The explanation is simply that people are happy to get a 10x speedup even if 100x is possible in theory. Python + NumPy has a large mindshare, and can be 10x f

The explanation is simply that people are happy to get a 10x speedup even if 100x is possible in theory. Python + NumPy has a large mindshare, and can be 10x from Fortran/Julia even if you use NumPy vectorization. And often loops are simpler or required anyway (dropping Python to 1000x slower). Languages like Julia and Fortran also allows you to hit peak where you are limited by memory bandwidth, which is most of the time (not for DGEMM though...)