Thx a lot. You saved my life :)
On Wednesday, October 7, 2015 at 3:00:25 PM UTC+2, Jonathan Malmaud wrote: > > Within the next few days, support for native threads will be merged into > to the development version of Julia ( > https://github.com/JuliaLang/julia/pull/13410 > <https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FJuliaLang%2Fjulia%2Fpull%2F13410&sa=D&sntz=1&usg=AFQjCNH7zK_jDRkwc3A-kFUUNZEcf-LbbA> > ). > > You can also used the SharedArray type which Julia already has, which lets > multiple Julia processes running on the same machine share memory. You > would use the standard Julia task-parallel tools (like @parfor, etc.) in > that model. > > On Wednesday, October 7, 2015 at 8:34:02 AM UTC-4, cheng wang wrote: >> >> Thanks all for replying. >> >> I have read th parallel computing document before I post this. >> Actually, what I mean is a shared memory model not a distributed model. >> >> My daily research involves extensively using of blas and parallel >> for-loop. >> Julia has a perfect support for blas, as well parallel for-loop could be >> solved by multi-process. >> >> However, if I want to have a shared array that could do efficient blast >> and parallel for-loop in the same time, >> what is the best solution ?? >> >> >> On Tuesday, October 6, 2015 at 4:24:51 PM UTC+2, Andrei Zh wrote: >>> >>> 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 >>>> >>>
