Fortran vs. Java vs. NumPy vs. Scilab Comparison Chart

Fortran has been designed from the ground up for computationally intensive applications in science and engineering. Mature and battle-tested compilers and libraries allow you to write code that runs close to the metal, fast. Fortran is statically and strongly typed, which allows the compiler to catch many programming errors early on for you. This also allows the compiler to generate efficient binary code. Fortran is a relatively small language that is surprisingly easy to learn and use. Expressing most mathematical and arithmetic operations over large arrays is as simple as writing them as equations on a whiteboard. Fortran is a natively parallel programming language with intuitive array-like syntax to communicate data between CPUs. You can run almost the same code on a single CPU, on a shared-memory multicore system, or on a distributed-memory HPC or cloud-based system.

Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.