# Redistribute workload from in-homogeneous local workers to BLAS threads

**URL:** <https://discourse.julialang.org/t/redistribute-workload-from-in-homogeneous-local-workers-to-blas-threads/128515>\
**Category:** General Usage\
**Tags:** parallel\
**Created:** [April 29, 2025, 8:56am UTC](https://discourse.julialang.org/t/redistribute-workload-from-in-homogeneous-local-workers-to-blas-threads/128515 "2025-04-29T08:56:21Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![VinceNeede](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/vinceneede/32/215744_2.png) [@VinceNeede](https://discourse.julialang.org/u/VinceNeede)\
**Post date:** [April 29, 2025, 8:56am UTC](https://discourse.julialang.org/t/redistribute-workload-from-in-homogeneous-local-workers-to-blas-threads/128515/1 "2025-04-29T08:56:21Z")

</div>

I have some expensive function that I’m executing on local workers, something like:

```julia
@everywhere begin
    using LinearAlgebra
    BLAS.set_num_threads(1)
    function expensive_fun()
        #some in-homogeneous task
    end
end
pmap(_ -> expensive_fun(), 1:N)

```

Since this is not homogeneous, if I start with 10 workers, I might end up with 2 after 10 minutes, while the remaining 2 might take other 60 minutes. Is there a way to redistribute the processors used initially as workers on the 2 remaining tasks as blas threads?

> **minimal working example**
>
> ```julia
> @everywhere begin
> using LinearAlgebra
> using ITensors, ITensorMPS
> using Random
> Random.seed!(1234)
> BLAS.set_num_threads(1)
> const sites = siteinds("S=1/2", 100)
> const mpo = random_mpo(sites)
> function random_evolve()
> linkdim = rand([fill(256, 7)..., fill(2048, 3)...])
> @info "evolving with" linkdim
> mps = random_mps(sites; linkdims=linkdim)
> apply(mpo, mps; cutoff=1.e-13)
> end
> end
> 
> pmap(_ -> random_evolve(), 1:10)
> 
> ```