I suspect they still generate temporaries, but probably have a better garbage collector and can re-use the same temporary across iterations. #5227 should take care of the first, and Jeff has posted his hopes that the second will also be implemented (someday). I suspect both of those would be easier than transforming vectorized expressions into optimized code without temporaries, and would achieve many of the same benefits.
--Tim On Wednesday, May 21, 2014 02:44:50 PM Tobias Knopp wrote: > Reading your posts I get the impression that Matlab outperforms Julia in > vectorized code. I think it would be really great if you could give some > numbers to let us know how far Julia lags behind. It seems they have an > outstanding JIT if it can transform vectorized expressions into optimum > code without temporaries. > > Am Mittwoch, 21. Mai 2014 20:36:07 UTC+2 schrieb Andreas Lobinger: > > Hello colleague, > > > > On Wednesday, May 21, 2014 3:41:53 PM UTC+2, Tobias Knopp wrote: > >> Julia does have high level SIMD constructs. > >> But again, does Matlab avoid temporaries in vectorized expressions? > > > > I dont't know, but my matlab experience (and i belong to the group of > > profile users and my unix insight doesn't stop at ps) shows me, that they > > somehow have a smart way of allocating temporaries. I was under the > > impression, that the point of using a high-level language is that i do not > > need to care what internally the processing is, i just write code that > > expresses what i want to calculate (i know, this is a idealized > > environment, but still...). matlab is now for a few years a JIT compiler > > and i assume that below a certain level of abstraction we would see > > technology like in julia. One thing i'd bet a premium bottle of tomato > > ketchup on, is that they have dedicated logic for array operators and an > > optimizer for that. > > > > In julia (and this i'm writing only as observation on the discussions > > here, my julia profiler and LLVM code skill are still evolving) something > > like A = A+B with A and B arrays seemed to be handled as > > > > allocate temp as size(A) > > temp = +(A,B) > > assign A to temp > > > > while something like add!(A,B) would be the solution without additional > > allocation. > > > > There might be a reason in the julia architecture why it's like this and > > i'm looking forward to see something like an expression level optimizer, > > rather than depending on LLVM internal optimizers. > > > > LLVM provides infrastructure for array datatypes but i'm missing > > information, how this is already used in julia. > > > >> The fact that for loops are slower than vectorized expressions in Matlab > >> does not mean that the vectorized code is optimal and any better than > >> what > >> we have in Julia. > > > > There is a reason why there is a MAT in the name. Some time ago i was busy > > in finding out why we hit a hard boundary optimizing some runtime in a > > simulation and (of course i could missinterpret something) align with the > > FPU performance of the system. In a lot of places matlab was slow and in > > some place it still is. For matrix and vector, basic or linear algebra > > matlab is reasonably fast. > > And exactly that situation leads to the effect that people try to > > vectorize things that shouldn't be vectorized.
