On parallel software engineering education using python
Python is gaining popularity in academia as the preferred language to teach novices serial programming. The syntax of Python is clean, easy, and simple to understand. At the same time, it is a high-level programming language that supports multi programming paradigms such as imperative, functional, a...
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| Vydáno v: | Education and information technologies Ročník 23; číslo 1; s. 357 - 372 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.01.2018
Springer Springer Nature B.V |
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| ISSN: | 1360-2357, 1573-7608 |
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| Abstract | Python is gaining popularity in academia as the preferred language to teach novices serial programming. The syntax of Python is clean, easy, and simple to understand. At the same time, it is a high-level programming language that supports multi programming paradigms such as imperative, functional, and object-oriented. Therefore, by default, it is almost obvious to believe that Python is also the appropriate language for teaching parallel programming paradigms. This paper presents an in-depth study that examines to what extent Python language is suitable for teaching parallel programming to inexperienced students. The findings show that Python has stumbling blocks that prevent it from preserving its advantages when shifting from serial programming to parallel programming. Therefore, choosing Python as the first language for teaching parallel programming calls for strong justifications, especially when better solutions exist in the community. |
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| AbstractList | Python is gaining popularity in academia as the preferred language to teach novices serial programming. The syntax of Python is clean, easy, and simple to understand. At the same time, it is a high-level programming language that supports multi programming paradigms such as imperative, functional, and object-oriented. Therefore, by default, it is almost obvious to believe that Python is also the appropriate language for teaching parallel programming paradigms. This paper presents an in-depth study that examines to what extent Python language is suitable for teaching parallel programming to inexperienced students. The findings show that Python has stumbling blocks that prevent it from preserving its advantages when shifting from serial programming to parallel programming. Therefore, choosing Python as the first language for teaching parallel programming calls for strong justifications, especially when better solutions exist in the community. |
| Audience | Academic |
| Author | Marowka, Ami |
| Author_xml | – sequence: 1 givenname: Ami surname: Marowka fullname: Marowka, Ami email: amimar2@yahoo.com organization: Parallel Research Lab |
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| Cites_doi | 10.1109/MC.2011.15 10.1109/MC.2011.133 10.59350/dg21g-mpr65 10.1016/B978-0-12-803899-4.00009-2 10.1109/MC.2008.494 |
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| References_xml | – reference: TIOBE Index. http://www.tiobe.com/tiobe-index/. – reference: Guo, P. (2014). Python is now the most popular introductory teaching language at top U.S, Universities. http://cacm.acm.org/blogs/blog-cacm/176450-python-is-now-the-most-popular-introductory-teaching-language-at-top-us-universities/fulltexthttp://cacm.acm.org/blogs/blog-cacm/176450-python-is-now-the-most-popular-introductory-teaching-language-at-top-us-universities/fulltext. – reference: pyCUDA. https://mathema.tician.de/software/pycuda/. – reference: Numba. http://numba.pydata.org/. – reference: Lanaro, G. (2013). Python high performance programming packt. – reference: CUDA. http://www.nvidia.com/object/cuda_home_new.html. – reference: EsmaeilzadehHBlemESt. AmantRSankaralingamKBurgerDCDark silicon and the end of multicore scalingProceeding of 38th international symposium on computer architecture (ISCA)2011365376 – reference: Most Popular Coding Languages of 2016 (2016). http://blog.codeeval.com/. – reference: Anaconda Python. https://www.continuum.io/. – reference: PyOpenCL. https://mathema.tician.de/software/pyopencl/. – reference: Bogaerts, S., & Stough, J. Parallelism in python for novices, chapter 3 in Prasad, Gupta, Rosenberg, Sussman, and Weems. Topics in parallel and distributed computing: Introducing concurrency in undergraduate courses, 1st edition, Morgan Kaufmann, ISBN: 9780128038994, pages: 360. – reference: Intel Inspector. https://software.intel.com/en-us/intel-inspector-xe. – reference: IEEE Spectrum ranking. http://spectrum.ieee.org/computing/software/the-2016-top-programming-languages. – reference: