NAS Parallel Benchmark Kernels with Python: A performance and programming effort analysis focusing on GPUs

Abstract

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.