Julia was designed from the start for scientific and numerical computation.
Thus it’s no surprise that Julia has many features advantageous for such use cases:
- Julia is fast. Julia’s JIT compilation and type declarations mean it can routinely beat “pure,” unoptimized Python by orders of magnitude. Python can be made faster by way of external libraries, third-party JIT compilers (PyPy), and optimizations with tools like Cython, but Julia is designed to be faster right out of the gate.
- Julia has a math-friendly syntax. A major target audience for Julia is users of scientific computing languages and environments like Matlab, R, Mathematica, and Octave. Julia’s syntax for math operations looks more like the way math formulas are written outside of the computing world, making it easier for non-programmers to pick up on.
- Julia has automatic memory management. Like Python, Julia doesn’t burden the user with the details of allocating and freeing memory, and it provides some measure of manual control over garbage collection. The idea is that if you switch to Julia, you don’t lose one of Python’s common conveniences.
- Julia offers superior parallelism. Math and scientific computing thrive when you can make use of the full resources available on a given machine, especially multiple cores. Both Python and Julia can run operations in parallel. However, Python’s methods for parallelizing operations often require data to be serialized and deserialized between threads or nodes, while Julia’s parallelization is more refined. Further, Julia’s parallelization syntax is less top-heavy than Python’s, lowering the threshold to its use.
- Julia is developing its own native machine learning libraries. Flux is a machine learning library for Julia that has many existing model patterns for common use cases. Since it’s written entirely in Julia, it can be modified as needed by the user, and it uses Julia’s native just-in-time compilation to optimize projects from inside out.