The Julia community has released Julia 0.4.0 with major language refinements and numerous standard library improvements. Julia 0.5.0 is now in preview with nightly builds. It will bring further, and breaking, improvements to core array functionality.

Julia is a dynamic language for technical computing that is especially good at running MATLAB and R-style programs. An open source language, Julia was first publicized in 2012 although development began on it in 2009 by MIT professor of Computer Science Alan Edelman with Jeff Bezanson, Stefan Karpinski, Viral B. Shah.

Back in 2012 its creators explained Why We Created Julia, saying: 

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.

Julia has built a strong community and there are now over 700 registered packages including: 

• JuliaStats - for statistics and machine learning 
• JuliaOpt - optimization packages
• JuliaQuantum -  libraries for quantum-science and technology
• JuliaGPU - GPU libraries and tooling
• IJulia - notebook interface built on IPython
• Gadfly - Grammar of Graphics-inspired statistical plotting

There is also a free open source IDE, Julio Studio, from Forio, a company that creates software for simulations, data explorations, and predictive analytics and promotes Julia.

One of the features of the new stable release, Julia 0.0.4 is Generated functions, also referred to as "staged functions" which introduce finer control over compile-time specialization. This innovation is the subject of this talk from JulaiCon 2015 presented by Jake Bolewski:

Other compiler and languages features listed on the release announcement as "notable are:

• Incremental code caching for packages, resulting in a major reduction in loading time for Gadfly and other large, inter-dependent packages.
  
  
• Generational garbage collector which greatly reduces GC overhead for many common workloads.
  
  
• Function call overloading for arbitrary objects
  


• Support for documenting user functions and other objects and retrieving the documentation via the help system
  
  
• Improvements in the performance and flexibility of multidimensional abstract arrays, SubArrays (array views), and efficient multidimensional iterators.
  
  
• Inter-task channels for faster communication between parallel tasks
  
  
• Tuple type improvements: the type tuple (A,B) now written Tuple{A,B}. This change has improved the performance of many tuple-related operations, and allowed one to write fixed-size aggregate fields as field::NTuple{N,T} (Number of elements of given Type).
  
  
• Major improvements in Julia’s test coverage and the ability to analyze the test coverage of packages
  
  
• The command line (REPL) now supports tab-completion of emoji characters (common LaTeX symbols have been supported since 0.3!)

Attention is now focused on an issue referred to Arraypocalype Now which will bring new functionality but involves some breaking changes. Matt Bauman who authored this issue says on its GitHub page that "There is definitely more than enough work to go around!" and that "Anyone is welcome to dig in!"

More Information

Julia

Julia 0.4 Release Announcement

JuliaLang on GitHub

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Julia Studio - An IDE For Julia

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