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Parallel Computing

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Introduction to Data Science

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

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Abstract

In this chapter, we will introduce the parallel capabilities of IPython that, through applying a set of techniques, reduce execution time drastically. In a non-computational example, if one painter would spend T units of time painting a house, having N painters can reduce the total time to T/N units of time. As will be shown, two ways of scaling the computational units can be chosen: multicore or distributed computing. IPython hides the differences between them to the programmer; the same commands can be used in both. The ways of sending tasks to computing units will be introduced with the direct and balanced interfaces. Finally, an example with a database made up of millions of entries will show the advantages of parallelism.

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Notes

  1. 1.

    For a more detailed description please see http://ipyparallel.readthedocs.io/en/stable/intro.html. Last seen July 2016.

  2. 2.

    More information on ipcluster profiles can be found at http://ipython.readthedocs.io/en/stable/.

  3. 3.

    http://ipython.readthedocs.io/en/stable/.

  4. 4.

    Changing this behavior is beyond the scope of this chapter. You can find more details here: http://ipyparallel.readthedocs.io/en/stable/task.html#schedulers. Last seen November 2015.

  5. 5.

    http://publish.illinois.edu/dbwork/open-data/.

References

  1. M. Herlihy, N. Shavit, The art of multiprocessor programming (Morgan Kaufmann, 2008)

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  2. T.K.G.B.G. Coulouris, J. Dollimore, Distributed Systems (Pearson, 2012)

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Acknowledgements

This chapter was co-written by Francesc Dantí and Lluís Garrido.

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Authors and Affiliations

  1. Departament de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain

    Laura Igual & Santi Seguí

Authors
  1. Laura Igual
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  2. Santi Seguí
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Correspondence to Laura Igual .

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Igual, L., Seguí, S. (2017). Parallel Computing. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-50017-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-50017-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50016-4

  • Online ISBN: 978-3-319-50017-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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