# Potential performance regressions in Julia 1.8 for special un-precompiled type dispatches and how to fix them

**URL:** https://discourse.julialang.org/t/potential-performance-regressions-in-julia-1-8-for-special-un-precompiled-type-dispatches-and-how-to-fix-them/86359
**Category:** Performance
**Tags:** performance
**Created:** [August 25, 2022, 8:27pm UTC](https://discourse.julialang.org/t/potential-performance-regressions-in-julia-1-8-for-special-un-precompiled-type-dispatches-and-how-to-fix-them/86359 "2022-08-25T20:27:47Z")
**Posts on this page:** 1
**Showing post:** 2

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### Author: ![giordano](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/giordano/32/2166_2.png) [@giordano](https://discourse.julialang.org/u/giordano)
#### Post date: [August 25, 2022, 8:53pm UTC](https://discourse.julialang.org/t/potential-performance-regressions-in-julia-1-8-for-special-un-precompiled-type-dispatches-and-how-to-fix-them/86359/2 "2022-08-25T20:53:11Z")

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The only known effect in Julia v1.8 I’m aware of is that loading time of packages may increase a bit because more code is cached, which should however result in overall slightly faster TTFX. I’ve seen this in a few small packages.

What you’re observing instead sounds similar to

> [@Help building MWE for 1.8 latency regression](https://discourse.julialang.org/t/help-building-mwe-for-1-8-latency-regression/84796):
>
> Motivated by the SnoopPrecompile announcement, I checked out my package’s TTFX on 1.8rc3. Using a sample script like: # test\_startup.jl @time using CMBLensing @time LenseFlow(FlatMap(randn(10,10))/1e6) \* FlatMap(randn(10,10)) I get about 2X slower on 1.8 cry $ julia +1.7.3 test\_startup.jl 3.949029 seconds (13.52 M allocations: 844.374 MiB, 7.40% gc time, 47.93% compilation time) 5.599200 seconds (20.76 M allocations: 1.123 GiB, 4.16% gc time, 98.32% compilation time) $ julia +1.8.0-rc3…

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