Devectorize.jl

Julia's for loops are comparable to C in performance, and its vectorized operations are comparable to Numpy/R, although some cases can be optimized using https://github.com/lindahua/Devectorize.jl (see the benchmarks table)

This is a common refrain: drop down to C, C++, Fortran for the computation intensive parts. It works, but only to a degree. The inefficiencies lie in the vectorization semantics of the host language(s) that leads to extra copies and extra levels of indirection. So this dual language mode of operation typically does not approach what one could have obtained had one disposed the baggage entirely, except for I/O. Usually in the quest for better speed, that is what remains, as one moves progressively larger portions of the application in the C, C++, Fortran part of the code. A reason I like Julia is that I can largely avoid this dual language annoyance, and enjoy the succinctness of pithy vectorized expressions using https://github.com/lindahua/Devectorize.jl