I agree, in fact my knowledge about branch prediction mechanisms is rather limited. But doing *if(rand()%2)* instead of *if(i%i)* yields times close to the given one. On the other hand, with a single type (with *if(true)*) the times drop to around 320 μs.
For julia code, randomness seems to have higher impact: 0.000103 seconds 0.002179 seconds 0.000915 seconds (again, these times highly variable) Anyway, I updated the codes to choose the type randomly. On Sun, Nov 8, 2015 at 3:44 AM Stefan Karpinski <ste...@karpinski.org> wrote: > Yeah, that's a good point. > > On Saturday, November 7, 2015, Gustavo Goretkin < > gustavo.goret...@gmail.com> wrote: > >> I think branch predictors on many platforms today use a table indexed on >> the history of the last couple of branches, so the period-two cycle you >> have is likely getting a lot of correct branch hits. If you mean to totally >> defeat the branch prediction, I think you should use something >> pseudorandom. >> On Nov 6, 2015 12:27 PM, "Cristóvão Duarte Sousa" <cris...@gmail.com> >> wrote: >> >>> Hi, >>> >>> I've been wondering how Julia dispatching system would compare to the >>> C++ virtual functions dispatch one. >>> Hence, I write a simple test in both Julia and C++ that measures the >>> calling of a function over the elements of both an array of concrete >>> objects and another of abstract pointers to objects of derived types. >>> >>> Here is the code https://gist.github.com/cdsousa/f5d669fe3fba7cf848d8 . >>> >>> The usual timings for C++ in my machine, for the concrete and the >>> abstract arrays respectively, are around >>> 0.000143 seconds >>> 0.000725 seconds >>> >>> For the Julia code the timings have much more variability, but they are >>> around >>> 0.000133 seconds >>> 0.002414 seconds >>> >>> This shows that Julia (single) dispatch performance is not that bad >>> while it has some room to improvement. >>> >>> If I'm doing something terribly wrong in these tests, please tell me :) >>> >>> PS: Thank you all, developers of Julia! >>> >>> >>>
