Optimizing Performance

Selecting sensible propagation parameters

Performing numerical propagation is almost always the most expensive part of any model. Even the most efficient and streamlined models will experience high memory usage and slow performance when working with large arrays and many wavelengths. Understanding how the propagation method’s parameters influence accuracy and speed will allow you to identify appropriate values for your specific needs.

propagate_dft()

Parameter	Usage	Performance considerations
wave, weight	Arrays representing wavelength and the corresponding weight of each wavelength in the propagation.	Propagation time scales linearly with the number of wavelengths.
shape	Shape of output plane.	The output plane size has minimal impact on propagation performance unless it is astronomically large. shape should be set to ensure all data is adequately captured by the output plane.
prop_shape	Shape of propagation plane.	Propagation time scales quadtatically with prop_shape. prop_shape should be chosen to capture the data extent necessary to accurately model all required diffraction effects.
oversample	Number of times to oversample the output and propagation planes.	Propagation time scales quadratically with oversample. For accuracy, oversample should be selected to ensure propagations are Nyquist sampled, but there is typically no benefit in selecting larger values.

Strategies for increasing runtime performance

Perform fewer propagations by increasing wavelength sampling

Selecting an appropriate wavelength sampling depends on many factors. Any opportunity to reduce the number of propagations required will increase overall model performance. Two patterns for more coarse wavelength sampling are provided here

Use appropriate plane sampling

Planes should be sized to ensure the smallest spatial features of interest are adequately sampled. Small features (e.g. secondary mirror supports, segment gaps) should be represented by at leat two samples.