There are several features that make it a better choice over other programming languages, especially for scientific and technical computing.
Julia is designed to be a high-performance language, with a speed that is comparable to that of C or Fortran. Its just-in-time (JIT) compilation allows for fast execution times, while its type system and multiple dispatch enable efficient and specialized code generation.
Ease of use
Julia has a syntax that is similar to that of MATLAB or Python, making it easy to learn and use for those familiar with these languages. Its interactive REPL (Read-Eval-Print-Loop) allows for rapid prototyping and exploration of data, while its package manager provides easy access to a large collection of third-party packages.
It also has built-in support for parallel and distributed computing, enabling users to take advantage of multiple cores or even multiple machines to speed up computations. Its lightweight threading model makes it easy to write efficient and scalable concurrent code.
Julia has seamless interoperability with other languages such as Python, R, and C, making it easy to integrate with existing codebases and libraries.
Open source and active community
Julia is open source, with an active and growing community of developers contributing to its development and providing packages for a wide range of applications. This community-driven approach ensures that Julia continues to evolve and improve over time.
Overall, Julia’s combination of performance, ease of use, scalability, interoperability, and community support make it a compelling choice for scientific and technical computing.