[julia-users] R code to Julia Performance issues with pmap

[jascisen]

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.