module globals and parallel methods

Consider the following:

julia> addprocs(1)
1-element Array{Int64,1}:
 2

julia> @everywhere module Foo

       foo() = remotecall(2, ()->(global X=[1]))
       bar() = @everywhere X[1]=2

       end

julia> Foo.foo()
RemoteRef(2,1,6)

julia> Foo.bar()
exception on 1: exception on 2: ERROR: X not defined
 in eval at /home/amitm/Work/julia/julia/base/sysimg.jl:7
 in anonymous at multi.jl:1439
 in run_work_thunk at multi.jl:598
 in run_work_thunk at multi.jl:607
 in anonymous at task.jl:6

This is because foo() creates X as module global, while the @everywhere call refers to Main

Would it be appropriate to have the remote part of all parallel methods execute under the same module as the calling module? Is information about the calling module available to a function in Base?

Reproduce the module Foo example in Distributed.jl, starting with the remotecall and @everywhere behavior described in the issue. Read the referenced multi.jl and sysimg.jl stack locations to trace module resolution. Done means the project has a decided and tested rule for which module remote parallel methods use.