# How to implement hybrid parallelized programs

**URL:** https://discourse.julialang.org/t/how-to-implement-hybrid-parallelized-programs/109604
**Category:** Performance
**Tags:** parallel, multithreading, distributed
**Created:** [February 2, 2024, 4:34am UTC](https://discourse.julialang.org/t/how-to-implement-hybrid-parallelized-programs/109604 "2024-02-02T04:34:46Z")
**Posts on this page:** 1
**Showing post:** 13

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### Author: ![abraemer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/abraemer/32/51403_2.png) [@abraemer](https://discourse.julialang.org/u/abraemer)
#### Post date: [February 4, 2024, 10:48am UTC](https://discourse.julialang.org/t/how-to-implement-hybrid-parallelized-programs/109604/13 "2024-02-04T10:48:37Z")

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Thanks for the simplified code 🙂  
I think you might be able to gain quite some performance by reusing the memory of the matrix. Assuming you can make a version of `make_matrix` that constructs the matrix into a preallocated matrix, then you could try:

```julia
using ChunkSplitters
function calc_function(x)
    kvalue_range = range(0, 0.5, 201)
    result_vector = zeros(201)
    for chunk in chunks(kvalue_range; n=Threads.nthreads())
        Threads.@spawn begin
            temp_matrix = zeros(1000,1000) # or whatever type/size the matrix needs to have
            for k_ind in chunk
                kvalue = kvalue_range[k_ind]
                energy = eigvals!( make_matrix!(temp_matrix, kvalue) ) 
                    # make_matrix make a thousands x thousands matrix
                    # we need different matrices for each kvalues
                result_vector[k_ind] = sub_calculation(energy)
              end
          end
      end

    result = integrate(result_vector)	
        # integrate uses the trapezoidal rule: just adding values
    retun result
end

```

This allocates only 1 matrix for each thread. You could profile `sub_calcuation` separately to make sure it does not allocate or else try to preallocate some more stuff. `integrate` is probably less important as it is called much less often. But you should profile and verify that it has no large chunk of runtime.

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