![]() GitHub, which will make interactively using PackageCompiler.jl more reasonable. ![]() There’s an open PR currently to make the process of incrementally building images much much faster Faster incremental sysimg rebuilds by Keno People have been using this for ages to bake packages like Plots.jl and DifferentialEquations.jl into a sysimage so that there’s no startup overhead with them. Julia has had ahead of time compilation through PackageCompiler.jl for some time now, it just takes a long time to compile and produces rather large binaries. Note that the compilation does not need to be repeated each session necessarily. Yes, and the compilation itself, which must be repeated in each session, can take a long time. Without that the “script” runs in 3s standalone (including julia startup and compilation), and runs “instantly” the second time if loaded from the same Julia section. (why that specific code takes so much to compile is something that should be figured out by the developers, that is not exactly “normal”).īut yes, being able to develop standalone precompiled libraries and reducing interactive compile time are two of the priorities of the Julia community, and are important to make Julia an alternative for developing programs that must be quick to startup, and also to improve the user experience.Įdit: what takes time in that example in the Julia version is the loading of the plotting library and plotting for the first time. You can also call the precompiled Fortran library from Julia, and then you won’t have the compile time problem for that library while, at the same time, being able to develop other custom parts in Julia, which can be made as fast as possible. Given this speed difference, it can be argued that Fortran (and even more so R and Python/Scipy/scikit-learn, which package Fortran codes such as loess) is better suited for interactive use than Julia I’ll stop engaging now because I don’t want to be seen as a julia zealot and parasite discussions : I just wanted to address what I saw as misconceptions regarding MPI.jl. Of course Fortran has a fifty years leg up, I’m not disputing that. There is also two models here, both with advantages and disadvantages: the Fortran model of ensuring backward compatibility at all costs, and the julia model of making it very clear where compatibility is broken (look up semantic versioning if you’re not familiar) and having tooling to make it easy to freeze a version of the language and libraries. What matters is commitment from developers to ensuring it, and I think the julia community has displayed that. I’ll just note that neither of these models is inherently better for long time stability of code. So it’s not particularly surprising that you like fortran and I like julia. Based on your posts it looks like you like and trust centralized ownership by identified groups and standard commitees I like and trust decentralized development by amateurs working together to interoperate (the extent to which this is true in Julia has to be seen to be believed). I agree, I think there is a cathedral and bazaar/encyclopedia brittanica and Wikipedia type disagreement here.
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