vscode-clangd VS Halide

Thanks for your reply, maybe I should go to vscode-clangd community to try to find out how they implement this:)

But I will say that I am mostly happy with either one of the clangd and ccls plugins. The clangd plugin is nicely packaged with a self-installer. The ccls plugin takes a little more work to set up, but it has loads more features. Both are quite good at working with a proper AST. Both build the AST by actually compiling your code with libclang (llvm's compiler core). Both work reliably for me because I am able to provide a compile_commands.json using my project builder (ninja). Nice as it is, it does not update automatically, and I'm not sure I want it to.

For C# there is https://github.com/muhammadsammy/free-omnisharp-vscode for Python there is https://github.com/Microsoft/pyright (Pylance is the proprietary counterpart) for C++ there is https://github.com/clangd/vscode-clangd

If CPU/GPU execution speed is the goal while simultaneously code golfing the source size, https://halide-lang.org/ might have come in handy.

This is a task where Halide https://halide-lang.org/ could really shine! It disconnects logic from scheduling (unrolling, vectorizing, tiling, caching intermediates etc), so every step the author describes in the article is a tunable in halide. halide doesn't appear to have bindings for golang so calling C++ from go might be the only viable option.

Probably would have been much easier to do 15 times in https://halide-lang.org/

The idea behind Halide is that scheduling memory access patterns is critical to performance. But, access patterns being interwoven into arithmetic algorithms makes them difficult to modify separately.

So, in Halide you specify the arithmetic and the schedule separately so you can rapidly iterate on either.

It is not the sorting per-se which was improved here, but sorting (particularly short sequences) on modern CPUs with really the complexity being on the difficulty of predicting what will work quickly on these modern CPUs.

Doing an empirical algorithm search to find which algorithms fit well on modern CPUs/memory systems is pretty common, see e.g. FFTW, ATLAS, https://halide-lang.org/

Halide https://halide-lang.org/

It doesn’t apply in this case, but in general if you really want the best vectorization I would suggest using https://halide-lang.org instead of trying to coerce your compiler.

If we drop the "APL" requirement, wouldn't Halide fit your criteria for the third?