NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs - Di Domenico, Daniel; Lima, João V. F.; Cavalheiro, Gerson G. H. (The Journal of supercomputing, 2023)
Compiled low-level languages, such as C/C++ and Fortran, have been employed as programming tools to implement applications to explore GPU devices. As a counterpoint to that trend, this paper presents a performance and programming effort analysis with Python, an interpreted and high-level language, which was applied to develop the kernels and applications of NAS Parallel Benchmarks targeting GPUs. We used Numba environment to enable CUDA support in Python, a tool that allows us to implement the GPU programs with pure Python code. Our experimental results showed that Python applications reached a performance similar to C++ programs employing CUDA and better than C++ using OpenACC for most NPB benchmarks. Furthermore, Python codes demanded less operations related to the GPU framework than CUDA, mainly because Python needs a lower number of statements to manage memory allocations and data transfers. Despite that, our Python implementations required more operations than OpenACC ones.