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Julia 1.0 Released After a Six-Year Wait (insidehpc.com)
An anonymous reader quotes InsideHPC:
Today Julia Computing announced the Julia 1.0 programming language release, "the most important Julia milestone since Julia was introduced in February 2012." As the first complete, reliable, stable and forward-compatible Julia release, version 1.0 is the fastest, simplest and most productive open-source programming language for scientific, numeric and mathematical computing. "With today's Julia 1.0 release, Julia now provides the language stability that commercial customers require together with the unique combination of lightning speed and high productivity that gives Julia its competitive advantage compared with Python, R, C++ and Java."
The Register reports: Created by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman, the language was designed to excel at data science, machine learning, and scientific computing.... Six years ago, Julia's creators framed their goals thus:
"We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled...."
In a julialang.org post announcing the milestone, the minders of the language claim to have achieved some of their goals.
The Register reports: Created by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman, the language was designed to excel at data science, machine learning, and scientific computing.... Six years ago, Julia's creators framed their goals thus:
"We want a language that's open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that's homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy. We want it interactive and we want it compiled...."
In a julialang.org post announcing the milestone, the minders of the language claim to have achieved some of their goals.
This is really impressive from the technical standpoint, but I am wondering if they have a Code of Conduct for the language?
Didn't RTFM? Julia has several backends including LLVM. Performance is more than respectable. In particular, puts Python to shame and close to Java + JIT but without the JIT lag, load time and memory footprint annoyance.
When all you have is a hammer, every problem starts to look like a thumb.
It's natively compiled, just not statically compiled.
If compiles optimised versions of your functions as needed. So you can define a function that works on any supported numeric type, and when you call it with doubles, it will compile and cache a version of the code optimised specifically for doubles.
As a result, it is very fast for numeric code, without having to create separate function definitions for each supported type.
My first impression of Julia was favorable. I fired up the command line interpreter and typed in 2 + 2. Prints out 4, just like Python. Then typed in 2**4:
Wow, I like that. I managed to define a function, f(n) = n * n, without reading the manual but didn't get much further without reading the docs docs. But great start, I already see this as a reasonable alternative to what I usually use Python for: a desktop calculator.
Initial impression matters. Now, with a postive one, I will go deeper and see if Julia is really what I should be coding one-offs in, instead of Python or Go.
When all you have is a hammer, every problem starts to look like a thumb.
Cheat sheet
When all you have is a hammer, every problem starts to look like a thumb.
Roughly, one should think of this more as a 'next gen fortran' than 'oh this will replace all your programming'.
Python with numpy has been respectable, but there's room for a language built from the ground up around technical computing.
XML is like violence. If it doesn't solve the problem, use more.
it's just a lie, C++ and Fortran are also used. We can argue if C is "high level" or "medium" level...
Classes are definitely included in the language. Specifically they're included by strong typing and type-specialized functions. A type specialized function is a "class method" that dispatches on more than just the first (usually implicit) argument.
Goto is implemented via the @goto macro.
((lambda (x) (x x)) (lambda (x) (x x))) http://www.endpointcomputing.com a scientific approach to custom computing.
Classes are definitely included in the language.
Julia has no classes
It does have compound types and extensible types (subtypes). Instead of class methods it has parametric polymorphism, a flavor of generics somewhat resembling interfaces. While this seems nice and natural as far as it goes, I am not sure how far it goes. Jury is out.
I absolutely hate the case conventions for identifiers. Why does every language of the month need to come with at least one blatantly stupid, avoidable idiocy guaranteed to trigger a large proportion of its intended audience? This is a minor thing but not something I could ever used to. A prime candidate for "sorry that was dumb, let's just fix it now before it festers".
At least, no significant indents, thanks for that.
When all you have is a hammer, every problem starts to look like a thumb.
The instances where goto is useful can often be boiled down to a few different cases.
IMHO it would be better to have specific language support for those cases than a generic statement. Having a generic goto (or even only a break label-statement) would encourage it to be used for things that it wasn't intended for, and such code would not be as readable.
* Breaking out of nested loops. Something like break label or multibreak number.
* Clause after a loop has reached its end and not broken. Python reuses else for this.
* Error handling: A type of exception handling. It could be simplified by using labels instead of throwing and catching objects of specific types.
"We mustn't be caught by surprise by our own advancing technology" -- Aldous Huxley
A leprechaun riding atop a unicorn handing out buckets of gold.
If they pull it all off in a single language, more power to them.
The multithreading support in Julia 1.0 still says "experimental". So does it have usable threads or not? How good is the thread implementation? Comparable to Java? To C++? Are the standard data structures thread safe? How many spurious locks are there?