[neonsunset]

SIMD situation in Go is still rather abysmal, it’s likely easier to just FFI (though FFI is very slow so I guess you're stuck with ugly go asm if you are using short vectors). As usual, there's a particular high-level language that does it very well, and has standard vector similarity function nowadays...

[bitexploder]

Go ahead and share the language, it is good etiquette for HN :)

[neonsunset]

Actually, there's three:

C#: https://github.com/dotnet/runtime/blob/main/docs/coding-guid... (and full family of other types: Vector2/3/4, Matrix3x2/4x4 and upcoming Tensor<T>), vector similarity I was talking about is this: https://learn.microsoft.com/en-us/dotnet/api/system.numerics..., it uses a highly optimized SIMD kernel, for DotProduct just use an adjacent method

Swift: https://developer.apple.com/documentation/swift/simd-vector-... it is also a competent language at portable SIMD by virtue of using LLVM and offering almost the same operators-based API (e.g. masked = vec1 & ~vec2) like C#

Mojo: https://docs.modular.com/mojo/stdlib/builtin/simd which follows the above two, it too targets LLVM so expect good SIMD codegen as long the lowering strategy does it in an LLVM-friendly way, which I have not looked at yet.

[e12e]

Not Julia?

[neonsunset]

Julia is less "general-purpose" and I know little about the quality of its codegen (it does target LLVM but I haven't seen numbers that place it exactly next to C or C++ which is the case with C#).

Mojo team's blog posts do indicate they care about optimal compiler output, and it seems to have ambitions for a wider domain of application which is why it is mentioned.