I don’t know, it depends on the specific library. If they were compiling for least-common denominator like SSE2, recompiling with -march=native might make a di

I don’t know, it depends on the specific library. If they were compiling for least-common denominator like SSE2, recompiling with -march=native might make a difference. The exact amount of that difference depends heavily on the code. Automatic vectorizers are limited, they only generating good vectorized code for pure vertical operations which handle long dense arrays of FP32 or FP64 values. OTOH, some widely-used libraries like Eigen https://eigen.tuxfamily.org/index.php?title=Main_Page https://eigen.tuxfamily.org/index.php?title=Main_Page are using manually-written intrinsics under the hood, selecting an implementation with preprocessor macros. If your R library uses Eigen, compiling for AVX2 or AVX512 will probably cause a non-trivial performance win. If your library uses runtime dispatch to select best implementation in runtime, and is compiled recently (i.e. you don’t have new instruction sets in your CPU which weren’t available back then), you’re unlikely to get much profit. BTW, I’ve noticed C standard library implemented by VC++ uses runtime dispatch, functions like memcpy() are using AVX vectors internally, when supported.