# Seed random number generator when using pmap

Hi

I am trying to use `pmap` to increase the speed of my code. And I try to seed the random generator in different processors, so I have a code like

``````using Distributed

@everywhere using LinearAlgebra
@everywhere using StatsBase
@everywhere using Statistics
@everywhere using Random
@everywhere using StaticArrays
@everywhere using Future

#generate random number generator for each processor
@everywhere const LOCAL_R = Future.randjump(MersenneTwister(123), nprocs())[myid()]

@everywhere function rand_haar2(::Val{n}) where n
M = randn(LOCAL_R,SMatrix{ComplexF64, n,n}) # = rand(Ginibre(2, n))
q = qr(M).Q
L = cispi.(2 .* @SVector(rand(n)))
return q*diagm(L)
end

@everywhere function main2(; ps=2, theta = 1.08, Nk = 800, Nr = 10000)
sum_z =@MVector zeros(Nk)
sum_zlog =@MVector zeros(Nk)
sum_s =@MVector zeros(Nk)
sum_slog =@MVector zeros(Nk)

Npool = Int(floor(10^ps))
pool = zeros(Npool)
pool_sample = zeros(Npool)
logpool = zeros(Npool)

Kup= @SMatrix[cos(theta) 0; 0 sin(theta)]
Kdown = @SMatrix[sin(theta) 0; 0 cos(theta)]
P2up = kron(@SMatrix[1. 0.;0. 1.], @SMatrix[1 0; 0 0])
P2down = kron(@SMatrix[1 0;0 1], @SMatrix[0 0;0 1])

for i in 1:Nr
pool .= 0.5
pool_sample .= 0

for k in 1:Nk
poolcount = 0
while poolcount < Npool
z1 = pool[rand(1:Npool)]
rho1 = diagm(@SVector[z1,1-z1])

z2 = pool[rand(1:Npool)]
rho2 = diagm(@SVector[z2,1-z2])

u1 = rand_haar2(Val{2}())
u2 = rand_haar2(Val{2}())

K1up = u1*Kup*u1'
K1down = u1*Kdown*u1'

K2up = u2*Kup*u2'
K2down = u2*Kdown*u2'

rho1p = K1up*rho1*K1up'
rho2p = K2up*rho2*K2up'

p1 = real(tr(rho1p+rho1p'))/2
p2 = real(tr(rho2p+rho2p'))/2

if rand(LOCAL_R)<p1
rho1p = (rho1p+rho1p')/(2*p1)
else
rho1p = K1down*rho1*K1down'/((1-p1))
end

if rand(LOCAL_R)<p2
rho2p = (rho2p+rho2p')/(2*p2)
else
rho2p = K2down*rho2*K2down'/((1-p2))
end

rho = kron(rho1p,rho2p)

U = rand_haar2(Val{4}())
rho_p = P2up*U*rho*U'*P2up'
p = real(tr(rho_p+rho_p'))/2

if rand(LOCAL_R)<p
temp =(rho_p+rho_p')/2
#rho_f = @SMatrix[tr(temp[1:2,1:2]) tr(temp[1:2,3:4]); tr(temp[3:4,1:2]) tr(temp[3:4,3:4])]/(p)
rho_f = @SMatrix[temp[1,1]+temp[2,2] temp[1,3]+temp[2,4]; temp[3,1]+temp[4,2] temp[3,3]+temp[4,4]]/(p)
else
temp = P2down*U*rho*U'*P2down'
rho_f = @SMatrix[temp[1,1]+temp[2,2] temp[1,3]+temp[2,4]; temp[3,1]+temp[4,2] temp[3,3]+temp[4,4]]/(1-p)
end
rho_f = (rho_f+rho_f')/2
t = abs(tr(rho_f*rho_f))
z = (1-t)/(1+sqrt(2*t-1))
if !iszero(abs(z))
poolcount = poolcount+1
pool_sample[poolcount] = abs(z)
end
end

pool .= pool_sample
pool_sample .= 0

logpool .=log.(pool)

sum_z[k] += mean(pool)
sum_zlog[k] += mean(logpool)/sqrt(Npool)
sum_s[k] += (std(pool)/sqrt(Npool))^2
sum_slog[k] += (std(logpool)/sqrt(Npool))^2
end

end
return sum_z/Nr, sqrt.(sum_s)/Nr, sum_zlog/Nr, sqrt.(sum_slog)/Nr
end

@time ret = pmap(main2,Nr = [2500,2500,2500,2500])
``````

But when I try to run this code, I got an error

``````ERROR: On worker 2:
MethodError: no method matching getindex(::MersenneTwister, ::Int64)
Stacktrace:
@ Distributed D:\Julia-1.7.2\share\julia\stdlib\v1.7\Distributed\src\macros.jl:219
[4] top-level scope
@ D:\Julia-1.7.2\share\julia\stdlib\v1.7\Distributed\src\macros.jl:203
``````

Also, I have some keywords arguments in function `main2`. `Nr =10000` represent the total number of iterations of one loop in the function. Now I want to split this loop to four processors and each one does just 2500 iterations. So I try `@time ret = pmap(main2,Nr = [2500,2500,2500,2500])`, is this correct or not?

I am sorry that I know very little about the parallel computing in Julia. I tried to read the manual, but I still donâ€™t understand how it works.

Thanks

Could you not just seed each process in `main2`?

``````@everywhere function main2(; ps=2, theta = 1.08, Nk = 800, Nr = 10000, processID)
Random.seed!(seedvalue) # pass in the process ID here  (see additional argument)
end
``````

Yes I think this is also a possible way. I will try it thanks.