Scratch that, I forgot about `precompile`:
http://docs.julialang.org/en/latest/stdlib/base/#Base.precompile
That may work for your needs.
On Saturday, May 24, 2014 10:21:16 AM UTC-5, Patrick O'Leary wrote:
>
> The first time you run a Julia function, it is lowered, type-inferred,
> generates LLVM code which is then optimized and compiled down to native
> code.
>
> The second time you run a Julia function, the native code already
> generated is called.
>
> (This is why you'll see the first run of a Julia function is thrown out
> when benchmarking. It is assumed that the compilation cost will be
> amortized out.)
>
> There isn't a way around this (that is easy to use, yet); though in your
> case, you can trial-run with plays==1 to get the thing compiled.
>
> On Saturday, May 24, 2014 9:39:36 AM UTC-5, St Elmo Wilken wrote:
>>
>> 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
>>
>>
>>
>>
