GenGlobal

Installing

Clone by running the following, and then package can be imported as usual.

Pkg.clone("https://github.com/magerton/GenGlobal.jl.git", "GenGlobal")

Explanation

@GenGlobal declares expression to be a global variable and generates exported functions
set_ and get_ that set the global. Should be used in a module declaration. Thanks to @npfrazier for suggesting how to use global variables in parallel computations.

Example

See example/example.jl and src/TestFunctions/testModule.jl for how to use GenGlobal to (1) update a shared array, (2) compute things based on the values in the Shared Array, and (3) return all the computations. This is useful in problems like dynamic discrete choice models where the inner NFXP loop computes the Emax function / integrated value function in parallel, and the outer loop returns the log-likelihood and its gradient (for which we need the pplus and remote_mapreduce functions).

Other examples

In the module

module mymodule
export dostuff
@GenGlobal myglob1 myglob2
dostuff(i::Int) = myglob1 * i
end

using mymodule
dostuff(1)  # errors out
set_myglob1(1.0)
get_myglob1() == 1.0
dostuff(2) == 2.0
set_myglob1(2.0)
dostuff(4) == 2.0

Example 2

Using @GenGlobal is one way to declare pre-allocated tmpvars for parallel
computations. Just make sure to annotate the type of the variables so that the compiler
knows what types are being used

module testModule
using GenGlobal
using StatsFuns
@GenGlobal globalx
export pplus, globf
pplus(x...) = broadcast(+, x...)
function globf(i::Int, s::T) where {T}
    y = logsumexp(globalx::Vector{T})
    return (y, fill(y, 2, 2), )
end
end # module end

using Base.Test
using BenchmarkTools
using testModule3
iters = 1:1000
addprocs()
@everywhere begin
    using testModule
    using StatsFuns
    rx = collect(1.:1000.)
    rxmyid = rx * myid()
    # to show that each worker has different variables...
    set_globalx(myid() * collect(1.:1000.))
    function fmyid(i::Int)
        y = logsumexp(rxmyid)
        return (y, fill(y, 2, 2), )
    end
end
# ---------- check on what gets computed ---------
plsemyid = @parallel (pplus) for i=iters
    fmyid(i)
end
gplse = @parallel (pplus) for i=iters
    globf(i, zero(Float64))
end
@show @benchmark mapreduce(fmyid, pplus, iters)
@show @benchmark begin
    @parallel (pplus) for i=iters
        f(i)
    end
end
@show @benchmark begin
    @parallel (pplus) for i=iters
        globf(i, zero(Float64))
    end
end