julia> wpid = addprocs(1)[1] 2 julia> rr = RemoteRef(wpid) RemoteRef(2,1,5)
julia> put!(rr, "Hello") RemoteRef(2,1,5) julia> fetch(rr) "Hello" --Tim On Thursday, February 20, 2014 05:20:49 PM Peter Simon wrote: > I'm finding myself in need of similar functionality lately. Has any > progress been made on this front? In my case, since the large object b > needs to be computed at run-time, I would prefer to simultaneously compute > it on all workers at the beginning, then have these copies stick around for > later reuse. In the Matlab version of the code I'm porting to Julia, I do > this with persistent variables. > > Thanks, > --Peter > > On Thursday, December 13, 2012 2:04:12 AM UTC-8, Viral Shah wrote: > > Need to wrap up remote_call / remote_call_fetch in a few higher level > > functions for such things. I'm going to get cracking on improving our > > parallel support soon. > > > > -viral > > > > On Thursday, December 13, 2012 12:29:58 PM UTC+5:30, Miles Lubin wrote: > >> Seemingly simple question that I haven't managed to figure out after > >> reading the documentation and playing around: How can you broadcast an > >> object from one process (say the main process) to all running processes? > >> I > >> come from an MPI background where this is a fundamental operation. > >> > >> To give an example, say I have a function f(a,b), where b is some large > >> 100MB+ dataset/matrix/object, and I want to compute f(a,b) for a in some > >> range and b fixed. It doesn't make sense to send a new copy of b with > >> each > >> call. Instead I'd like to broadcast b to each process and keep a > >> persistent > >> copy in each process to use during the pmap. What's the best and > >> prettiest > >> way to do this? > >> > >> Thanks, > >> Miles
