Performance Tips

import sasktran2 as sk

config = sk.Config()

Multi-Threading Performance

By default SASKTRAN2 runs on a single thread. To change this behaviour you can set

config.num_threads = 8

which will let the model run on 8 threads. For maximum performance it is reccomended to set config.num_threads to the number of physical cores on your machine, which may be less than the number of logical cores.

SASKTRAN2 is most efficiently multi-threaded over the wavelength dimension, which works very well when the number of wavelengths is high (a factor of at least 2 larger than the number of threads). When the number of wavelengths is small, it may be better to multi-thread over other parts of the code instead. This behaviour can be changed by setting

config.threading_model = sk.ThreadingModel.Source

which will attempt to multi-thread the calculation over the source function calculation. This threading is usually significantly less efficient than the wavelength dimension multi-threading, but has the advantage that it works well for a small number of wavelengths, and also may use significantly less RAM.

Note

The sk.ThreadingModel.Source works well for the sk.MultipleScatterSource.SuccessiveOrders source, but may not offer much improvement for the sk.MultipleScatterSource.DiscreteOrdinates source.

Installation Method

For maximum performance, we recommend installing SASKTRAN2 through the conda packages rather than the pip wheels. The conda packages are able to use accelerated LAPACK/BLAS libraries for your system rather than the bundled OpenBLAS library in the wheels. You can check what BLAS vendor you have installed by running conda list | grep libblas from inside your conda environment. You should see something like 20_osxarm64_accelerate. Which BLAS library offers the best performance is very dependent on what platform you are on as well as the specific calculation you are performing. If you want the absolute best performance we recommend trying a few different one.