# Huge distributed overhead
**URL:** https://discourse.julialang.org/t/huge-distributed-overhead/115815
**Category:** Performance
**Tags:** question, parallel, memory-allocation, distributed, sharedarrays
**Created:** [June 18, 2024, 3:54pm UTC](https://discourse.julialang.org/t/huge-distributed-overhead/115815 "2024-06-18T15:54:01Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@greivin](https://discourse.julialang.org/u/greivin)
#### Post date: [June 18, 2024, 3:54pm UTC](https://discourse.julialang.org/t/huge-distributed-overhead/115815/1 "2024-06-18T15:54:01Z")
My code looks something like this. First, I have a file where I define my functions
```julia
#Functions.jl file
#Here we import the relevant packages and define all the functions with @everywere
@everywhere Solver(A, N)
B = SharedArray{Float64}(N)
@sync @distributed for i = 1:N
...#Some operations over A dependent on i. We save the results of these operations in B
end
return B
end
```
Then we have a new file for calling the functions
```julia
include("Functions.jl")
for j=1:M #sequential for loop
A = ...#generates sparse array A. This array is very large.
B = Solver(A, N)
writedlm("Data.csv", B)
end
```
With this code I experience a massive overhead. For nworkers \<= 3 (that coincides with the size of the distributed for loop, N, so we can regard nworkers = N) everything works as expected, each “Data.csv” file is produced in around 5 min each.
However for nworkers \> 3 it takes around 25 min to produce one file. This time is independent of nworkers after nworkers \> 3, meaning that the time that it takes to write a file for N = nworkers = 5 is the same as for N = nworkers = 32.
I have no idea what could be going on. One of my hypothesis was that the input sparse matrix A was being copied N times, however my last solution attempt was to declare it as a distributed matrix, this didn’t solve the problem.
If someone has some insight or comments about this problem I’d be extremely grateful.
---
### Author:  [@Oscar\_Smith](https://discourse.julialang.org/u/Oscar_Smith)
#### Post date: [June 18, 2024, 4:32pm UTC](https://discourse.julialang.org/t/huge-distributed-overhead/115815/2 "2024-06-18T16:32:46Z")
In general using mulitthreading will have much lower overhead than distributed because you can then share data
---
### Author:  [@greivin](https://discourse.julialang.org/u/greivin)
#### Post date: [June 19, 2024, 11:46am UTC](https://discourse.julialang.org/t/huge-distributed-overhead/115815/4 "2024-06-19T11:46:17Z")
I tried to do so, however it is not working properly.
When I use distributed write `ps auxr` on the terminal of the respective PC, I can see the different workers (different PID) being executed. However this is not the case with multi-threating. When I run `julia -t 4` I just see a single PID instead of the 4 processes that I expect. Is this expected?