Francesco Cattoglio:
Honestly, to my eyes, Julia really looks like a "better
Matlab", with a heckload of stuff packed in his standard
library.
I have not yet experimented with it but I don't like the
premises that much.
In short: I think D language can do as much as Julia can do,
with pretty much same bang for the buck.
Look better, Julia aims also at partially replacing Python as
golden glue in scientific computing, and it seems to have some of
the numbers for it. It's statically typed, it has type
inferencing, a refined type system with multi-methods and more,
and a good LLVM-based JIT (that's in my benchmarks produces a
performance no more than 2-4 times slower than D compiled with
ldc2. If you compile D with dmd Julia is often faster for
FP-heavy code. This means it's much faster than any Python code).
It's better than Matlab about as much as D is better than C, and
it's already better than Python for some things :-) And Julia is
currently much more flexible than D (there's a REPL, lot of
scientific routines in the std lib, and the JIT). In two years
its easy to write code has allowed lot of people to write more
standard library than D community has done in 7 years. For the
kind of purposes Julia is designed for, I don't think D has the
upper hand, it seems D has already lost that race, despite Julia
is rather younger. D remains my preferred for general or heavy
computing.
Bye,
bearophile
