# Any performance issues with multiple dispatch?

**URL:** <https://discourse.julialang.org/t/any-performance-issues-with-multiple-dispatch/55468>\
**Category:** New to Julia\
**Created:** [February 17, 2021, 3:55pm UTC](https://discourse.julialang.org/t/any-performance-issues-with-multiple-dispatch/55468 "2021-02-17T15:55:52Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![lmiq](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lmiq/32/18314_2.png) [@lmiq](https://discourse.julialang.org/u/lmiq)\
**Post date:** [February 17, 2021, 5:18pm UTC](https://discourse.julialang.org/t/any-performance-issues-with-multiple-dispatch/55468/6 "2021-02-17T17:18:49Z")

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You will reach a performance penalty if the number of types increase, which can be dealt with manual splitting. There are some threads discussing that, and solutions, for example:

> [@Performance problems when dealing with deliberately type-unstable code. How to use type knowledge better?](https://discourse.julialang.org/t/performance-problems-when-dealing-with-deliberately-type-unstable-code-how-to-use-type-knowledge-better/52535):
>
> I have a problem that crops up again and again, which relates to the use of different concrete types in situations where I can’t possibly establish type stability. Here’s a contrived example: There are three different subtypes, and I have a vector of two of them, which is parameterized with the abstract type. Therefore, the compiler doesn’t know the return type of the function get\_value. But I as the programmer do know that it will always be Float64, because I have set up the logic that way. How…

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