# How to improve performance in a function that repeatedly defines and multiplies matrices

**URL:** <https://discourse.julialang.org/t/how-to-improve-performance-in-a-function-that-repeatedly-defines-and-multiplies-matrices/108153>\
**Category:** Julia at Scale\
**Tags:** performance, parallel, linearalgebra, complex-numbers, matrix\
**Created:** [December 29, 2023, 9:47am UTC](https://discourse.julialang.org/t/how-to-improve-performance-in-a-function-that-repeatedly-defines-and-multiplies-matrices/108153 "2023-12-29T09:47:43Z")\
**Posts on this page:** 1\
**Showing post:** 65

<div class="post-metadata">

**Author:** ![Uranium238](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/uranium238/32/209706_2.png) [@Uranium238](https://discourse.julialang.org/u/Uranium238)\
**Post date:** [January 6, 2024, 2:22pm UTC](https://discourse.julialang.org/t/how-to-improve-performance-in-a-function-that-repeatedly-defines-and-multiplies-matrices/108153/65 "2024-01-06T14:22:15Z")

</div>

The main purpose of this thread was to find ways to optimize the serial code rather than focusing on parallelization either by `Threads` or using `Distributed` . With the code finally running faster than the serial Fortran equivalent on my personal PC , I think the goal (at least in terms of programming optimization of the serial code) has been achieved. Hence I am marking this threads as solved. Huge thanks to @ufechner7 @DNF @nilshg @abraemer and others for their constant support.

Further questions deal with parallelizing the code and running it on supercomputers. I have opened a [new thread](https://discourse.julialang.org/t/julia-code-becomes-slower-on-running-on-supercomputers-and-does-not-scale-well-when-parallelizing-with-base-threads/108439) to carry on the further discussion.

---

_[View the full topic](https://discourse.julialang.org/t/how-to-improve-performance-in-a-function-that-repeatedly-defines-and-multiplies-matrices/108153)._