Which is why I've never quite understood the appeal of portable SIMD libraries for performance-critical code. If I'm explicitly writing SIMD rather than relying on the auto-vectorizer, it's usually because I want access to the particular capabilities of the target ISA.

For many problems, choosing the right instruction or instruction sequence makes a large difference. Portable SIMD abstractions necessarily expose some common semantic layer, but SIMD ISAs don't actually have equivalent capabilities. Instructions like pshufb, for example, enable algorithmic tricks that don't necessarily have an equally efficient analogue on another architecture.

If maximum performance matters, I generally want intrinsics and architecture-specific implementations; if portability matters more, I'd rather move further up the abstraction stack and use something designed to target multiple architectures, such as ISPC. There are certainly cases where portable SIMD gets close enough to optimal, but I don't think there's a compiler or abstraction that can express every useful SIMD idiom and lower it equally efficiently across fundamentally different ISAs.