Performance considerations

[rafmudaf]

Ideas for further performance improvements.

• reduction in number of rotor points
• not storing all the rotor points from the calculations
  • reducing calculations to only produce effective wind speed

See my python_performance repo for demos of these notes.

Numpy performance considerations

Vectorization and broadcasting are fundamental techniques for using Numpy in a performant manner. This PaperspaceBlog post taught me the foundation of this.

A major consideration when dealing with Numpy arrays is whether the array in an expression is or needs to be copied. This extra step can be time consuming and Numpy has infrastructure to avoid it when possible.

Array copies

Forcing a copy is often done accidentally. For example, the first expression below forces a copy of the array by including [:] while the second doesn't.

    array1, array2, buffer_array = SETUP()
    for _ in range(N):
        buffer_array[:] = array1 * array2 + array1 * array2

    array1, array2, buffer_array = SETUP()
    for _ in range(N):
        buffer_array = array1 * array2 + array1 * array2

The second is about 20% faster than the first.

This SO post is relevant: https://stackoverflow.com/questions/15424211/transfer-ownership-of-numpy-data

Numpy arrays are sometimes a View to the array. This sounds and acts like a pointer, but it may be something else at the low level
https://numpy.org/doc/stable/reference/generated/numpy.ndarray.view.html
https://numpy.org/doc/stable/reference/generated/numpy.ndarray.transpose.html#numpy.ndarray.transpose
https://www.w3schools.com/python/numpy/numpy_copy_vs_view.asp

Array access

Array access should be contiguous
No element-wise access is better - let Numpy do the multiplication

[rafmudaf]

Cython tutorial
https://cython.readthedocs.io/en/latest/src/tutorial/cython_tutorial.html
https://blog.paperspace.com/boosting-python-scripts-cython/