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
>>
>
