Alternative to SharedArrays for multi-node cluster?

General Usage
2

You may use the DistributedArray constructor to get around these issues, it’s not very much more complicated.

julia> nworkers()
3

julia> @everywhere using DistributedArrays, OffsetArrays

julia> N = 6 # for demonstration
6

julia> Data = distribute(rand(N)); # using the default distribution at this point

julia>  Result = DArray((N,4)) do inds # local indices on each processor
           arr = zeros(inds) # We create an OffsetArray with the correct local indices
           data_local = OffsetArray(localpart(Data),localindices(Data)) # get the local part of data on each processor and assign the proper indices
           for i in inds[1]
               arr[i,:] .= ones(4)*data_local[i]
           end
           parent(arr) # strip the OffsetArray wrapper
       end
6×4 DArray{Float64,2,Array{Float64,2}}:
 0.54454   0.54454   0.54454   0.54454 
 0.285744  0.285744  0.285744  0.285744
 0.678867  0.678867  0.678867  0.678867
 0.62554   0.62554   0.62554   0.62554 
 0.862751  0.862751  0.862751  0.862751
 0.103856  0.103856  0.103856  0.103856
2 Likes