>> >> (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.
Could you provide a bit more context for this list. I think this is an important technology concept. I'd like to understand better how well it jives with Numba-produced APIs and how we can make use of it in NumPy. Where exactly would this be used in the NumPy API? What would it replace? > > - 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] > > 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. This does sound very nice. > > 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. We will look forward to what you come up with. Best regards, -Travis _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
