Julia supports multiprocessing pretty well, including map-reduce-like jobs.
E.g. in the next example I add 3 processes to a "workgroup", distribute
simulation between them and then reduce results via (+) operator:
julia> addprocs(3)
3-element Array{Int64,1}:
2
3
4
julia> nheads = @parallel (+) for i=1:200000000
Int(rand(Bool))
end
100008845
You can find full example and a lot of other fun in official documentation
on parallel computing:
http://julia.readthedocs.org/en/latest/manual/parallel-computing/
Note, though, that it's not real (i.e. Hadoop/Spark-like) map-reduce, since
original idea of MR concerns distributed systems and data-local
computations, while here we do everything on the same machine. If you are
looking for big data solution, search this forum for some (dead or alive)
projects for it.
On Monday, October 5, 2015 at 11:52:21 PM UTC+3, cheng wang wrote:
>
> Hello everyone,
>
> I am a Julia newbie. I am thrilled by Julia recently. It's an amazing
> language!
>
> I notice that julia currently does not have good support for
> multi-threading programming.
> So I am thinking that a spark-like mapreduce parallel model +
> multi-process maybe enough.
> It is easy to be thread-safe and It could solve most vector-based
> computation.
>
> This idea might be too naive. However, I am happy to see your opinions.
>
> Thanks in advance,
> Cheng
>
