On Fri, May 11, 2012 at 6:13 AM, Dag Sverre Seljebotn < d.s.seljeb...@astro.uio.no> wrote:
> (NumPy devs: I know, I get too many ideas. But this time I *really* > believe in it, I think this is going to be *huge*. And if Mark F. likes > it it's not going to be without manpower; and as his mentor I'd pitch in > too here and there.) > > (Mark F.: I believe this is *very* relevant to your GSoC. I certainly > don't want to micro-manage your GSoC, just have your take.) > > Travis, thank you very much for those good words in the "NA-mask > interactions..." thread. It put most of my concerns away. If anybody is > leaning towards for opaqueness because of its OOP purity, I want to > refer to C++ and its walled-garden of ideological purity -- it has, > what, 3-4 different OOP array libraries, neither of which is able to > out-compete the other. Meanwhile the rest of the world happily > cooperates using pointers, strides, CSR and CSC. > > Now, there are limits to what you can do with strides and pointers. > Noone's denying the need for more. In my mind that's an API where you > can do fetch_block and put_block of cache-sized, N-dimensional blocks on > an array; but it might be something slightly different. > > Here's what I'm asking: DO NOT simply keep extending ndarray and the > NumPy C API to deal with this issue. > > What we need is duck-typing/polymorphism at the C level. If you keep > extending ndarray and the NumPy C API, what we'll have is a one-to-many > relationship: One provider of array technology, multiple consumers (with > hooks, I'm sure, but all implementations of the hook concept in the > NumPy world I've seen so far are a total disaster!). > There is similar intent behind an idea I raised last summer here: http://mail.scipy.org/pipermail/numpy-discussion/2011-June/056945.html I stopped doing anything on it when considering the scope of it with the linear algebra functions included like Pauli suggested. Nathaniel did an experimental implementation of some parts of the idea in Python here: https://github.com/njsmith/numpyNEP/blob/master/numpyNEP.py#L107 > What I think we need instead is something like PEP 3118 for the > "abstract" array that is only available block-wise with getters and > setters. On the Cython list we've decided that what we want for CEP 1000 > (for boxing callbacks etc.) is to extend PyTypeObject with our own > fields; we could create CEP 1001 to solve this issue and make any Python > object an exporter of "block-getter/setter-arrays" (better name needed). > > What would be exported is (of course) a simple vtable: > > typedef struct { > int (*get_block)(void *ctx, ssize_t *upper_left, ssize_t > *lower_right, ...); > ... > } block_getter_setter_array_vtable; > > Let's please discuss the details *after* the fundamentals. But the > reason I put void* there instead of PyObject* is that I hope this could > be used beyond the Python world (say, Python<->Julia); the void* would > be handed to you at the time you receive the vtable (however we handle > that). > > I think this would fit neatly in Mark F.'s GSoC (Mark F.?), because you > could embed the block-transposition that's needed for efficient "arr + > arr.T" at this level. > > Imagine being able to do this in Cython: > > a[...] = b + c * d > > and have that essentially compile to the numexpr blocked approach, *but* > where b, c, and d can have whatever type that exports CEP 1001? So c > could be a "diagonal" array which uses O(n) storage to export O(n^2) > elements, for instance, and the unrolled Cython code never needs to know. > > As far as NumPy goes, something along these lines should hopefully mean > that new C code being written doesn't rely so much on what exactly goes > into "ndarray" and what goes into other classes; so that we don't get > the same problem again that we do now with code that doesn't use PEP 3118. > This general idea is very good. I think PEP 3118 captures a lot of the essence of the ndarray, but there's a lot of potential generality that it doesn't handle, such as the "diagonal" array or pluggable dtypes. -Mark > > Dag > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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