Hi,
I'm using a parallel for loop to evaluate a function a few times; the weird
thing is that if I run the program more than once there is a significant
speed improvement.
The speed improvement phenomenon is independent of how many processes I
have (the results discussed below are for 4 processes).
If I run the code shown below the first time (by using include("main.jl"))
the timed part takes about 1 second on my machine (about how long it takes
with no parallelisation).
When I run it again (and for all subsequent runs) it takes about 0.35
seconds.
Does anyone know why this happens/how to fix this?
Thanks!
I've copy/pasted the code below:
*******In the file main.jl******************************************
********************************************
# Main program
using PyPlot
@everywhere using Distributions
require("bandit.jl")
plays = 1000
replicates = 2000
ave_rewardp = zeros(plays, 1)
tic()
ave_rewardp = @parallel (+) for k=1:replicates
bandit(0.0, plays)
end
ave_rewardp = ave_rewardp*(1.0/replicates)
toc()
# Plot the results
figure()
plot([1:plays], ave_rewardp,"b")
*******In the file bandit.jl******************************************
********************************************
function bandit(epsilon, plays)
Nbandit = 10::Int64 #arms of the bandit
Qtmeans = rand(Normal(), Nbandit) #true means of each arm
Qemeans = rand(MvNormal(Qtmeans, eye(Nbandit))) #estimated means of each arm
Qdraw(mean) = rand(Normal(mean)) #normal draw with mean
meanUpdate = ones(Nbandit,2) # array creation
meanUpdate[:,1] = Qemeans #update matrix of means of the bandit
# Play the game
reward = zeros(plays,1)
for p=1:plays
# Epsilon-Greedy Player
if rand() < epsilon
gp_index = indmax(rand(Nbandit)) #return random index
else
gp_index = indmax(Qemeans) #return the max estimated index
end
# Update Step
value = Qdraw(Qtmeans[gp_index]) #draw from true means
reward[p] = value
meanUpdate[gp_index, 2] = meanUpdate[gp_index, 2] + 1.0
meanUpdate[gp_index, 1] = meanUpdate[gp_index, 1] + value
Qemeans[gp_index] = meanUpdate[gp_index, 1]/meanUpdate[gp_index, 2]
end
return reward
end
