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Published in:
2017 | OriginalPaper | Chapter
11. Parallel Computing
Authors : Laura Igual, Santi Seguí
Published in: Introduction to Data Science
Publisher: Springer International Publishing
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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.