Python at C-Speed

How can the C and Python programming languages be used, together, to form more efficient software? Optimizing Python run time performance can be achieved with the following methods;

• Cython

• Extension Programming

• Numba

• NumPy

Currently, comparisons between pure Python, Numba, and NumPy have been implemented in Source Code/comparison_plotter.py

Running the Demonstration

Dependencies

To run comparison_plotter.py, a few dependencies must first be installed.

The version number may or may not affect the results of the program. They are included for the reproducibility of experimental results.

• Python ver. 3.8.5
• matplotlib ver. 3.3.3
• numba ver. 0.51.2
• numpy ver. 1.17.4

The Program

To run the Python program, first navigate to this project's root folder.

Next, run the following command to start the program.

python3 Source\ Code/comparison_plotter.py

Alternatively, from the Source Code folder, the following command will start the program.

python3 comparison_plotter.py

Results

Upon completion of all processes, MatPlotLib will generate and display a graph for each process's run time. Within the MatPlotLib window, the graph can be zoomed in and moved for further analysis.