Why Julia?

What is Julia?

The reason to use Julia can be found in the Julia blog: Why we create Julia.

We are greedy: we want more. 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.

From Ref 1.

Julia is a general-purpose, multi-platform programming Language that is Suited for numerical analysis and computational Science Dynamically typed (In static language, expression have types; in dynamic language, values have types. -Stefan karpinski) High performance and just-in-time compiled Using automatic memory management (garbage collection) Composable

Pros & Cons of Julia

Mainly cited from Ref 1.

Julia is a relatively new programming language that overcomes the two-language problem (Ref 2.)

• It provides both "easy-to-use" and "high performance" advantages.
• Growing ecosystem of state-of-the-art application packages allow scientists to expand their research interests.

  Julia is a dynamically typed but is as fast as statically typed languages through JIT compilation.

• This is TRUE in recent scientific research, in which large amount data is available.

  Julia catches two rabbits! -> Satisfy scientists and other developers who require the fascinating features of both dynamically & statically typed languages.

add an image (Ref 3.)

filter(!isempty, readlines(filename))  # strip out empty lines
filter(endswith("*.png"), readdir())   # get PNG files
findall(==(4), [4, 8, 4, 2, 5, 1])     # find every index of the number 4

Julia's powerful features

From Ref 1.

Strong facilities for modularizing and reusing code. A strict type system that helps catch bugs in your code when it runs. A sophisticated system for reducing repetitive boilerplate code (metaprogramming9). A rich and flexible type system that allows you to model a wide variety of problems. A well-equipped standard library and various third-party libraries to handle various tasks. Great string processing facilities. This ability is usually a key selling point for any Swiss-Army-knife-style programming language. It is what initially made languages such as Perl, Python, and Ruby popular. Easy interfacing with a variety of other programming languages and tools.

Examples of Julia applications in various resaerch fields

References

1. [Engheim, E., 2023. Julia as a Second Language, Manning, NY, ISBN 978-16-1729971-1.]
2. Roesch, E., Greener, J. G., MacLean, A. L., Nassar, H., Rackauckas, C., Holy, T. E., Stumpf, M. P. H., 2023. Julia for biologists. Nature Methods, 20: 655-664
3. Nazarathy, Y., 2022. Why use Julia for scientific computing?