4 ms·

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

• neonsunset 2y ago
  Actually, there's three: C#: https://github.com/dotnet/runtime/blob/main/docs/coding-guidelines/vectorization-guidelines.md 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.tensors.tensorprimitives.cosinesimilarity 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-types 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 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 2y ago
    Not Julia?
    • neonsunset 2y ago
      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.