High-Performance Parallel Computations Using Python as High-Level Language

High-performance and parallel computations have always represented a challenge in terms of code optimization and memory usage, and have typically been tackled with languages that allow a low-level management of resources, like Fortran, C and C++. Nowadays, most of the implementation effort goes into constructing the bookkeeping logic that binds together functionalities taken from standard libraries. Because of the increasing complexity of this kind of codes, it becomes more and more necessary to keep it well organized through proper software engineering practices. Indeed, in the presence of chaotic implementations, reasoning about correctness is difficult, even when limited to specific aspects like concurrency; moreover, due to the lack in flexibility of the code, making substantial changes for experimentation becomes a grand challenge. Since the bookkeeping logic only accounts for a tiny fraction of the total execution time, we believe that for such a task it can be afforded to introduce an overhead due to a high-level language. We consider Python as a preliminary candidate with the intent of improving code readability, flexibility and, in turn, the level of confidence with respect to correctness. In this study, the bookkeeping logic of SMP-MRRR, a C & Fortran highly optimized multi-core eigensolver, is ported to Python. We report here on the porting process and on the pros and cons of using a high-level language in a high-performance parallel library.