| The example code in this article is using Zig's portable SIMD features. Similar features are available for C/C++ (GCC/Clang extension) [0], (nightly) Rust [1] and C++26 [2]. All of these provide a similar set of features and you can use normal arithmetic operations (+, -, *, etc) for SIMD vectors. Together with templates/generics you can also write code that can deal with any vector width. These get compiled to LLVM vector types and will generally give you pretty good generated code. This is a very good way of writing basic SIMD code and has the benefit that your code can be compiled to multiple instruction sets. I've been working on a project that can compile down to SSE2, AVX2, AVX-512 and NEON, with just a change of compiler options. Somewhat surprisingly I get the best performance by using 2x the native vector width (ie. f32x16 = 512 bits on 256 bit AVX2), which is kinda like unrolling the loop once. There are some caveats, though. You will need to keep an eye on the generated assembly code to make sure you're on the happy path. You will inevitably need to drop down to ISA specific intrinsics every now and then (for that fast reciprocal square root with `__mm_rsqrt_ps` etc). As an example I needed to do a gather load from an array of fp16's on AVX2, which does not do 16 bit loads. Rust's `Simd::gather_select` takes 64 bit usize as the index but AVX2 doesn't do 64 bit indices. But as long as I did all the index arithmetic in 32 bits and cast to usize at the last second, the compiler did what I wanted. But you need to kinda know what is available in the ISA to stay on the happy path. Not really an issue with arithmetic. I'm sure that an experienced SIMD programmer can get better performance by writing intrinsics manually (say 5-20% better) but I'm already at 3-6x better than the scalar implementation I started with. And you'd have to write (and benchmark) the code for each ISA separately, meaning that you'd spend at least five times more time with it (and have 5x more code to maintain). [0] https://gcc.gnu.org/onlinedocs/gcc-4.6.1/gcc/Vector-Extensio...
[1] https://doc.rust-lang.org/nightly/std/simd/index.html
[2] https://en.cppreference.com/cpp/numeric/simd |