which Mike D. has had to help maintain.  And if it does
>> >> increase compiler specificity, that's bad.
>> >
>> >
>> > This gets to the recruitment issue, which is one of the most important
>> > problems I see numpy facing. I personally have contributed a lot of
>> code to
>> > NumPy *in spite of* the fact it's in C. NumPy being in C instead of C++
>> was
>> > the biggest negative point when I considered whether it was worth
>> > contributing to the project. I suspect there are many programmers out
>> there
>> > who are skilled in low-level, high-performance C++, who would be
>> willing to
>> > contribute, but don't want to code in C.
>> >
>> > I believe NumPy should be trying to find people who want to make high
>> > performance, close to the metal, libraries. This is a very different
>> type of
>> > programmer than one who wants to program in Python, but is willing to
>> dabble
>> > in a lower level language to make something run faster. High performance
>> > library development is one of the things the C++ developer community
>> does
>> > very well, and that community is where we have a good chance of finding
>> the
>> > programmers NumPy needs.
>> >
>> >> I would much rather see development in the direction of sticking with C
>> >> where direct low-level control and speed are needed, and using cython
>> to
>> >> gain higher level language benefits where appropriate.  Of course, that
>> >> brings in the danger of reliance on another complex tool, cython.  If
>> >> that danger is considered excessive, then just stick with C.
>> >
>> >
>> > There are many small benefits C++ can offer, even if numpy chooses only
>> to
>> > use a tiny subset of the C++ language. For example, RAII can be used to
>> > reliably eliminate PyObject reference leaks.
>> >
>> > 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. There
>> are
>> > other examples like this where the C language has forced a suboptimal
>> design
>> > choice because of how hard it would be to do it better.
>> >
>> > Cheers,
>> > Mark
>> >
>>
>> In a similar vein, could incorporating C++ lead to a simpler low-level
>> API for numpy?
>
>
> This could definitely happen. One way to do it is to have a stable C API
> which remains fixed over many releases, and a C++ library which is allowed
> to change significantly at each release. This is what the LLVM project
> does, for example. OpenCV is an example of another project which was
> previously just C, but now has an extensive C++ API.
>
>
>> I know Mark has talked before about--in the long-term,
>> as a dream project to scratch his own itch, and something the BDF12
>> doesn't necessarily agree with--implementing the great ideas in numpy
>> as a layered C++ library. (Which would have the added benefit of
>> making numpy more of a general array library that could be exposed to
>> any language which can call C++ libraries.)
>>
>> I don't imagine that's on the table for anything near-term, but I
>> wonder if making more of the low-level stuff C++ would make it easier
>> for performance nuts to write their own code in C/C++ interfacing with
>> numpy, and then expose it to python. After playing around with ufuncs
>> at the C level for a little while last summer, I quickly realized any
>> simplifications would be greatly appreciated.
>>
>
> This is all possible, yes. The way this typically works is that library
> authors use advanced C++ techniques to get generality, performance, and
> usability. The library user can then write code which is very simple and
> written in a way which makes simple errors very difficult to make compared
> to using a C-like API.
>

While the longer compile times are going to annoy me, I don't have a strong
opinion on using C++. One thing to keep in mind though is portability.
Numpy is used on many platforms and with many compilers. Keeping things
working on AIX or with a PathScale compiler for example will be a lot more
difficult when using C++. Or will support for not-so-common platforms be
reduced?

Ralf
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