Hinsen, K. Teaching parallel computing in Python. https://khinsen.wordpress.com/2012/02/06/teaching-parallel-computing-in-python/. – reference: The Jython Project. http://www.jython.org/. – reference: PyPy. http://pypy.org/. – reference: Python mpi4py package. https://pypi.python.org/pypi/mpi4py. – reference: ResearchGate. https://www.researchgate.net/post/Which_is_the_best_parallel_programming_language_for_initiating_undergraduate_students_in_the_world_of_multicore_parallel_computing. – reference: Python’s Hardest Problem. http://jeffknupp.com/blog/2012/03/31/pythons-hardest-problem/. – reference: Cormen, T.H. (2015). Parallel computing in a python-based computer science course. In Prasad, Gupta, Rosenberg, Sussman, & Weems (Eds.), Topics in parallel and distributed computing: introducing concurrency in undergraduate courses, (P. 360). Morgan Kaufmann. 1st edn. ISBN:9780128038994. – reference: Coursera. https://www.coursera.org/. – reference: OpenACC. http://www.openacc.org/. – reference: Yappi. https://code.google.com/archive/p/yappi/. – reference: Quora. Why does Python get so much hate? November 2014. https://www.quora.com/Why-does-Python-get-so-much-hatehttps://www.quora.com/Why-does-Python-get-so-much-hate. – reference: PyVmMonitor. http://www.pyvmmonitor.com/. – reference: Python. https://www.python.org/. – reference: Parallel Processing in Python. https://wiki.python.org/moin/ParallelProcessing. – reference: POSIX Treads. https://computing.llnl.gov/tutorials/pthreads/. – reference: Intel Trace Analyzer and Collector. https://software.intel.com/en-us/intel-trace-analyzer. – reference: OpenMP. http://openmp.org/wp/. – reference: Lee, E.A. (2006). The problem with threads, electrical engineering and computer sciences, University of California, Berkeley. Technical Report No. UCB/EECS-2006-1. – reference: Python Code profiling. https://docs.python.org/2/library/profile.html. – reference: Intel VTune Amplifier. https://software.intel.com/en-us/intel-vtune-amplifier-xe. – reference: Microsoft Task Parallel Library (TPL). https://msdn.microsoft.com/enus/library/dd460717(v=vs.110).aspx. – reference: IronPython. https://ironpython.codeplex.com/. – reference: Python threading module. https://docs.python.org/3.3/library/threading.html. – reference: MPICH. https://www.mpich.org/. – reference: OpenCL. https://www.khronos.org/opencl/. – reference: MarowkaABack to thin-core massively parallel processorsIEEE Computer20114412495410.1109/MC.2011.133 – reference: WooDHLeeHSExtending Amdahl’s law for energy-efficient computing in the Many-Core EraIEEE Computer20084112243110.1109/MC.2008.494 – reference: FullerSHMillettLIComputing performance: game over or next level?IEEE Computer2011441313810.1109/MC.2011.15 – reference: Palach, J. (2014). Parallel programming with python packt. – reference: Cython. http://cython.org/. – reference: Python Multiprocessing module. https://docs.python.org/2/library/multiprocessing.html. – reference: Zaccone, G. (2015). Python parallel programming cookbook packt. – reference: Open MPI. https://www.open-mpi.org/. – reference: Anaconda Accelerate. https://docs.continuum.io/accelerate/. – ident: 9607_CR27 – ident: 9607_CR35 – ident: 9607_CR29 – ident: 9607_CR5 – ident: 9607_CR25 – ident: 9607_CR23 – ident: 9607_CR7 – ident: 9607_CR3 – ident: 9607_CR1 – ident: 9607_CR18 – ident: 9607_CR43 – volume: 44 start-page: 31 issue: 1 year: 2011 ident: 9607_CR33 publication-title: IEEE Computer doi: 10.1109/MC.2011.15 – ident: 9607_CR20 – ident: 9607_CR39 – ident: 9607_CR16 – ident: 9607_CR41 – ident: 9607_CR47 – ident: 9607_CR37 – ident: 9607_CR45 – ident: 9607_CR14 – ident: 9607_CR12 – ident: 9607_CR10 – ident: 9607_CR30 – ident: 9607_CR26 – ident: 9607_CR28 – ident: 9607_CR36 – ident: 9607_CR24 – ident: 9607_CR4 – ident: 9607_CR6 – ident: 9607_CR22 – ident: 9607_CR2 – ident: 9607_CR17 – ident: 9607_CR42 – ident: 9607_CR9 – volume: 44 start-page: 49 issue: 12 year: 2011 ident: 9607_CR34 publication-title: IEEE Computer doi: 10.1109/MC.2011.133 – ident: 9607_CR40 doi: 10.59350/dg21g-mpr65 – ident: 9607_CR21 – ident: 9607_CR15 – ident: 9607_CR8 doi: 10.1016/B978-0-12-803899-4.00009-2 – ident: 9607_CR19 – ident: 9607_CR38 – ident: 9607_CR44 – ident: 9607_CR46 – volume: 41 start-page: 24 issue: 12 year: 2008 ident: 9607_CR32 publication-title: IEEE Computer doi: 10.1109/MC.2008.494 – ident: 9607_CR11 – ident: 9607_CR13 – start-page: 365 volume-title: Proceeding of 38th international symposium on computer architecture (ISCA) year: 2011 ident: 9607_CR31 |
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| Title | On parallel software engineering education using python |
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