Performance improvement attempt #1
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…l loops in parallel loops to avoid CPU overload.
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Congratulations on the article, code and work, incredible method, I'm using it!
I'm not a computer expert, but I saw the opportunity to contribute and speed up calculations. In simple tests, images in examples, I verified a performance gain.
The changes were to the dissimilarity and iteration_no_verbose calculations (in force.execute); name updated to dissimilarity_no_verbose and iteration_no_verbose, respectively, as they don't use tqdm.
Changes: (a) Critical loops have been included in numba functions;
(b) numba cache has been activated to save compilation time;
(c) parallel loops (prange) inside other parallel loops have been removed, replaced by range, avoiding parallelization overload, making parallel the highest demand only.
Previous comments contain test prints comparing the efficiency of the functions.
The functions have not been changed, just reorganized.
I'm happy to help if this has helped. Thank you for your attention.