SIMD.jl

This just isn't true. Julia lets you write intrinsics (either LLVM intrinsics or native assembly code) just the same as C. For example, https://github.com/eschnett/SIMD.jl/blob/master/src/LLVM_int....

Julia's SIMD programming model is still very much a work in progress; I think we have a way to go in providing the kind of flexibility and control that languages such as ISPC, Halide, TVM, etc... provide. That being said, packages such as SIMD.jl [0], and LoopVectorization.jl [1] are making fantastic progress, to the point that LoopVectorization forms the basis of a legitimate BLAS contender, in pure Julia [2]. It's not totally there yet, but it's close enough that real work is being done in LV at OpenBLAS-like speeds. As an aside, I find it incredible that these kinds of extensions can be built in packages thanks to the fact that Julia's compiler is extensible enough to allow for direct manipulation of the LLVM intrinsics being emitted by user code. [0] https://github.com/eschnett/SIMD.jl [1] https://github.com/chriselrod/LoopVectorizat

Ah I see, yeah I misunderstood you. > What I did't see was how I could use the AVX2 instructions myself. If you ever find yourself in a situation where you want manual control over vectorization, the package SIMD.jl [1] is pretty good for manual, handwritten vectorization. There's also VectorizationBase.jl [2] which LoopVectorization.jl uses. Which one of these two packages are most appropriate just kinda depends on what sort of interface you prefer. [1] https://github.com/eschnett/SIMD.jl [2] https://github.com/chriselrod/VectorizationBase.jl

Here is how to do explicit SIMD in julia: https://github.com/eschnett/SIMD.jl

Julia uses LLVM to do code-gen so whatever optimizations are available in LLVM are available to Julia. If the auto-vectorizer in LLVM is not kicking in, it is possible to write explicit SIMD with the https://github.com/eschnett/SIMD.jl package.