Another substantial transition that may be required is that Julia is not directly object-oriented since objects do not directly have embedded methods instead, a given object must be passed to a function to be modified. Thus, special care must be taken when switching between languages in addition to ensuring consistent strategies for speeding up code. Furthermore, Julia uses column-major ordering (like Matlab, Fortran, and R) unlike Python and C (row-major ordering), making iterating column-by-column substantially faster. However, because much of its functionality is based on MATLAB, similar issues and their corresponding solutions bleed over.Īdditional transition issues that may be encountered is that unlike Python, Julia’s indexing begins with 1. There is not nearly the base of documentation that I am accustomed to nearly every issue imaginable in the Python world has been explored on Stack Overflow, Julia is much more limited.
One of the most obvious downsides to Julia when compared to Python is the relatively small community.
Beyond being able to utilize Jupyter Notebook commands, Julia has a range of code diagnostic tools, such as a command to inform the user of wall time and memory allocation for a function. Julia has relatively straightforward profiling modules built in. Likewise, it can easily be called from Python ( PyJulia) to speed up otherwise cumbersome operations. Furthermore, Julia can access Python modules with wrappers and C/Fortran functions without, making it a very versatile language. On the same hand, distributing and contributing to the opensource community is straightforward.
Most packages and libraries are hosted on GitHub and are relatively straightforward to install with just a couple lines of code. Installing and implementing packages from the start is nearly seamless, simply requiring the name of the package and an internet connection. Furthermore, Julia allows users to create unique variable types, allowing for maximum flexibility while maintaining efficiency. Julia has the ability to move between dynamic and static variable types. Its language is similar Python, allowing for not only list comprehension but also ‘sloppy’ dynamic variable type declarations and use. The biggest case for implementing Julia over C/C++/Fortran is its simplicity in writing and implementation. It is comparable to Cython/Numba and faster than Numpy, Python, Matlab, and R. Note that further benchmarking is required on a project-specific basis due to the speed of individual packages/libraries, but a simple case is shown in the following section. As such, Julia can handle matrices with ease while performing at speeds nearly comparable to C and Fortran. With Version 1.1.0 recently released, Julia now stands amongst the established and stable programming languages. It is parallelizable on high performance computing resources utilizing CPUs and GPUs, allowing for its use in large-scale experiments. Julia is a high-level open-source dynamic programming language built for scientific computing and data processing that is Pythonic in syntax to ensure accessibility while boosting computational efficiency. Following this, I give an overview of common Julia development environments as well as linking to additional resources. Through this post, I will give an overview of the features, pros, and cons of Julia before comparing its performance to Python. While it has taken root in finance, machine learning, life sciences, energy, optimization, and economic realms, it is certainly not common within the water programming realm. In this blog post, I am introducing Julia, a high-level open-source dynamic programming language.