Making your code faster cython and parallel processing in the jupyter notebook
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This document discusses using Cython and parallel processing in Jupyter notebooks to make code faster. It describes using Euler's method to approximate the function y=x^2 for a million points and determine the minimum step size such that the result is within 1e-5 of the correct answer. The author aims to show how to optimize this problem using Cython and parallel processing without financial interests or being a computer scientist.
Making your code faster cython and parallel processing in the jupyter notebook
- 2. Making Your Code Faster: Cython and parallel processing in the Jupyter Notebook PyData DC 2016 Gustavo A. Patino Department of Biomedical Sciences Department of Neurology Oakland University William Beaumont School of Medicine Rochester, MI https://github.com/gapatino/Making-Your-Code-Faster-Cython-and-parallel-processing-in-the-Jupyter-Notebook
- 3. Disclaimer • No financial interests on any company or package that will be mentioned • Not a computer scientist https://github.com/gapatino/Making-Your-Code-Faster-Cython-and-parallel-processing-in-the-Jupyter-Notebook
- 4. Problem description • Using Euler’s method: y(n+1)=yn+(step_size*y’) approximate the function y=x2 for a million points • Determine the minimum step size for the result to be within 1e-5 of the correct answer at the last point evaluated • Note how a step size of 1 means we will evaluate values of x between 0 and 1000000, while a step sizeof 0.001 means that x ranges from 0 to 1000 https://github.com/gapatino/Making-Your-Code-Faster-Cython-and-parallel-processing-in-the-Jupyter-Notebook