I am very interested in improving the user-experience around precompilation and performance, may I ask why you are creating a sysimage from scratch?

> I would opt into prebuilt x86_64 generic binaries if Julia had them

The environment varial JULIA_CPU_TARGET [1] is what you are looking for, it controls what micro-architecture Julia emits for and supports multi-versioning.

As an example Julia is built with [2]: generic;sandybridge,-xsaveopt,clone_all;haswell,-rdrnd,base(1)

[1] https://docs.julialang.org/en/v1/manual/environment-variable...

[2] https://github.com/JuliaCI/julia-buildkite/blob/9c9f7d324c94...

I have a monorepo full of Julia analysis scripts written by different people. I want to run them in a Docker container on ephemeral Linux EC2 instances and on user Windows workstations. I don't want to sit through precompilation of all dependencies whenever a new machine runs a particular version of the Julia project for the first time because it takes a truly remarkable amount of time. For the ephemeral Linux instances running Julia in Docker, that happens on every run. Precompiling at Docker build time doesn't help you; it precompiles everything again when you run that container on a different host computer. R and Python don't work like this; if you install everything during the Docker image build, they will not suddenly trigger a lengthy recompilation when run on a different host machine.

I am intimately familiar with JULIA_CPU_TARGET; it's part of configuring PackageCompiler and I had to spend a fair amount of time figuring it out. Mine is [0]. It's not related to what I was discussing there. I am looking for Julia to operate a package manager service like R's CRAN/Posit PPM or Python's PyPI/Conda that distributes compiled binaries for supported platforms. JuliaHub only distributes source code.

[0] generic;skylake-avx512,clone_all;cascadelake,clone_all;icelake-server,clone_all;sapphirerapids,clone_all;znver4,clone_all;znver2,clone_all