Fortran vs. Java vs. CUDA vs. NumPy 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.

CUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers program in popular languages such as C, C++, Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords. The CUDA Toolkit from NVIDIA provides everything you need to develop GPU-accelerated applications. The CUDA Toolkit includes GPU-accelerated libraries, a compiler, development tools and the CUDA runtime.