Thanks! In that case, I'll file an issue then to get this noted. Also, I think there is no (general) issue on the bad performance of higher order functions. Should I file that too?
On Thu, 2015-04-23 at 15:52, Jameson Nash <[email protected]> wrote: > The short answer is that there is a certain set of optimizations that have > been implemented in Julia, but still a considerable set that has not been > implemented. This falls into the category of optimizations that have not > been implemented. Pull requests are always welcome (although I do not > recommend this one as a good beginner / "up-for-grabs" issue). > > On Thu, Apr 23, 2015 at 9:18 AM Mauro <[email protected]> wrote: > >> It is well know that Julia struggles with type inference in higher order >> functions. This usually leads to slow code and memory allocations. >> There are a few hacks to work around this. Anyway, the question I have >> is: Why can't Julia do better with in-place functions? >> >> In short, a higher-order function like this: >> >> function f(fn!,ar) >> for i=1:n >> fn!(ar, i) # fn! updates ar[i] somehow, returns nothing >> nothing # to make sure output of f is discarded >> end >> end >> >> has almost as bad a performance (runtime and allocation-wise) as >> >> function g(fn,ar) >> for i=1:n >> ar[i] = fn(ar[i]) >> end >> end >> >> A in-depth, ready to run example is here: >> https://gist.github.com/mauro3/f17da10247b0bad96f1a >> Including output of @code_warntype. >> >> So, why is Julia allocating memory when running f? Nothing of f gets >> assigned to anything. >> >> Would this be something which is fixable more easily than the whole of >> the higher-order performance issues? If so, is there an issue for this? >> >> Having good in-place higher order functions would go a long way with >> numerical computations. Thanks! >>
