Hi all, Using Julia v0.3.x, there appears to be a significant performance hit when using functions as variables or using anonymous functions. I asked a StackOverflow question about it here <http://stackoverflow.com/questions/28356437/julia-compiler-does-not-appear-to-optimize-when-a-function-is-passed-a-function>, and, as I understand it, the problem is that the compiler is currently unable to determine the type of the output of a function when that function is itself a variable or an anonymous function - consequently, a big performance hit. I run into this issue on an (almost) daily basis, so I was wondering what the best work-around is at this point. Thus far, there are three solutions I'm aware of:
1) Suffer the slower performance: If the function-as-variable/anonymous function is itself a fairly big function, then the slow-down associated with the unknown output type is not such a big deal when overall run-time is taken into consideration. However, frequently the function in question is simple, and so this option is not really tenable in these cases. 2) Use Tim Holy's very cool FastAnonymous <https://github.com/timholy/FastAnonymous.jl> package: For the case of anonymous functions, this package speeds things up quite a bit. However, my understanding is that it is still a bit of a work-around. Moreover, there are some cases in my code where there was still a fairly significant performance hit relative to the in-lined case (if anyone wants more detail on this I'm happy to provide it) 3) Write in-line code: This results in the best possible speed, but there ends up being quite a lot of code duplication in my function. For example, my code might look something like this: if method == "Method1" for n = 1:N y[n] = Method1Function(x1[n], x2[n]) end elseif method == "Method2" for n = 1:N y[n] = Method2Function(x1[n], x2[n]) end elseif etc.... . . . end Most of the time the loops are more complicated than just simply iterating from 1:N, so the code gets really long and is mostly duplication. Moreover, if you want to allow for an arbitrarily large class of functions to be applied within the loop, this solution is not tenable. I understand the core development team is currently aiming to deal with this by v1.0, although there was some noise here <https://github.com/JuliaLang/julia/issues/1864> about trying to get things working by the v0.4 stable release. In the meantime, I'm interested in the best work-around that other members of this user-group have come up with. Cheers, Colin
