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The Journal of Supercomputing

, Volume 74, Issue 4, pp 1449–1460 | Cite as

Python accelerators for high-performance computing

  • Ami Marowka
Article

Abstract

Python became the preferred language for teaching in academia, and it is one of the most popular programming languages for scientific computing. This wide popularity occurs despite the weak performance of the language. This weakness is the motivation that drives the efforts devoted by the Python community to improve the performance of the language. In this article, we are following these efforts while we focus on one specific promised solution that aims to provide high-performance and performance portability for Python applications.

Keywords

Python Numba Just-in-Time compilation Performance portability 

References

  1. 1.
    Guo P (2014) Python is now the most popular introductory teaching language at top U.S. universities, July 7, 2014. http://cacm.acm.org/blogs/blog-cacm/176450-python-is-now-the-most-popular-introductory-teaching-language-at-top-us-universities/fulltext
  2. 2.
    Most Popular Coding Languages of 2016, February 2, 2016. http://blog.codeeval.com/
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Marowka A Is python suitable for teaching parallel programming?. will be publishedGoogle Scholar
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    PyPy Speed Center. http://speed.pypy.org/
  15. 15.
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
    Volkov V, Demmel J (2008) Benchmarking GPUs to tune dense linear algebra. In: Proc. (2008) ACM/IEEE Conf. on Supercomputing (SC’08), Piscataway, NJ: IEEE Press, 2008. Art. 31, pp 1–11Google Scholar
  22. 22.
    LLVM compiler project. https://en.wikipedia.org/wiki/LLVM

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Parallel Research LabJerusalemIsrael

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