Scientific computing requires the highest computing requirements. Over the years, the scientific community has used dynamic languages, which are comparatively much slower, to build their applications. A major reason for this is that applications are generally developed by physicists, biologists, financial experts, and other domain experts who, despite having experience with programming, are not seasoned developers. These experts always prefer dynamic languages over statically typed languages, which could have given them better performance, simply because they ease development and readability. However, there are now special packages to improve the performance of the code, such as Numba for Python. As the compiler techniques and language design has advanced, it is now possible to eliminate the trade-off between performance and dynamic prototyping. The requirement was to build a language, that is easy to read and code in, like Python, which is a dynamic language and gives the performance of C, which is a statically typed language. In 2012, a new language emerged—Julia. It is a general purpose programming language highly suited for scientific and technical computing. Julia's performance is comparable to C/C++ measured on the different benchmarks available on the JuliaLang's homepage and simultaneously provides an environment that can be used effectively for prototyping, like Python. Julia is able to achieve such performance because of its design and Low Level Virtual Machine (LLVM)-based just-in-time (JIT) compiler. These enable it to approach the performance of C and Fortran. We will be reading more about LLVM and JIT at the end of the chapter. The following quote is from the development team of Julia—the gist of why Julia was created (source: https://julialang.org/blog/2012/02/why-we-created-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. (Did we mention it should be as fast as C?)
Julia is highly influenced by Python because of its readability and rapid prototyping capabilities, by R because of the support it gives to mathematical and statistical operations, by MATLAB (also GNU Octave) because of the vectorized numerical functions, especially matrices, and by some other languages too. Some of these languages have been in existence for more than 20 years now. Julia borrows ideologies from many of these languages and tries to bring the best of all these worlds together, and quietly succeeds too!
Julia is really good at scientific computing but is not restricted to just that, as it can also be used for web and general purpose programming. Julia's development team aims to create a remarkable and previously unseen combination of power and efficiency in one single language without compromising ease of use. Most of Julia's core is implemented in Julia. Julia's parser is written in Scheme. Julia's efficient and cross-platform I/O is provided by the libuv of Node.js.
Some of Julia's features are mentioned as follows:
- It is designed for distributed and parallel computation.
- Julia provides an extensive library of mathematical functions with great numerical accuracy.
- Julia gives the functionality of multiple dispatch. It will be explained in detail in coming chapters. Multiple dispatch refers to using many combinations of argument types to define function behaviors. Julia provides efficient, specialized, and automatic generation of code for different argument types.
- The
Pycallpackage enables Julia to call Python functions in its code and MATLAB packages using theMATLAB.jlpackage. Functions and libraries written in C can also be called directly without any need for APIs or wrappers. - Julia provides powerful shell-like capabilities for managing other processes in the system.
- Unlike other languages, user-defined types in Julia are compact and quite fast as built-ins.
- Scientific computations makes great use of vectorized code to gain performance benefits. Julia eliminates the need to vectorize code to gain performance. De-vectorized code written in Julia can be as fast as the vectorized code.
- It uses lightweight green threading, also known as tasks or coroutines, cooperative multitasking, or one-shot continuations.
- Julia has a powerful type system. The conversions provided are elegant and extensible.
- It has efficient support for Unicode.
- It has facilities for metaprogramming and Lisp-like macros.
- It has a built-in package manager (
Pkg). - It's free and open source with an MIT license.