Parallel processing 朱莉娅：SharedArray和远程工作者成为一个0元素数组

我正在尝试在服务器上使用远程工作者运行一些代码，我希望与Julia 1.5.3上的本地工作者结合使用。以下代码在使用24个工作线程在本地运行时运行良好：

using Distributed
using SharedArrays
a = SharedArray{Float64}(100)
@sync @distributed for i = 1:100
    a[i] = i+1
end
sum(a)

如果我添加具有

N_remote = 24
for i=1:N_remote
    addprocs(["user@192.168.0.129"], tunnel=true, dir="/home/user/scripts/", exename="/home/user/julia-1.5.3/bin/julia")
end

然后，在运行第一个代码时出现以下错误：

 julia> include("test_sharedarray.jl")
ERROR: LoadError: TaskFailedException:
On worker 4:
BoundsError: attempt to access 0-element Array{Float64,1} at index [1]
setindex! at ./array.jl:847 [inlined]
setindex! at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/SharedArrays/src/SharedArrays.jl:510
macro expansion at /home/usuaris/spcom/gfebrer/bayesian_mc_watson/scripts/test_sharedarray.jl:5 [inlined]
#13 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Distributed/src/macros.jl:301
#160 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Distributed/src/macros.jl:87
#103 at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Distributed/src/process_messages.jl:290
run_work_thunk at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Distributed/src/process_messages.jl:79
run_work_thunk at /buildworker/worker/package_linux64/build/usr/share/julia/stdlib/v1.5/Distributed/src/process_messages.jl:88
#96 at ./task.jl:356

...and 23 more exception(s).

Stacktrace:
 [1] sync_end(::Channel{Any}) at ./task.jl:314
 [2] (::Distributed.var"#159#161"{var"#13#14",UnitRange{Int64}})() at ./task.jl:333
Stacktrace:
 [1] sync_end(::Channel{Any}) at ./task.jl:314
 [2] top-level scope at task.jl:333
 [3] include(::String) at ./client.jl:457
 [4] top-level scope at REPL[5]:1
in expression starting at /home/user/scripts/test_sharedarray.jl:4

---

SharedArray仅在单个群集节点中工作。 换句话说，这用于在同一服务器上运行的进程之间共享RAM内存。 当您添加另一个服务器时，显然您将看不到该内存

您应该做的是使用DistributedArray.jl：

using Distributed, DistributedArrays
addprocs(2)
@everywhere using DistributedArrays
a=dzeros((3,4),workers())
@sync @distributed for i = 1:nworkers()
    a_part = localpart(a) 
    vec(a_part) .= (1:length(a_part)) .+ 1000*myid()
end

现在让我们看看

a

：

julia> a
3×4 DArray{Float64,2,Array{Float64,2}}:
 2001.0  2004.0  3001.0  3004.0
 2002.0  2005.0  3002.0  3005.0
 2003.0  2006.0  3003.0  3006.0