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NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs.

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Title: NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs.
Authors: Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.
Source: Journal of Supercomputing; May2023, Vol. 79 Issue 8, p8890-8911, 22p
Subject Terms: PYTHON programming language, FORTRAN, C++
Abstract: 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. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract: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. [ABSTRACT FROM AUTHOR]
ISSN:09208542
DOI:10.1007/s11227-022-04932-3