If you have heard about Julia programming language, you must be aware that it has been endorsed by the MIT (Massachusetts Institute of Technology) as the next big programming language. Well, although it is yet to make it to the top ranks of the most popular programming languages; Julia is being used by developers for its many advantages. This is evident from its adoption by several major companies, like Netflix, Aviva, and BlackRock; as well as hundreds of research institutions worldwide.
So when was this language developed? Who are the brains behind this fast-computing software environment? Let’s explore.
The evolution of Julia as a programming language
Julia was developed and incubated at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) by Jeff Bezanson, Stefan Karpinski, Viral Shah, and Alan Edelman. The work on Julia began in 2009, and the language itself was launched in 2012. Ever since, it has grown very speedily, with version v1.2 released on 20 August 2019 and the latest preview version 1.3 available on 4 October 2019.
The premise behind developing Julia was to build an open-source language that is free to implement, and usable for general programming. The developers of Julia wanted dynamic numerical software that addresses the shortcomings of Python and other languages applied in scientific computing and data processing. So Julia was devised as a language that combines the speed of C with the flexibility of Ruby, the mathematical prowess of Matlab, the macros of Lisp, the functionality of R and Python, the easy string processing of Perl, and the speed of Java and C++. What the founders conjured up was a high-end programming language with a robust quantitative environment suited for ‘Numerical Computation’, and a speed that makes it one of the best software for statistical analysis and Big Data problems. The computing speed of Julia supports cloud computing and parallel computing, with applications experiencing faster running times than ever before.
Today, the success of Julia is visible in its more than four million downloads, and 2400 Julia packages for community use. These include mathematical libraries, data manipulation tools, and packages for general-purpose computing. Additionally, libraries from Python, R, C/Fortran, C++, and Java can be used. What’s more, it has a vibrant community from scientific computing, statistics and data-science worlds that is enriching the Julia ecosystem more and more.
The prolific use of Julia software for data visualization, general-purpose coding and deployment, data science, numerical and scientific computing, scalable machine learning, and parallel computing at every level; has led to a steep rise in the adoption of Julia as the language for development.
A Julia community called Julia Stewards manages the Julia contributory ecosystem, and the company Julia Computing provides paid support and services to institutions and enterprises that wish to use Julia.
What is Julia?
Julia is a comprehensive programming language, with procedural programming, object-oriented programming, meta-programming, functional programming, numerical computations, parallel computing, and much more. As numerical software, it is well suited for heavy numerical computations. At the same time, its programmatic simplicity makes it well suited for most application development that involves parallelism and high computing. The capability of Julia programs to compile to efficient native code for multiple platforms and be deployed on large clusters quickly has been implemented for large-scale supercomputer simulations as well.
Data visualization is an important characteristic of Julia.
With a built-in package manager, integrated mathematical functions, and the ability to generate code automatically; Julia remains a proven, simple-to-learn language that is both interactive and compiled.
Julia and Python
The development environment in Python has many libraries, tools, and applications that streamline scientific computing and data analysis. However, it is not considered fast enough for advanced machine learning, big-scale linear algebra, deep analytics, or distributed and parallel computing in a multi-platform dynamic environment.
This is where Julia programming language comes in. It is fast, without the aid of external libraries, tools or compilers.
Julia has a numerical syntax, making it easy for programming newbies to use the math operations.
Julia has an easy-to-use parallelization syntax that supports high-level parallel computing across multiple cores. Moreover, it is very easy to convert code from Python and/or C to Julia. Model output readability is also easier in Julia, compared to Python.
At the same time, Python has some undeniable advantages over Julia. The 1-indexing of Julia arrays is a deterrent for general-use developers with traditional programming habits. Python is a well-developed mature language that has existed for 30 years. So, the extent of third-party packages available for Python and an extensive community, make it easy for developers to use Python rather than Julia.
Although Python is getting faster, it does not measure up to Julia for technical computing at very high speeds. So whether you use Python or Julia, you may have to do a trade-off when the need for speed exceeds the availability of packages. Developers are however walking the middle path – learning and implementing both, Python and Julia; making the most of the ability to transpose Python codes into Julia.
Features of Julia programming language
Julia is a unique programming language as it facilitates the writing of high-performance code without the need for another language.
Other features of Julia that make it an easily usable, deployable and scalable language are as follows:
It is Fast: Julia programs compile to efficient native code for multiple platforms via LLVM, for high-performance.
