[Numpy-discussion] Proposed Roadmap Overview
Fri Feb 17 20:59:10 CST 2012
Le 17 févr. 2012 18:21, "Mark Wiebe" <firstname.lastname@example.org> a écrit :
> On Fri, Feb 17, 2012 at 11:52 AM, Eric Firing <email@example.com> wrote:
>> On 02/17/2012 05:39 AM, Charles R Harris wrote:
>> > On Fri, Feb 17, 2012 at 8:01 AM, David Cournapeau <firstname.lastname@example.org
>> > <mailto:email@example.com>> wrote:
>> > Hi Travis,
>> > On Thu, Feb 16, 2012 at 10:39 PM, Travis Oliphant
>> > <firstname.lastname@example.org <mailto:email@example.com>> 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
>> > next year:
>> > >
>> > > * NumPy 1.7 to come out as soon as the serious bugs can
>> > 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
>> > 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
>> > > * 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
>> > code being compiled as C++ ? IMO, it will be difficult to do so
>> > without preventing useful C constructs, and without removing some
>> > the existing features (like our use of C99 complex). The subset
>> > is both C and C++ compatible is quite constraining.
>> > I'm in favor of this myself, C++ would allow a lot code cleanup and
>> > 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
>> > should grab onto it. The initial step would be a release whose code
>> > 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.
This is a really important issue, because accessibility is the essential
reason why I am so strongly against it. It trumps by far all my technical
Maybe this is just a coincidence that you use this word but "recrutment" is
not what is happening in an open community, and finding people who want to
make close to the metal, high performance is very different from making the
codebase more accessible. I would argue that they are actually
contradictory, but I would concede this is slightly more subjective claim.
To be used approprietly, c++ requires much more discipline than c. Doing
this for a community-based project is very hard. Doing this with people who
often are scientist first and programmers second even harder.
I have been contributing to numpy for quite a few years and I have
seen/been told many times that numpy c code was hard to dive in, people did
not know where to start, etc... I cannot remember a case where people said
that C itself was the reason: other contributors can correct me if I am
wrong, but I believe you are the first person who considered c/c++ to be a
I have no reason to believe you would not be able to produce better code in
c++. But I believe you are in a minority within the people I would like to
see contributing to numpy.
> 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:
> 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.
>> > Chuck
>> NumPy-Discussion mailing list
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