CODEWITHSUNDEEP
parallel-processingjuliadistributed-computing
Sungmin Ji
Sungmin Ji

Reputation: 87

How can I set Julia `distributed`

I have a question about how to use parallel computing in Julia

Following codes do not work

using Distributed
addprocs(10)

@everywhere include("ADMM2.jl")
@everywhere tuning = [0.04, 0.5, 0.1]
@everywhere include("Basicsetting.jl")
@everywhere using SharedArrays

## generate samples
n_simu = 10
Z_set = SharedArray{Float64, 3}(n, r, n_simu)
X_set = SharedArray{Float64, 3}(n, p, n_simu)
Y_set = SharedArray{Float64, 3}(n, q, n_simu)
Binit_set = SharedArray{Float64, 3}(p, r, n_simu)
Ginit_set = SharedArray{Float64, 3}(p, r, n_simu)

for i in 1:n_simu
    dataset = get_data(fun_list, n, p, q, B_true, G_true, snr, binary = false)
    Z_set[:,:,i] = dataset[:Z_scaled]
    X_set[:,:,i] = dataset[:X]
    Y_set[:,:,i] = dataset[:Y]
    ridge = get_B_ridge(dataset[:Z_scaled], dataset[:X], dataset[:Y], lambda=0.03)
    Binit_set[:,:,i] = ridge[:B]
    Ginit_set[:,:,i] = ridge[:G]
end


## optimization process
@sync @distributed for i in 1:n_simu
    Z = Z_set[:,:,i]
    X = X_set[:,:,i]
    Y = Y_set[:,:,i]
    B = copy(Binit_set[:,:,i])
    G = copy(Ginit_set[:,:,i])
    result2[i] = get_BG_ADMM3(Z,X,Y,B,G, lambda1=0.05, lambda2=0.2, lambda3=0.05, rho=1.0,
    control1 = Dict(:max_iter => 5e1, :tol => 1e-4, :rounding => 0.0), 
    control2 = Dict(:elesparse_B => true, :lowrank_G => true, :elesparse_G  => false, :rowsparse_G => true))
end

Without using distributed, the for loop hasn't any problem operating.

Upvotes: 1

Views: 345

Answers (1)

Przemyslaw Szufel
Przemyslaw Szufel

Reputation: 42194

You are not collecting any results in the for loop. Please note that each variable in a for loop will be created on a different worker process of the Julia cluster. Normally the best strategy is to used an aggregator function to collect the results (for most scenarios I would also prefer such approach over SharedArrays):

result = @distributed (append!) for i in 1:10
    res = rand()+i
    [res]
end

BTW, use @views - you are now creating unnecessary copies of your matrices

Upvotes: 1

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