On 06/26/2012 04:08 PM, Dag Sverre Seljebotn wrote: > On 06/26/2012 01:48 PM, David Cournapeau wrote: >> Hi, >> >> I am just continuing the discussion around ABI/API, the technical side >> of things that is, as this is unrelated to 1.7.x. release. >> >> On Tue, Jun 26, 2012 at 11:41 AM, Dag Sverre Seljebotn >> <d.s.seljeb...@astro.uio.no> wrote: >>> On 06/26/2012 11:58 AM, David Cournapeau wrote: >>>> On Tue, Jun 26, 2012 at 10:27 AM, Dag Sverre Seljebotn >>>> <d.s.seljeb...@astro.uio.no> wrote: >>>>> On 06/26/2012 05:35 AM, David Cournapeau wrote: >>>>>> On Tue, Jun 26, 2012 at 4:10 AM, Ondřej >>>>>> Čertík<ondrej.cer...@gmail.com> wrote: >>>>>> >>>>>>> >>>>>>> My understanding is that Travis is simply trying to stress "We >>>>>>> have to >>>>>>> think about the implications of our changes on existing users." and >>>>>>> also that little changes (with the best intentions!) that however >>>>>>> mean >>>>>>> either a breakage or confusion for users (due to historical reasons) >>>>>>> should be avoided if possible. And I very strongly feel the same >>>>>>> way. >>>>>>> And I think that most people on this list do as well. >>>>>> >>>>>> I think Travis is more concerned about API than ABI changes (in that >>>>>> example for 1.4, the ABI breakage was caused by a change that was >>>>>> pushed by Travis IIRC). >>>>>> >>>>>> The relative importance of API vs ABI is a tough one: I think ABI >>>>>> breakage is as bad as API breakage (but matter in different >>>>>> circumstances), but it is hard to improve the situation around our >>>>>> ABI >>>>>> without changing the API (especially everything around macros and >>>>>> publicly accessible structures). Changing this is politically >>>>> >>>>> But I think it is *possible* to get to a situation where ABI isn't >>>>> broken without changing API. I have posted such a proposal. >>>>> If one uses the kind of C-level duck typing I describe in the link >>>>> below, one would do >>>>> >>>>> typedef PyObject PyArrayObject; >>>>> >>>>> typedef struct { >>>>> ... >>>>> } NumPyArray; /* used to be PyArrayObject */ >>>> >>>> Maybe we're just in violent agreement, but whatever ends up being used >>>> would require to change the *current* C API, right ? If one wants to >>> >>> Accessing arr->dims[i] directly would need to change. But that's been >>> discouraged for a long time. By "API" I meant access through the macros. >>> >>> One of the changes under discussion here is to change PyArray_SHAPE from >>> a macro that accepts both PyObject* and PyArrayObject* to a function >>> that only accepts PyArrayObject* (hence breakage). I'm saying that under >>> my proposal, assuming I or somebody else can find the time to implement >>> it under, you can both make it a function and have it accept both >>> PyObject* and PyArrayObject* (since they are the same), undoing the >>> breakage but allowing to hide the ABI. >>> >>> (It doesn't give you full flexibility in ABI, it does require that you >>> somewhere have an "npy_intp dims[nd]" with the same lifetime as your >>> object, etc., but I don't consider that a big disadvantage). >>> >>>> allow for changes in our structures more freely, we have to hide them >>>> from the headers, which means breaking the code that depends on the >>>> structure binary layout. Any code that access those directly will need >>>> to be changed. >>>> >>>> There is the particular issue of iterator, which seem quite difficult >>>> to make "ABI-safe" without losing significant performance. >>> >>> I don't agree (for some meanings of "ABI-safe"). You can export the data >>> (dataptr/shape/strides) through the ABI, then the iterator uses these in >>> whatever way it wishes consumer-side. Sort of like PEP 3118 without the >>> performance degradation. The only sane way IMO of doing iteration is >>> building it into the consumer anyway. >> >> (I have not read the whole cython discussion yet) > > So here's the summary. It's rather complicated but also incredibly neat > :-) And technical details can be hidden behind a tight API. > > - We introduce a C-level metaclass, "extensibletype", which to each type > adds a branch-miss-free string->pointer hash table. The ndarray type is > made an instance of this metaclass, so that you can do > > PyCustomSlots_GetTable(array_object->ob_type) > > - The hash table uses a perfect hashing scheme: > > a) We take the lower 64 bits of md5 of the lookup string (this can be > done compile-time or module-load-time) as a pre-hash "h". > > b) When looking up the table for a key with pre-hash "h", the index in > the table is given by > > ((h >> table->r) & table->m1) ^ table->d[r & table->m2]
Sorry, typo. Should be ((h >> table->r) & table->m1) ^ table->d[h & table->m2] What happens is that "h & table->m2" sorts the keys of the table into n buckets. Then "r" is selected (among 64 possible choices) so that there's no intra-bucket collisions. Finally, d is chosen so that none of the buckets collide, starting with the largest one. Dag > > Then, *if* the element is present, it will always be found on the first > try; the table is guaranteed collisionless. This means that an expensive > branch-miss can be avoided. It is really incredibly fast in practice, > with a 0.5 ns penalty on my 1.8 GHz laptop. > > The magic is in finding the right table->r and table->d. For a 64-slot > table, parameters r and d[0]..d[63] can be found in 10us on my machine > (it's an O(n) operation). (table->d[i] has type uint16_t) > > (This algorithm was found in an academic paper which I'm too lazy to dig > up from that thread right now; perfect hashing is an active research > field.) > > The result? You can use this table to store function pointers in the > type, like C++ virtual tables or like the built-in slots like > tp_get_buffer, but *without* having to agree on everything at > compile-time like in C++. And the only penalty is ~0.5 ns per call and > some cache usage. > > Cython would use this to replace the current custom "cdef class" vtable > with something more tools could agree on, e.g. store function pointers > in the table with keys like > > "method:foo:i4i8->f4" > > But NumPy could easily store entries relating to its C API in the same > hash table, > > "numpy:SHAPE" > > Then, the C API functions would all take PyObject*, look up the fast > hash table on the ob_type. > > This allows for incredibly flexible duck typing on the C level. > > PyArray_Check would just check for the presence of the C API but not > care about the actual Python type, i.e., no PyObject_TypeCheck. > > Me and Robert have talked a lot about this and will move forward with it > for Cython. Obviously I don't expect others than me to pick it up for > NumPy so we'll see... I'll write up a specification document sometimes > over the next couple of weeks as we need that even if only for Cython. > > Dag _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion