Julia was initially designed as a language directed to find solutions to the problems arising from science and mathematics. The current scripting languages then, and to some extent now, were slow especially when dealing with 'looping' code (devectorized) that is normally the algorithmic approach that the analyst will take in his/her blocked pseudo-coding.

This leads to the 'two-language' approach where analysis is made using the scripting language, whereas code needs to be compiled into a second language, usually C, in order to achieve enterprise performance.

Julia compiles its sources to the appropriate machine code using just-in-time compilation from LLVM and so achieves execution times comparable with those of C and Fortran.

It is natural that the applications of Julia in the fields of scientific programming are many and varied and in a single chapter I can do no more than point the reader to some of the more elemental examples. However, in a fashion similar...