Yeah, I really like that Julia and LLVM allow applying it on a per-operation basis. Because most of LLVM's backends don't allow for the same level of granulari

Yeah, I really like that Julia and LLVM allow applying it on a per-operation basis. Because most of LLVM's backends don't allow for the same level of granularity, they do end up propagating some information more than I would like. For example, marking an operation as fast lets LLVM assume that it does not result in NaNs, letting nan checks get compiled away even though they themselves are not marked fast: julia> add_isnan(a,b) = isnan(@fastmath(a+b)) add_isnan (generic function with 1 method) julia> @code_llvm debuginfo=:none add_isnan(1.2,2.3) define i8 @julia_add_isnan_597(double %0, double %1) #0 { top: ret i8 0 } meaning it remains more dangerous to use than it IMO should be. For this reason, LoopVectorization.jl does not apply "nonans".