# Blog post about my experiences with Julia

**URL:** https://discourse.julialang.org/t/blog-post-about-my-experiences-with-julia/79976
**Category:** Community
**Created:** [April 25, 2022, 8:19am UTC](https://discourse.julialang.org/t/blog-post-about-my-experiences-with-julia/79976 "2022-04-25T08:19:00Z")
**Posts on this page:** 1
**Showing post:** 234

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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: [May 2, 2022, 8:14am UTC](https://discourse.julialang.org/t/blog-post-about-my-experiences-with-julia/79976/234 "2022-05-02T08:14:28Z")

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> [@paulmelis](#):
>
> That’s why I think just downloading Julia on a supercomputer and running it will not immediately give you all the performance you want, especially when running large multi-node jobs.

Why? Keep in mind that Julia is a compiler, the performance of the code it generates doesn’t depend on how it was compiled. There can be a little improvement in the performance of Julia’s runtime, but the benchmarks in [Compiling Julia using LTO+PGO - #5 by stabbles](https://discourse.julialang.org/t/compiling-julia-using-lto-pgo/39168/5) showed negligible speedup when compiling Julia and _all_ of its dependencies with `-march=native`. You mention large multi-node jobs, but if you’re referring to MPI, the package `MPI.jl` does dynamic loading, so compiling Julia locally doesn’t have any advantages compared to using a prebuilt generic binaries of Julia. Or were you thinking of something else?

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