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

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Τίτλος: NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs.
Συγγραφείς: Di Domenico, Daniel, Lima, João V. F., Cavalheiro, Gerson G. H.
Πηγή: Journal of Supercomputing; May2023, Vol. 79 Issue 8, p8890-8911, 22p
Θεματικοί όροι: Python programming language, FORTRAN, C++
Περίληψη: 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]
Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  – Url: https://dx.doi.org/doi:10.1007/s11227-022-04932-3
    Name: EDS - Springer Nature Journals (s7799221)
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  Data: NAS Parallel Benchmarks with Python: a performance and programming effort analysis focusing on GPUs.
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  Data: <searchLink fieldCode="AR" term="%22Di+Domenico%2C+Daniel%22">Di Domenico, Daniel</searchLink><br /><searchLink fieldCode="AR" term="%22Lima%2C+João+V%2E+F%2E%22">Lima, João V. F.</searchLink><br /><searchLink fieldCode="AR" term="%22Cavalheiro%2C+Gerson+G%2E+H%2E%22">Cavalheiro, Gerson G. H.</searchLink>
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  Data: Journal of Supercomputing; May2023, Vol. 79 Issue 8, p8890-8911, 22p
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  Data: <searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22FORTRAN%22">FORTRAN</searchLink><br /><searchLink fieldCode="DE" term="%22C%2B%2B%22">C++</searchLink>
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  Label: Abstract
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  Data: 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]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Supercomputing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2023
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