On Wednesday, 7 September 2016 at 19:19:23 UTC, data pulverizer
wrote:
The "One language to rule them all" motif of Julia has hit the
rocks; one reason is because they now realize that their
language is being held back because the compiler cannot infer
certain types for example:
http://www.johnmyleswhite.com/notebook/2015/11/28/why-julias-dataframes-are-still-slow/
As an avid user of Julia, I'm going to have to disagree very
strongly with this statement. The language is progressing very
nicely and while it doesn't aim to be the best choice for every
programming task imaginable, it already does an excellent job of
letting a scientific programmer such as myself do most of my
workflow in a single language with remarkable performance.
Furthermore, the article you linked pertains to a simple type
inference issue, exposed by the design constraints of a
particular library. While certain design patterns can and often
do lead to Python-style Julia code with optimal performance, you
can always get there by manually enforcing type stability at the
cost of less pretty code.
More to the general point of the discussion, I find that most
scientifically minded users of Python already appreciate some of
the inherent advantages of lower level statically typed languages
and often rather write C/C++ code than descend into the likes of
Cython. D has considerable advantages over C++ in conciseness and
template facilities for achieving zero cost static polymorphism
without descending into utter unreadability. Personally, I find
myself still forced to write most of my non-Julia high
performance code in C++ due to the available libraries and GPGPU
support (especially CUDA), but in terms of language properties
I'd much rather be writing D.
