# How to avoid large movement of data when using remotecall_fetch?

I wrote a code to solve 10k linear programming problems in parallel. I timed this code on a computer with `nprocs()=4`. Ideally, it should be three times faster than non-parallel code, however, it is only two times faster. I think the problem is in here.

``````remotecall_fetch(fun_sim_optimal_assignment, p, w_sim_column[t,idx])
``````

The argument `w_sim_column[t,idx]` is a long vector with a length of 250k. So, I doubt the transmission of data took too much time. Is there a way to circumvent this data movement?

I have one “violent” idea. Because `w_sim_column` could be generated by just a couple of parameters, I am thinking to write a new function to generate `w_sim_column` and load it in every process. Then, will Julia automatically use the `w_sim_column` in the local process to compute the problem?

``````# This is the code for parallel computing
function fun_H_sim_parallel(fun_sim_optimal_assignment::Function, w_sim_column::Matrix{Vector{Float64}}, num_simulation::Int64, T::Int64, N::Int64)
H_sim_temp = Matrix{Matrix{Int64}}(undef, T, num_simulation)
np = nprocs()
for t = 1:T
i = 1
nextidx() = (idx=i; i+=1; idx)
@sync begin
for p = 1:np
if p != myid() || np == 1
@async begin
while true
idx = nextidx()
if idx > num_simulation
break
end
H_sim_temp[t,idx] = remotecall_fetch(fun_sim_optimal_assignment, p, w_sim_column[t,idx])
end
end
end
end
end
end
return H_sim_temp
end
``````
``````# This is the function being called to solve the linear programming problem
@everywhere function fun_sim_optimal_assignment(w_sim::Vector{Float64})
N = convert(Int64, sqrt(length(w_sim)))
w_sim = reshape(w_sim, N, N)
model_sim = Model(optimizer_with_attributes(Gurobi.Optimizer, "Presolve" => 0, "Method" => 1, "OutputFlag" => 0))
@variable(model_sim, H_sim_temp[1:N, 1:N] >= 0)
@expression(model_sim, row_sum_sim[i = 1:N], sum(H_sim_temp[i, j] for j = 1:N))
@expression(model_sim, column_sum_sim[j = 1:N], sum(H_sim_temp[i, j] for i = 1:N))
@constraint(model_sim, row_constraint_sim[i = 1:N], row_sum_sim[i] == 1)
@constraint(model_sim, column_constraint_sim[j = 1:N], column_sum_sim[j] == 1)
@objective(model_sim, Max, sum(w_sim[i, j] * H_sim_temp[i, j] for i = 1:N, j = 1:N))
optimize!(model_sim)
if termination_status(model_sim) == MOI.OPTIMAL
H_market_sim = value.(H_sim_temp)
else
error("The model was not solved correctly.")
end
H_star_sim = round.(Int64, H_market_sim) # Allocation matrix
return H_star_sim
end
``````