# Allocation and slow down when # of types involved increase (slower than C++ virtual methods)

**URL:** <https://discourse.julialang.org/t/allocation-and-slow-down-when-of-types-involved-increase-slower-than-c-virtual-methods/87656>\
**Category:** Performance\
**Tags:** question, hep\
**Created:** [September 22, 2022, 3:43pm UTC](https://discourse.julialang.org/t/allocation-and-slow-down-when-of-types-involved-increase-slower-than-c-virtual-methods/87656 "2022-09-22T15:43:34Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![stevengj](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/stevengj/32/71_2.png) [@stevengj](https://discourse.julialang.org/u/stevengj)\
**Post date:** [September 22, 2022, 4:02pm UTC](https://discourse.julialang.org/t/allocation-and-slow-down-when-of-types-involved-increase-slower-than-c-virtual-methods/87656/3 "2022-09-22T16:02:02Z")

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Dynamic dispatch is slow in Julia — slower than C++ virtual methods, because C++ method dispatch only depends on a _single_ object type (`this`) and hence can use [vtable](https://en.wikipedia.org/wiki/Virtual_method_table) lookup.

High-performance code in Julia always relies on devirtualizing critical code. This also means that you can’t easily write performant geometry code in Julia by having an array of geometric objects of different types and relying on dynamic dispatch to execute different methods (determined at _runtime_) for different objects — you need to implement a different dispatch strategy.

See also this discussion: [Union splitting vs C++](https://discourse.julialang.org/t/union-splitting-vs-c/61772)

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