Despite taking several years to become fully popularised, Python continues to dominate the programming sphere thanks to its clean and relatable syntax, readability, and ease of learning for beginners.
However, the most common complaint among users is that Python is slow. Slower than C++, slower than Java, and slower than C#.
It’s also slower than Julia, a high-performing, relatively new kid on the block which was released in 2012.
Conceived by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman as a free language that’s both fast and high-level, Julia is as easy to use as Python or R, as fast as C or Fortran, and removes the need to manoeuvre between two languages as it can be used for both prototyping and production.
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Benefits of Julia for devs
In its first 10 years, the Julia community and ecosystem has grown substantially, and now has over 11.8 million lines of code.
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Its latest version, 1.10.4, which was updated on 4 June 2024, boasts several new features including improved package load time, better error messages, and an improved stack trace rendering.
It also has a rich type system that facilitates the definition of complex and efficient data structures.
Add to that its ease of use—Julia’s syntax is straightforward and intuitive, similar to that of Python, meaning it can be used extensively for exploratory programming and data analysis.
It’s also highly accessible for those familiar with other high-level languages. And as it uses multiple dispatch as a core feature, it’s extremely flexible and can be used for data science, machine learning, artificial intelligence, scientific research, and financial modelling.
A new approach to machine learning
OpenAI’s ChatGPT uses algorithms to process large datasets, however, Julia differs in that it can be used in scientific machine learning, where algorithms are fed scientific knowledge to solve complex scientific equations.
This language can also work to quantify the value of complex constants and align machine learning more closely with real-world applications. It’s already being used by logistics and drone delivery company Zipline to find the best flight paths to deliver pharmaceutical products more efficiently.
Big pharma, including AstraZeneca and Pfizer, have both used Julia to accelerate simulations of new therapies. Julia’s cloud-based platform, Julia Computing, has also been adopted by F1’s Williams Racing to power its modelling and simulation software and improve speed.
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Tapping into the community
As an open-source language, Julia isn’t affiliated with one company or operating system. As a result, its software is available freely and the Julia community of developers is active in developing the language and fixing bugs—you can find all the community forums on the JuliaLang website.
There is also a yearly event, JuliaCon, where developers, researchers, and experts in the field come together to explore Julia’s capabilities and how it’s advancing. Alongside keynotes, speakers, and a hackathon, attendees can also participate in workshops and technical talks.
When it comes to learning Julia, the more proficient you are in other programming languages, the better. That said, universities including MIT and TU Berlin offer online courses, as well as online learning platforms DataCamp, Coursera, and Udemy.
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