I think you are looking for shared arrays? http://docs.julialang.org/en/latest/manual/parallel-computing/#id2
On Tuesday, December 1, 2015 at 2:55:59 PM UTC-5, Pieterjan Robbe wrote: > > does the @everywhere macro allocate extra memory to make local copies of a > matrix for every processor? > > A = sprandn(10000,10000,0.7) > @time A = sprandn(10000,10000,0.7) > > gives 2.422259 seconds (23 allocations: 1.565 GB, 3.77% gc time) > > @everywhere A = sprandn(10000,10000,0.7) > @time @everywhere A = sprandn(10000,10000,0.7) > > gives 16.495639 seconds (1.31 k allocations: 1.565 GB, 6.14% gc time). > > However, I know that there are local copies of the matrix on each > processor: > > @everywhere println(A[1,1]) > > -1.2751101862102039 > > From worker 5: 0.0 > > From worker 4: 0.0 > > From worker 2: 0.853669869355948 > > From worker 3: 0.0 > > Is there a way to use the @everywhere macro without allocating extra > memory? Suppose A was created using > > A = speye(10000,10000,0.7) > > is there also a copy of the matrix A for all of the workers? >
