Can you update Julia to the current 0.4.5? Your version is year old.
(I keep asking the same question over and over.)
On Wed, Apr 27, 2016 at 6:56 PM, Michael Wojnowicz <mwojn...@uci.edu> wrote:
>
> Here's some behavior using Julia's parallel processing that really
> surprised me. I'm not sure if this behavior is a bug (in which case I
> should report it), or if I'm just missing something. Really stupid but
> minimal example:
>
> This code fails:
> @everywhere function doit(vec::Array{Int64,1})
> AccumulateVectorEntries=SharedArray(Int64,1,1)
> pmap(enumerate(vec)) do index_pair #note: this works in parallel
> (i,entry) = index_pair
> AccumulateVectorEntries+=entry
> end
> return sdata(AccumulateVectorEntries)
> end
>
> result=doit([1,2,3,4,5])
>
> This code works as intended:
> @everywhere function doit(vec::Array{Int64,1})
> AccumulateVectorEntries=SharedArray(Int64,1,1)
> pmap(enumerate(vec)) do index_pair #note: this works in parallel
> (i,entry) = index_pair
> AccumulateVectorEntries[1,1]+=entry
> end
> return sdata(AccumulateVectorEntries)
> end
>
> result=doit([1,2,3,4,5])
>
> Is this sensible based on some principles of how parallel processing
> works, or am I just missing something? PS - I'm trained as a statistician,
> so please excuse any naivete re: computing principles.
>
> BTW -- I'm using
>
> *julia> **versioninfo()*
>
> Julia Version 0.3.8-pre+22
>
> Commit 5078421* (2015-04-28 09:05 UTC)
>
> Platform Info:
>
> System: Linux (x86_64-amazon-linux)
>
> CPU: Intel(R) Xeon(R) CPU E5-2670 v2 @ 2.50GHz
>
> WORD_SIZE: 64
>
> BLAS: libmkl_rt
>
> LAPACK: libmkl_rt
>
> LIBM: libimf
>
> LLVM: libLLVM-3.3
>
>
