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IPython Interactive Computing and Visualization Cookbook

You're reading from   IPython Interactive Computing and Visualization Cookbook Over 100 hands-on recipes to sharpen your skills in high-performance numerical computing and data science in the Jupyter Notebook

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785888632
Length 548 pages
Edition 2nd Edition
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Author (1):
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Cyrille Rossant Cyrille Rossant
Author Profile Icon Cyrille Rossant
Cyrille Rossant
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Table of Contents (17) Chapters Close

Preface 1. A Tour of Interactive Computing with Jupyter and IPython FREE CHAPTER 2. Best Practices in Interactive Computing 3. Mastering the Jupyter Notebook 4. Profiling and Optimization 5. High-Performance Computing 6. Data Visualization 7. Statistical Data Analysis 8. Machine Learning 9. Numerical Optimization 10. Signal Processing 11. Image and Audio Processing 12. Deterministic Dynamical Systems 13. Stochastic Dynamical Systems 14. Graphs, Geometry, and Geographic Information Systems 15. Symbolic and Numerical Mathematics Index

Releasing the GIL to take advantage of multi-core processors with Cython and OpenMP


As we have seen in this chapter's introduction, CPython's GIL prevents pure Python code from taking advantage of multi-core processors. With Cython, we have a way to release the GIL temporarily in a portion of the code in order to enable multi-core computing. This is done with OpenMP, a multiprocessing API that is supported by most C compilers.

In this recipe, we will see how to parallelize the previous recipe's code on multiple cores.

Getting ready

To enable OpenMP in Cython, you just need to specify some options to the compiler. There is nothing special to install on your computer besides a good C compiler. See the instructions in this chapter's introduction for more details.

The code in this recipe has been written for GCC on Ubuntu. It can be adapted to other systems with minor changes to the %%cython options.

How to do it...

Our simple ray tracing engine implementation is embarrassingly parallel (see https...

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