# 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:** 58

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**Author:** ![DNF](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dnf/32/10191_2.png) [@DNF](https://discourse.julialang.org/u/DNF)\
**Post date:** [January 5, 2024, 1:23pm UTC](https://discourse.julialang.org/t/how-to-improve-performance-in-a-function-that-repeatedly-defines-and-multiplies-matrices/108153/58 "2024-01-05T13:23:51Z")

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> [@nilshg](#):
>
> I would advise you to stop thinking about parallelization

_Emphatically_ agreed.

@Uranium238 Optimize single-threaded performance, with a particular view to reducing the number of allocations, first. _Then_ start thinking about multi-threading.

Not only are single-threaded optimizations more likely to be significant, but parallelization will scale better as a result. Julia parallelization is not great for GC-heavy code.

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