1. Julia
If any language was built to be a spiritual successor to Fortran in the scientific realm, it's Julia. Released in 2012, it was designed from the ground up to solve the "two-language problem," where scientists prototype in a slow, easy language (like Python
or R) and then rewrite performance-critical parts in a fast language (like C or Fortran). Julia aims to be both easy to write and incredibly fast. Fortran programmers will immediately appreciate its syntax, which is clean, mathematical, and designed for numerical computation. It supports multiple dispatch, a powerful feature for writing generic and extensible code that works across different data types. Furthermore, Julia offers excellent interoperability, allowing you to call existing C and Fortran libraries directly, ensuring you don't have to leave battle-tested code behind. With a growing ecosystem and a passionate community, Julia is arguably the most direct modern analog to Fortran's mission.
2. Python (with NumPy)
This might seem like an odd choice. Python is an interpreted, dynamically-typed language—philosophically quite different from compiled, statically-typed Fortran. However, in the world of modern scientific computing, Python with the NumPy library is a dominant force. The reason a Fortran lover might enjoy it comes down to utility and power. The core of NumPy, the fundamental package for scientific computing in Python, is written in C and Fortran, providing blistering speed for array operations. This means you get the best of both worlds: Python's simple, readable syntax for high-level logic and the raw performance of compiled code for the heavy number-crunching. A Fortran programmer will feel right at home with NumPy's powerful n-dimensional array object and the vast library of mathematical functions that operate on them, from linear algebra to Fourier transforms.
3. Rust
If your appreciation for Fortran stems from its robustness, speed, and compile-time rigor, then Rust is a compelling modern option. Rust is a systems programming language focused on three things: performance, reliability, and safety. Its most famous feature is the ownership model, which guarantees memory safety and thread safety at compile time, eliminating entire categories of bugs that plague languages like C++. For a Fortran developer accustomed to a compiler that catches errors early, Rust's strictness will feel familiar and welcome. While not specifically designed for numerics like Fortran, Rust's performance is on par with C++ and often close to Fortran itself. The ecosystem for high-performance computing in Rust is growing, with libraries for parallel computation and numerical analysis. It's the choice for building fast, correct, and maintainable software from the ground up.
4. C++
For decades, C++ has been the primary alternative for Fortran programmers seeking more flexibility and object-oriented features without sacrificing performance. Modern C++ (C++11 and beyond) is a powerhouse for high-performance computing. While historically Fortran often had a slight edge in pure numerical optimization due to stricter aliasing rules, C++ compilers and advanced library techniques have largely closed that gap. A Fortran developer will value C++'s low-level control over memory and its ability to generate highly optimized machine code. Libraries like Blitz++ and Eigen bring Fortran-like array notation and performance to C++, allowing for expressive yet incredibly fast scientific code. It's a more complex language, but for those who need to build large, intricate systems that still run at near-metal speeds, C++ remains a top contender.
5. Chapel
Chapel is perhaps the most intriguing and direct heir to Fortran's supercomputing legacy. Developed by Cray (now part of HPE), a name synonymous with high-performance computing, Chapel is a language explicitly designed for productive parallel programming at scale. Where Fortran introduced native parallel programming features with coarrays, Chapel takes this concept to the next level with high-level abstractions for both data and task parallelism. This makes it easier to write code that scales from a multicore laptop to a massive supercomputer cluster. A Fortran veteran will recognize the focus on large-scale numerical work and locality-aware programming. Chapel aims to combine the performance of languages like Fortran with the readability of scripting languages like Python, making it a powerful tool for the next generation of computational science.
