Groups   198 of 99+      julia-users ›  everywhere and memory allocation 3 posts by 2 authors      Pieterjan Robbe    12 1 15   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?     Andre Bieler    12 1 15   I think you are looking for shared arrays? http:  docs.julialang.org en latest manual parallel-computing  id2      Pieterjan Robbe    12 1 15   No, not really. SharedArrays do only support bitstypes. I have an application where the function to be executed in parallel depends on some fixed data  some constants, a dict, some arrays, etc.  I would like to replace my  parallel for loop by  everywhere  because of the reuse of my cholesky factorization , but I'm worried that this will explode my memory usage.