Scientific Programming
Julia was initially designed as a language aimed at finding solutions to problems arising from science and mathematics.
Current scripting languages then, and to some extent now, were slow especially when dealing with devectorized (“looping”) code, and resorted to the use of compile code (for example, written in C) to achieve acceptable executing times. This led 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.
We have seen that Julia compiles its sources to the appropriate machine code using just-in-time (JIT) compilation from LLVM and so achieves execution times comparable with those of C and Fortran. Naturally, 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...
