On Fri, Feb 17, 2012 at 11:37 AM, Neal Becker <ndbeck...@gmail.com> wrote:
> Mark Wiebe wrote: > > > On Fri, Feb 17, 2012 at 11:52 AM, Eric Firing <efir...@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 <courn...@gmail.com > >> > <mailto:courn...@gmail.com>> wrote: > >> > > >> > Hi Travis, > >> > > >> > On Thu, Feb 16, 2012 at 10:39 PM, Travis Oliphant > >> > <tra...@continuum.io <mailto:tra...@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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