It is rare that I have to somewhat-contradict Matthew here, but so it goes.
One of my colleagues, Jan Vitek, has studied Julia with a special focus on performance. As many have said, Julia is good at numerics because its compiler can specialize certain cases really well. More generally, it does well when a programmer commits to the effort of writing down performance-enhancing types. [1] BUT, there’s an incredible pitfall associated with this approach. If the programmer is a bit off with the types (not wrt correctness but wrt to narrowness or forgets to specify a type somewhere), the compiler still succeeds except that the program will be rather slow when uses certain paths [2]. So your performance experience with Julia could be quite varied. Use Racket for what you have in mind. It’s obviously the superior language :-) — Matthias p.s. If there’s interest, I can dig out Jan’s draft paper. [1] Julia is gradually typed but it should really be considered optionally typed. The types are about performance, so they are more like C types than ML type.s [2] Julia’s compiler is tailor-made for an application of Vincent St-Amour’s dissertation research on feature-specific profiling. -- You received this message because you are subscribed to the Google Groups "Racket Users" group. To unsubscribe from this group and stop receiving emails from it, send an email to racket-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
