Similar question and answer:
http://stackoverflow.com/questions/38075163/julia-uses-only-20-30-of-my-cpu-what-should-i-do/38075939
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On Tue, Jul 5, 2016 at 11:26 AM, <jasci...@gmail.com> wrote:
> I am a complete newcomer to Julia and trying to port some of my R code to
> it;
> Basically I have rewritten the following R code in Julia:
>
> library(parallel)
>
> eps_1<-rnorm(1000000)
> eps_2<-rnorm(1000000)
>
> large_matrix<-ifelse(cbind(eps_1,eps_2)>0,1,0)
> matrix_to_compare = expand.grid(c(0,1),c(0,1))
> indices<-seq(1,1000000,4)
> large_matrix<-lapply(indices,function(i)(large_matrix[i:(i+3),]))
>
> function_compare<-function(x){
> which((rowSums(x==matrix_to_compare)==2) %in% TRUE)
> }
>
> > system.time(lapply(large_matrix,function_compare))
> user system elapsed
> 38.812 0.024 38.828
> > system.time(mclapply(large_matrix,function_compare,mc.cores=11))
> user system elapsed
> 63.128 1.648 6.108
>
> As one can notice I am getting significant speed-up when going from one
> core to 11. Now I am trying to do the same in Julia:
>
> using Distributions;
> @everywhere using Iterators;
> d = Normal();
>
> eps_1 = rand(d,1000000);
> eps_2 = rand(d,1000000);
>
> #Define cluster:
> addprocs(11);
>
> #Create a large matrix:
> large_matrix = hcat(eps_1,eps_2).>=0;
> indices = collect(1:4:1000000)
>
> #Split large matrix:
> large_matrix = [large_matrix[i:(i+3),:] for i in indices];
>
> #Define the function to apply:
> @everywhere function function_split(x)
> matrix_to_compare =
> transpose(reinterpret(Int,collect(product([0,1],[0,1])),(2,4)));
> matrix_to_compare = matrix_to_compare.>0;
> find(sum(x.==matrix_to_compare,2).==2)
> end
>
> @time map(function_split,large_matrix )
> @time pmap(function_split,large_matrix )
> 5.167820 seconds (22.00 M allocations: 2.899 GB, 12.83% gc time)
> 18.569198 seconds (40.34 M allocations: 2.082 GB, 5.71% gc time)
>
> I somehow do not understand why parallel map function does not work for
> me. Maybe somebody can point me to a correct solution.
>
