Switch from PackageCompiler to JuliaC without trimming#2761
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They need to be the same version. And we need the latest for Julia 1.12 support. Now get juliacall from PyPI, since the latest on conda-forge is v0.9.28. Taken out of #2761.
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They need to be the same version. And we need the latest for Julia 1.12 support. Now get juliacall from PyPI, since the latest on conda-forge is v0.9.28. Taken out of #2761.
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This is #2761 but staying on PackageCompiler, not moving to JuliaC yet. It seems most of the issues are related to that part, so better hold off on that and let it mature so we can at least work on the latest Julia.
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Nice! This is pretty quick to build locally and it's the first time I could just run the executable without first moving dylibs around. |
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Trying #2629 again now that we are some patch releases further along.
EDIT: the switch to Julia v1.12 is now done with #2815, I rebased this to only add the JuliaC switch, which is less urgent.
For JuliaC we use this branch: JuliaLang/JuliaC.jl#47. That should set the desired CPU_TARGET, which was the main reason for reverting the previous attempt.
Status: I can run the TeamCity JuliaC generated binaries locally, so the CPU_TARGET seems to work. The Linux binaries seem to for some models. The libribasim.dll file grew from 0.9 to 1.2 GB.
Update: this now works well on Windows. There is a libribasim.dll.a added to the zip root that we perhaps should remove, but the Windows build went from 1h40m to 1h8m, and testing the binaries from 20m to 17m as well so latency doesn't seem increased. I ran the Windows binaries locally.
The binary wasn't working directly for me on Linux only after fiddling with the load path, we need to fix that, perhaps in main.rs or with a JuliaC rpath option or such. EDIT: see JuliaLang/JuliaC.jl#109
Details, now fixed with rpath=`@bundle`
It seems the default of the JuliaC LinkRecipe is the magic string
@julia, whereas we need the magic string@bundle.