Typed: Julia is strongly typed, which means that every object or variable can be defined, allowing for fine integration with Julia’s just-in-time (JIT) compiler.
Creates fast running codes: As Libraries and primitive operations are written in Julia itself, it enables fast running code for easy implementation.
Dynamic: Julia can be used in a wide range of problems. As a dynamically typed language, it feels like a scripting language and can be used interactively for optimization, documentation, and dispatch.
Multiple-dispatch: Julia uses multiple-dispatch to invoke functions, which allows the user to choose from different programming patterns adapted to the application.
High-level syntax: Julia has a powerful, expressive syntax well-matched to mathematical operations, with numeric data types. This makes it a very simple-to-learn language for a user with any background.
Compiled: Julia is JIT compiled using the LLVM compiler framework, and not interpreted, and this gives a faster runtime performance,
Interactive: It has a REPL (read-eval-print loop) interactive command line, which enables fast one-off scripts and commands.
Code conversion: It is easy to convert code from Python and/or C to Julia. Julia can also interface with external libraries written in C, R, Python, Matlab, C, C++ or Fortran; either directly or through packages, and without wrappers or special APIs.
Metaprogramming. With Lisp-like macros, Julia can generate other programs, and even modify their own code while running.
Full-featured debugger: Julia executes code in a local REPL and allows you to debug, inspect and interpret the codes.
Type inference Even when the user does not specify all variable types in a code, Julia will infer the types and execute the code using these types. This makes Julia very easy to learn, especially for those new to coding.
Parallelism and distributed computing: Julia is designed for parallelism, and provides built-in primitives for parallel computing at every level.
Highly scalable: It can be deployed on large clusters quickly, which is critical when working with Big Data across a distributed system.
Extended library and third-party packages: Julia also has a built-in package manager.
Supports Unicode: It has good support for Unicode, including but not limited to UTF-8
Shell-like ability: Julia has a powerful interface to manage processes.
Ecosystem: The numerical software is supported by many tools and frameworks in concurrency, for various implementations. For instance: data visualization and plotting, data science, scalable machine learning, scientific computing, domain-centric ecosystems, parallel computing and general development of applications.
How Julia is used by Developers, Data Scientists, and Machine Learning Practitioners
Julia has a large community of enthusiastic developers and contributors from various industries and job roles, making this a richer ecosystem by the day.
Some of the application areas are mentioned herein, for an idea of how and where Julia is being implemented.
Used for Development: Build, Deploy or Embed Code
The many robust features of Julia and the functionality to write UIs, compile code statically, use it interoperable with Python or C, manage other processes and deploy on a web server; have made it a favorite of developers who want to use the best of programming languages for their work. The metaprogramming facilities and high-performance; are other reasons why courses are including Julia programming language in the Developer learning curve.
Used for Data Science
Julia is highly intuitive, with speed more than R or Python. This makes it a compelling language for data science, where it’s distributed and parallel high computing can be leveraged in a Big Data environment. Multidimensional datasets can be loaded quickly, to perform aggregations, joins and preprocessing operations in parallel. Computations on streaming data support real-time analysis. The ability to work with most databases and integrate with the Hadoop ecosystem is another big plus, that renders it highly useful in data science.
Used in Machine Learning
Julia offers powerful tools for scalable deep learning, machine learning, and AI. The richly developed mathematical syntax makes it most-suited for algorithms and build training models. As its statistics ecosystem can compute generalized linear models, decision trees, or clustering, Julia is fast emerging as a must-learn language for machine learning engineers.
Use Cases
Besides, machine learning, data science, and development; Julia programming language has witnessed applications in the following:
- Astronomy
- Ecology
- Operations Research
- Self-driving cars
- 3-D printers, Image Processing
- Precision medicine
- Augmented reality
- Genomics
- Risk management
- Robot navigation and movement
Who should learn Julia Programming Language and Why
Julia is a highly useful language for coding and deployment of web applications that integrate with DevOps processes. So, developers, coders, and app builders are learning Julia to use in conjunction with other languages.
As a dynamic computational tool, Julia is also becoming indispensable for data scientists, machine learning practitioners, statisticians and finance professionals who want the dynamics of a fast language for numerical analysis in parallelism.
So if you want to reap the vast career opportunities in IT or data science, learn Julia for that working edge. Its simplicity makes it easy to learn, and you can add another language to your arsenal for higher career growth and salaries.