Pyccel: a Python-to-X Transpiler for Scientific High-Performance Computing.

The Python programming language has gained significant popularity in scientific computing and data science, mainly because it is easy to learn and provides many scientific libraries, including parallel ones. While these libraries are very fast, they are usually written in compiled languages such as Fortran and C/C++. User code written in pure Python is usually much slower; because Python is a dynamically typed language which introduces overhead in many basic operations. Due to this limitation, one often needs to rewrite the computational parts of their Python code in a statically typed language to take full advantage of optimization and acceleration techniques. This expensive process happens naturally during the transition from a prototype to a production code, which is the principal bottleneck in scientific computing. We believe that such a bottleneck can be resolved, or at least drastically reduced, through the use of automatic code generation tools.