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. Cheers, Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
