Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
The Python programming language has a number of advantages, such as simple and clear syntax, concise and readable code, and open source implementation with a lot of extensions available, that makes it a great tool for teaching programming to students. Unfortunately, Python, as a very high level interpreted programming language, is relatively slow, which becomes a nuisance when executing computationally intensive programs. There is, however, a number of tools aimed at speeding-up execution of programs written in Python, such as Just-in-Time compilers and automatic translators to statically compiled programming languages. In this paper a comparative evaluation of such tools is done with a focus on the attained performance boost.
Wydawca
Rocznik
Tom
Strony
33--41
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
autor
- Institute of Informatics Technology in Management, University of Szczecin, Mickiewicza 64,71-101 Szczecin
Bibliografia
- [1] Lutz M., Programming Python, O’Reilly, Sebastopol, CA, USA (2001).
- [2] Swacha J., New concepts for teaching computer programming to future Information Technology engineers, [in:] Perspective technologies and methods in MEMS design, Lviv Politechnic National University, Lviv, Ukraine (2010): 188.
- [3] McConnell S., Code complete: a practical handbook of software construction, Microsoft Press, Redmond, WA, USA (1993).
- [4] Python Programming Language - Official Website, http://www.python.org (Visited 2010-12-10).
- [5] Cuni A., High performance implementation of Python for CLI/.NET with JIT compiler generation for dynamic languages, Universita di Genova, Genoa, Italy (2010).
- [6] Behnel S., Bradshaw R., Seljebotn D. S., Cython: C-Extensions for Python, http://cython.org (Visited 2010-12-10).
- [7] Foord M. J., Muirhead Ch., IronPython in Action, Manning Publications, Greenwich, CT, USA (2009).
- [8] Pedroni S., Rappin N., Jython Essentials, O’Reilly, Sebastopol, CA, USA (2002).
- [9] Rigo A., Representation-Based Just-In-Time Specialization and the Psyco Prototype for Python, [in:] Proceedings of the ACM SIGPLAN Workshop on Partial Evaluation and Semantics-based Program Manipulation, ACM Press, Washington, DC, USA (2004): 15.
- [10] Dufour M., Shedskin - An experimental (restricted) Python-to-C++ compiler, http://code.google.com/p/shedskin (Visited 2010-12-10).
- [11] Winter C., Yasskin J., Unladen-swallow - A faster implementation of Python, http://code.google.com/p/unladen-swallow (Visited 2010-12-10).
- [12] Lattner Ch., Adve V., LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation, [in:] Proceedings of the 2004 International Symposium on Code Generation and Optimization, IEEE CS, Palo Alto, CA, USA (2004): 75.
- [13] Ewing G., Pyrex - a Language for Writing Python Extension Modules, http://www.cosc.canterbury.ac.nz/greg.ewing/python/Pyrex (Visited 2010-12-10).
- [14] Fujita S., Py2llvm translates Python syntax into LLVM IR, http://code.google.com/p/py2llvm (Visited 2010-12-10).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-02097ec1-9a28-4e76-939a-036915cd5811