[Numpy-discussion] Proposed Roadmap Overview

Charles R Harris charlesr.harris@gmail....
Fri Feb 17 12:46:27 CST 2012


On Fri, Feb 17, 2012 at 11:37 AM, Neal Becker <ndbecker2@gmail.com> wrote:

> Mark Wiebe 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
> >
> >
>
> I think numpy really wants to use c++ templates to generate specific
> instantiations of algorithms for each dtype from a generic version, rather
> than
> the current code that uses cpp.
>
>
One of many places. Exception handling, smart pointers, and iterators are
the first things that come to my mind. Note that smart pointers also
provide a nice way to do some high performance stuff, like transparent
pointer swapping with memory deallocation.

Chuck
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