Show HN: A physically-based GPU ray tracer written in Julia (makie.org)

The vendor-agnostic GPU approach via KernelAbstractions is great to see. The Vulkan compute path is underrated for this — it runs on AMD, NVIDIA, and Intel without needing ROCm or CUDA, just whatever driver ships with the GPU.

Re: the compilation latency discussion — it's a real tension. JIT gives you expressiveness but kills startup. AOT gives you instant start but limits flexibility. Interesting that most GPU languages went JIT when the GPU itself runs pre-compiled SPIR-V/PTX anyway.

Nope. Funnily enough no one can agree on why - if you ask five people, you get six answers.

My own take is that Julia didn’t since the two language problem as much as was defeated by it.

Julia didn’t attract the high-level Python data science crowd because of Julia’s latency issue, lack of package ecosystem, and the inconveniences that a high performance compiled language incurs, such as having parametric containers.

The research software engineer crowds didn’t buy in because Julia has no interfaces or automatically checkable behavior, poor static tooling, imprecise semantics which is hard to build abstraction on, and a complex performance model that makes it hard to ensure speed, and is hard to deploy.

So, where they tried to make a language that can span the gap, they succeeded in making a language that works for neither, and which no-one wants.

I like the language. But after having used it for eight years, I find it increasingly hard to argue against the point that it’s better to choose Rust for software engineering and Python for scripting.

Edit: I should say: I used it for eight years because it IS fine for my specific niche: High performance research software engineering. Where I care neither about the convenience of Python, nor need to write truly robust and maintainable code. Where my choice of language was personal and I didn’t need to convince a team of coworkers.

I think there's quite a bit of "quiet work" going on that isn't very visible. Personally I've been happily using Julia for work everyday for years. When the language was younger there was "big" updates that were news worthy, now that slowed down, but it seems there's a decent number of people using the language for serious work that is just a bit too specialised for general interest. E.g. in recent registered packages there's a simulation of earth, a method to analyse EEG recording, or a method to measure loudness.

https://github.com/NumericalEarth/NumericalEarth.jl

https://github.com/Marco-Congedo/Xloreta.jl

https://github.com/slink/ZwickerLoudness.jl

i hope you realize this is purely because julia uses LLVM and LLVM has backends for those targets (noticeably absent are GPUs which do not have LLVM backends). any other language which uses LLVM could do the same exact same thing (and would be hampered in the exact same way).

This was supposed to be under a different post. Not the main article. Whops.