One of the facts about scientific programming is that it requires high performance flexible dynamic programming language. Unfortunately, to a great extent, the domain experts have moved to slower dynamic programming languages. There can be many good reasons for using such dynamic programming languages and, in fact, their use cannot be diminished as well. On the flip side, what can we expect from modern language design and compiler techniques? Some of the expectations are as follows −
It should eradicate the performance trade-off.
It should provide the domain experts a single environment that is productive enough for prototyping.
It should provide the domain experts a single environment that is efficient enough for deploying performance-intensive applications.
The Julia programming language fulfills these expectations. It is a general purpose high-performance flexible programming language which can be used to write any application. It is well-suited for scientific and numerical computing.
Let us see the history of Julia programming language in the following points −
Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman has started to work on Julia in 2009.
The developer’s team of above four has launched a website on 14th February 2012. This website had a blog post primarily explaining the mission of Julia programming language.
Later in April 2012, Stefan Karpinski, in an interview with a magazine named InfoWorld, gave the name “Julia” for their programming language.
In 2014, the annual academic conference named ‘The JuliaCon’ for Julia; users and developers has been started and since then it was regularly held every year.
In August 2014, Julia Version 0.3 was released for use.
In October 2015, Julia Version 0.4 was released for use.
In October 2016, Julia Version 0.5 was released for use.
In June 2017 Julia Version 0.6 was released for use.
Julia Version 0.7 and Version 1.0 were both released on the same date 8th August 2018. Among them Julia version 0.7 was particularly useful for testing packages as well as for the users who wants to upgrade to version 1.0.
Julia versions 1.0.x are the oldest versions which are still supported.
In January 2019, Julia Version 1.1 was released for use.
In August 2019, Julia Version 1.2 was released for use.
In November 2019, Julia Version 1.3 was released for use.
In March 2020, Julia Version 1.4 was released for use.
In August 2020, Julia Version 1.5 was released for use.
Following are some of the features and capabilities offered by Julia −
Julia provides us unobtrusive yet a powerful and dynamic type system.
With the help of multiple dispatch, the user can define function behavior across many combinations of arguments.
It has powerful shell that makes Julia able to manage other processes easily.
The user can cam call C function without any wrappers or any special APIs.
Julia provides an efficient support for Unicode.
It also provides its users the Lisp-like macros as well as other metaprogramming processes.
It provides lightweight green threading, i.e., coroutines.
It is well-suited for parallelism and distributed computation.
The coding done in Julia is fast because there is no need of vectorization of code for performance.
It can efficiently interface with other programming languages such as Python, R, and Java. For example, it can interface with Python using PyCall, with R using RCall, and with Java using JavaCall.
Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and Alan Edelman, the core designers and developers of Julia, have made it clear that Julia was explicitly designed to bridge the following gap in the existing software toolset in the technical computing discipline −
Prototyping − Prototyping is one such problem in technical computing discipline that needs a high-level and flexible programming language so that the developer should not worry about the low-level details of computation and the programming language itself.
Performance − The actual computation needs maximum performance. The production version of a programming language should be often written in “Fortran” or “C” programming language.
Speed − Another important issue in technical domain is the speed. Before Julia, the programmers need to have mastery on both high-level programming (for writing code in Matlab, R, or, Python for prototyping) and low-level programming (writing performance-sensitive parts of programs, to speed up the actual computation, in statistically complied languages such as C or Fortran).
Julia programming language gives the practitioners a possibility of writing high-performance programs that uses computer resources such as CPU and memory as effectively as C or Fortran. In this sense, Julia reduces the need for a low-level programming language. The recent advances in Julia, LLVM JIT (Low Level Virtual Machine Just in Time) compiler technology proves that working in one environment that has expressive capabilities and pure speed is possible.
One of the goals of data scientists is to achieve expressive capabilities and pure speed that avoids the need to go for ‘C’ programming language. Julia provides the programmers a new era of technical computing where they can develop libraries in a high-level programming language.
Following is the detailed comparison of Julia with the most used programming languages — Matlab, R, and Python −
MATLAB − The syntax of Julia is similar to MATLAB, however it is a much general purpose language when compared to MATLAB. Although most of the names of functions in Julia resemble OCTAVE (the open source version of MATLAB), the computations are extremely different. In the field of linear algebra, Julia has equally powerful capabilities as that of MATLAB, but it will not give its users the same license fee issues. In comparison to OCTAVE, Julia is much faster as well. MATLAB.Jl is the package with the help of which Julia provides an interface to MATLAB.
Python − Julia compiles the Python-like code into machine code that gives the programmer same performance as C programming language. If we compare the performance of Julia and Python, Julia is ahead with a factor of 10 to 30 times. With the help of PyCall package, we can call Python functions in Julia.
R − As we know, in statistical domain, R is one of the best development languages, but with a performance increase of a factor of 10 to 1,000 times, Julia is as usable as R in statistical domain. MATLAB is not a fit for doing statistics and R is not a fit for doing linear algebra, but Julia is perfect for doing both statistics and linear algebra. On the other hand, if we compare Julia’s type system with R, the former has much richer type system.