I have migrated to Julia very recently. To be able to lower computation time, I have been trying to implement parallel programming concepts in my serial code.

@time begin

`@everywhere N = 100 ; @everywhere agents = collect(1:1:N) ; @everywhere Nᵣ = 100 ; @everywhere Nₗ = 0 ; @everywhere rᵣ = round(10*pi/180,sigdigits = 4) ; @everywhere rₗ = round(40*pi/180,sigdigits = 4) ; @everywhere μ = 90 ; @everywhere σ = 10 ; @everywhere P = 0.25 ; @everywhere Pᵢ = 0.5 ; @everywhere Pᵣ = 0.25 ; @everywhere K = 100 ; @everywhere maxd = 25 ; # Simulation Related @everywhere mcf = 10 ; @everywhere h = 1 ; # Step-size @everywhere tf = 100 ; # Final time @everywhere n = Int32(1+tf/h) ; # Total number of instances @everywhere θₜ = zeros(Float64,n,N) ; θₜ = MonteCarlo(N,agents,Nᵣ,Nₗ,rᵣ,rₗ,μ,σ,P,Pᵢ,Pᵣ,K,maxd,n,mcf)`

end

Where, The function MonteCarlo is

@everywhere function MonteCarlo(N,agents,Nᵣ,Nₗ,rᵣ,rₗ,μ,σ,P,Pᵢ,Pᵣ,K,maxd,n,mcf)

for mc = 1:1:mcf

`C = zeros(Float64,N,1) ; θ = zeros(Float64,N,1) ; R = zeros(Float64,N,1) ; con = zeros(Int32,Nᵣ,1) ; lib = zeros(Int32,Nₗ,1) ; upcount = zeros(Int32,N,1) ; upagents = zeros(Int32,N,1) ; θₜ = zeros(Float64,n,N) ; θMC = zeros(Float64,mcf,n,N) ; NC = zeros(Int32,N,N) ; W = zeros(Float64,N,N) ; Asep = zeros(Int32,N,N) ; A = zeros(Int32,N,N) ; neigh = zeros(Int32,N,N) ; dc = zeros(Int32,N,N) ; hop = zeros(Int32,N,N) ; hopscore = zeros(Float64,N,N) ; all = zeros(Int32,N,N) ; (R,con,lib,θ) = initial(N,Nᵣ,rᵣ,rₗ,μ,σ) ; Asep = separation(R,θ,N) ; (g,gbef,gaf,A,Abef,Aaf,o,obef,oaf) = graphgeneration(N,Pᵣ,Asep) ; C = kcentrality(g) ; W = initialweights(A,C,N) ; for t = 1:1:n θₜ[t,:] = θ' ; (upagents,upcount,neigh,dc,hop,all,hopscore,NC,A,W,Asep) = timeloop(A,obef,Asep,NC,W,all,hopscore,hop,upcount,upagents,R,C,θ,N,P,Pᵢ) ; end θ[mc,:,:] = θₜ ; end return θₜ`

end

I would like each worker to execute one outer-loop (corresponding to one set of initial conditions).