Julia has established itself as the programming language that sits in the intersection
between very fast languages, like C and Fortran, and high abstraction
programming languages, like Python and Matlab. It has been argued
that this solves the 2 language problem where a prototype is programmed
in a high abstraction language and later needs to be re-implemented in a
fast language. In this talk, we will argue that, while Julia does solve the 2
language problem, it also creates the 1.5 language problem where there is a
huge difference between working Julia code and very efficient Julia code. We
will showcase a medium-sized problem to highlight this discrepancy. Finally,
we want to start a discussion in the community on how to solve this problem
by targeting the education of Julia novices towards performance at scale.