Julia 1.0 brings API stability to machine learning language

The Julia programming language has gained version 1.0 status and caught the eyes of enterprise software developers building machine learning and data science applications.

Julia 1.0 is an open source programming language for scientific, technical and high-performance computing environments. Work on Julia began in 2009 to solve the "two-language problem," said Viral Shah, CEO of Julia Computing in Cambridge, Mass., one of the co-authors of the language.

The two-language problem has been a conundrum for developers who seek to build computational programs: Go for ease of use and write in languages such as R, Python or MATLAB, or go for performance with Java, C or C++. Most often they do both, Shah said. They build prototypes of a system in R, Python or MATLAB, and turn it over to a systems programmer to rewrite in Java, C or C++ for the performance boost.

Instructors at the University of California, Santa Barbara, told Shah he could not have both ease of use and performance in creating computational systems, he said. So he and Julia co-authors -- Jeff Bezanson, Stefan Karpinski and Alan Edelman -- set out to prove that theory wrong, and borrowed from a host of established programming languages, including C, FORTRAN, Python and Ruby. The two-language problem wastes a lot of time, and it's expensive to pay two separate groups of developers to deliver one product. And the headaches return during upgrades or additions to an existing system.

Viral Shah

Julia stands apart from other commonly used programming languages thanks to its foundation in multiple dispatch, Shah said. Multiple dispatch means the system chooses which programming procedure to invoke at runtime based on the dynamic types of multiple parameters, which improves the performance of Julia programs, he said. The multiple dispatch approach is illustrated in logistics: A package arrives at a warehouse, and the warehouse decides how to route the package based on availability of delivery modes and vehicles.

Julia 1.0 suits highly mathematical programs in a variety of fields, including business, finance, scientific computing and predictions for weather or pricing options. But it's also useful for data science, machine learning and scientific computing, said Kathleen Walch, an analyst at Cognilytica in Washington, D.C..

It combines the best of many languages that both data scientists and data engineers use and the result is a language that is fast, dependable and easily scalable, Walch said. She cited as an example that Julia can process enormous volumes of data quickly and reliably, to help identify outages and network failures in a power grid.

Julia can also enable autonomous vehicles in programs that collect data from sensors in real time and provide quick reactions to obstacles, adjust speed as needed and more, Walch noted. It also has uses in robotics.