# Running a function in parallel
**URL:** https://discourse.julialang.org/t/running-a-function-in-parallel/30756
**Category:** Performance
**Created:** [November 5, 2019, 4:24pm UTC](https://discourse.julialang.org/t/running-a-function-in-parallel/30756 "2019-11-05T16:24:57Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@Patrik\_Waldmann](https://discourse.julialang.org/u/Patrik_Waldmann)
#### Post date: [November 5, 2019, 4:24pm UTC](https://discourse.julialang.org/t/running-a-function-in-parallel/30756/1 "2019-11-05T16:24:57Z")
I need help getting a function to run in parallel. I want to distribute both data and packages to all workers, see the following example (which doesn’t work):
```julia
#Simulated data (n observations, p variables, tr truevariables, sig error)
n, p, q, tr_p1, tr_p2, sig = 1000, 5000, 2, 10, 5, 0.5
X = randn(n, p)
Y = randn(n, q)
B_true1 = [randn(tr_p1)..., zeros(p-tr_p1)...]
B_true2 = [randn(tr_p2)..., zeros(p-tr_p2)...]
y1 = X*B_true1 + sig*randn(n)
y2 = X*B_true2 + sig*randn(n)
Y[:,1] = y1
Y[:,2] = y2
#Initialize 4 workers
using Distributed
addprocs(4)
@everywhere using StructuredOptimization, Random
@everywhere function l1sep(λ1,λ2) # opt function with random permutation of train and test data
cvind = randperm(size(X)[1])
Ytrain = Y[cvind[1:800],:]
Ytest = Y[cvind[801:size(X)[1]],:]
Xtrain = X[cvind[1:800],:]
Xtest= X[cvind[801:size(X)[1]],:]
B = Variable(size(X)[2],size(Y)[2])
@minimize ls(Xtrain*B - Ytrain) + λ1*norm(B[:,1],1) + λ2*norm(B[:,2],1) with ZeroFPR()
Bhat = copy(~B)
Ytestpred = Xtest*Bhat
MSEtest = (0.5*norm(Ytestpred-Ytest,2)^2)/(length(Ytest)*size(X)[2])
return MSEtest
end
# Trying to run 4 optimizations in parallel doesn't work
XY = @spawn X,Y
pmap(l1sep(5.0,5.0),XY)
# How to collect MSEtest from the different workers?
```
---
### Author:  [@Noel\_Araujo](https://discourse.julialang.org/u/Noel_Araujo)
#### Post date: [November 5, 2019, 4:56pm UTC](https://discourse.julialang.org/t/running-a-function-in-parallel/30756/2 "2019-11-05T16:56:41Z")
I can see that your function calling of `pmap` is not correct. Here a dummy example that maybe can help you:
```julia
using Distributed
addprocs(3)
@everywhere function foo(a,b)
aa = a + b*9
println(aa)
return aa
end
pmap(x->foo(x[1],x[2]), [(1,2) (3,4)])
# or
X = [1 3]
Y = [2 4]
XY = [(X[i],Y[i]) for i in 1:length(X)]
pmap(x->foo(x[1],x[2]), XY)
```
---
### Author:  [@Patrik\_Waldmann](https://discourse.julialang.org/u/Patrik_Waldmann)
#### Post date: [November 5, 2019, 6:14pm UTC](https://discourse.julialang.org/t/running-a-function-in-parallel/30756/3 "2019-11-05T18:14:59Z")
I’m not sure I follow you, but neither:
```julia
pmap(x->l1sep(x[1],x[2]),[Y X])
```
nor
```julia
XY = [(Y[i,:],X[i,:]) for i in 1:size(X)[1]]
pmap(x->l1sep(x[1],x[2]),XY)
```
works.