There is some reason in this comparison. You might want to answer the question: "if I pick the common approach to matrix multiplication in the world of Data Science (numpy), how far off is the performance from some potential ideal reference implementation?"

I actually do have that question niggling in the back of my mind when I use something like NumPy. I don't necessarily care exactly _where_ the overhead comes from, I might just be interested whether it's close to ideal or not.