I noticed in Julia 4 now if you call A+B where A and B are matrices of
equal size, the llvm code shows vectorization indicating it is equivalent
to if I wrote my own function with an @simd tagged for loop. I still
notice though that it uses a single core to maximum capacity but never
spreads an SIMD loop out over multiple cores. In contrast if I use BLAS
functions like gemm! or even just A*B it will use every core of the
processor. I'm not sure if these linear algebra operations also use simd
vectorization but I imagine they do since BLAS is very optimized. Is there
a way to write an SIMD loop that spreads the data out across all processor
cores, not just the multiple functional units of a single core?
