On Wed, Jun 19, 2013 at 5:45 AM, Matthew Brett <matthew.br...@gmail.com>wrote:
> Hi, > > On Wed, Jun 19, 2013 at 1:43 AM, Frédéric Bastien <no...@nouiz.org> wrote: > > Hi, > > > > > > On Mon, Jun 17, 2013 at 5:03 PM, Julian Taylor > > <jtaylor.deb...@googlemail.com> wrote: > >> > >> On 17.06.2013 17:11, Frédéric Bastien wrote: > >> > Hi, > >> > > >> > I saw that recently Julian Taylor is doing many low level optimization > >> > like using SSE instruction. I think it is great. > >> > > >> > Last year, Mark Florisson released the minivect[1] project that he > >> > worked on during is master thesis. minivect is a compiler for > >> > element-wise expression that do some of the same low level > optimization > >> > that Julian is doing in NumPy right now. > >> > > >> > Mark did minivect in a way that allow it to be reused by other > project. > >> > It is used now by Cython and Numba I think. I had plan to reuse it in > >> > Theano, but I didn't got the time to integrate it up to now. > >> > > >> > What about reusing it in NumPy? I think that some of Julian > optimization > >> > aren't in minivect (I didn't check to confirm). But from I heard, > >> > minivect don't implement reduction and there is a pull request to > >> > optimize this in NumPy. > >> > >> Hi, > >> what I vectorized is just the really easy cases of unit stride > >> continuous operations, so the min/max reductions which is now in numpy > >> is in essence pretty trivial. > >> minivect goes much further in optimizing general strided access and > >> broadcasting via loop optimizations (it seems to have a lot of overlap > >> with the graphite loop optimizer available in GCC [0]) so my code is > >> probably not of very much use to minivect. > >> > >> The most interesting part in minivect for numpy is probably the > >> optimization of broadcasting loops which seem to be pretty inefficient > >> in numpy [0]. > >> > >> Concerning the rest I'm not sure how much of a bottleneck general > >> strided operations really are in common numpy using code. > >> > >> > >> I guess a similar discussion about adding an expression compiler to > >> numpy has already happened when numexpr was released? > >> If yes what was the outcome of that? > > > > > > I don't recall a discussion when numexpr was done as this is before I > read > > this list. numexpr do optimization that can't be done by NumPy: fusing > > element-wise operation in one call. So I don't see how it could be done > to > > reuse it in NumPy. > > > > You call your optimization trivial, but I don't. In the git log of NumPy, > > the first commit is in 2001. It is the first time someone do this in 12 > > years! Also, this give 1.5-8x speed up (from memory from your PR > > description). This is not negligible. But how much time did you spend on > > them? Also, some of them are processor dependent, how many people in this > > list already have done this? I suppose not many. > > > > Yes, your optimization don't cover all cases that minivect do. I see 2 > level > > of optimization. 1) The inner loop/contiguous cases, 2) the strided, > > broadcasted level. We don't need all optimization being done for them to > be > > useful. Any of them are useful. > > > > So what I think is that we could reuse/share that work. NumPy have c code > > generator. They could call minivect code generator for some of them when > > compiling NumPy. This will make optimization done to those code generator > > reused by more people. For example, when new processor are launched, we > will > > need only 1 place to change for many projects. Or for example, it the > call > > to MKL vector library is done there, more people will benefit from it. > Right > > now, only numexpr do it. > > > > About the level 2 optimization (strides, broadcast), I never read NumPy > code > > that deal with that. Do someone that know it have an idea if it would be > > possible to reuse minivect for this? > > Would someone be able to guide some of the numpy C experts into a room > to do some thinking / writing on this at the scipy conference? > > I completely agree that these kind of optimizations and code sharing > seem likely to be very important for the future. > > I'm not at the conference, but if there's anything I can do to help, > please someone let me know. > Concerning the future development of numpy, I'd also suggest that we look at libdynd <https://github.com/ContinuumIO/libdynd>. It looks to me like it is reaching a level of maturity where it is worth trying to plan out a long term path to merger. Chuck
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
