NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs
Daniel Di Domenico, Gerson Geraldo H. Cavalheiro, João V. F. Lima · 2022
GPU devices are currently seen as one of the trending topics for parallel computing. Commonly, GPU applications are developed with programming tools based on compiled languages, like C/C++ and Fortran. This paper presents a performance and programming effort analysis employing the Python high-level language to implement the NAS Parallel Benchmark kernels targeting GPUs. We used Numba environment to enable CUDA support in Python, a tool that allows us to implement a GPU application 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 kernels. Furthermore, Python codes required 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. However, our Python versions demanded more operations than OpenACC implementations.