The problem with MATLAB is that idiomatic MATLAB style (every operation returns a fresh matrix) can easily become very inefficient: it leads to countless heap memory allocations of new matrices, resulting in low data-access locality, i.e. your data is needlessly copied around in slow DRAM all the time, rather than being kept in the fastest CPU cache.

Julia's MATLAB-inspired syntax is at least as nice, but the language was from the ground up designed to enable you writing high-performance code. I have seen numerous cases where code ported from MATLAB or NumPy to Julia performed well over an order of magnitude faster, while often also becoming more readable at the same time. Julia's array-broadcast facilities, unparalleled in MATLAB, are just reason for that. The ubiquitous availability of in-place update versions of standard library methods (recognizable by an ! sign) is another one.

In our group, nobody has been using MATLAB for nearly a decade, and NumPy is well on its way out, too. Julia simply has become so much more productive and pleasant to work with.

https://julialang.org/

https://docs.julialang.org/en/v1/manual/noteworthy-differenc...