Yes, that code is slow because it accesses and modifies non-constant gobals a, b.
On Sat, Nov 1, 2014 at 1:10 PM, Kapil <[email protected]> wrote: > The updated code is available at > https://github.com/kapiliitr/JuliaBenchmarks/blob/master/streamp.jl > > I am getting a performance slightly less than C for multiple processes, > but for single process, performance is much less (though better than > previous values) > > Just a very basic implementation of one of the benchmarks is: > > julia> size=5000000; > julia> a=fill(3,size); > julia> b=fill(0,size); > julia> @time for i=1:size > b[i]=a[i] > end > > The output is > elapsed time: 0.537257426 seconds (159983648 bytes allocated, 3.16% gc > time) > > In C, the same code takes 0.012210 seconds. > > ᐧ > > Regards, > Kapil Agarwal > > On Sat, Nov 1, 2014 at 12:36 PM, Jason Merrill <[email protected]> > wrote: > >> On Saturday, November 1, 2014 9:35:03 AM UTC-7, Jason Merrill wrote: >>> >>> On Saturday, November 1, 2014 9:15:43 AM UTC-7, Kapil Agarwal wrote: >>>> >>>> I need those variables as globals as otherwise I will have to pass them >>>> from one function to another all the time. >>>> >>> >>> It's generally better long-term design to pass the relevant state around >>> than to have it be global. One thing that helps is to define a type that >>> encapsulates the relevant state, so that you only have to pass one thing >>> around between functions instead of many. E.g. if I have >>> >>> f(a, b, c, d) >>> mutatea!(a) >>> mutateb!(b) >>> mutatec!(c) >>> mutated!(d) >>> return g(a,b,c,d) >>> end >>> >> >> Sorry, this wasn't quite real Julia syntax. Should have written >> `function` before `f(a, b, c, d)` here and below. >> >> >>> and suppose that g calls another function that also depends on a, b, c, >>> and d, and so on, it can get kind of tedious to pass all the arguments >>> separately. >>> >>> But you can do >>> >>> type algorithmState >>> a::Ta >>> b::Tb >>> c::Tc >>> d::Td >>> end >>> >>> (where the Ta, Tb, Tc, and Td should be replaced by appropriate concrete >>> types). >>> >>> Then you can do >>> >>> f(x::algorithmState) >>> mutateState!(x) >>> g(x) >>> end >>> >>> >> >
