I think the argument nworkers = -1 to scipy.fft.fft2 and scipy.fft.ifft2 is in the wrong places in the notebook.
Le lun. 11 mars 2024, à 21 h 25, via NumPy-Discussion < numpy-discussion@python.org> a écrit : > Good afternoon, Ralf. > > We have done some of the measurements you recommended, for your > convenience we have created a separate folder with notebooks where we > measured memory usage and performance of our interpretation against Scipy. > Separately you can run the tests on your hardware and separately measure > memory. I've left the link below. > > https://github.com/2D-FFT-Project/2d-fft/tree/main/notebooks > > We measured efficiency for 4 versions - with multithreading and data type > conversion. According to the results of the tests, our algorithm has the > greatest lead in the case with multithreading and without data type > conversion - 75%, the worst performance without multithreading and with > data type conversion - 14%. In terms of memory usage we beat NumPy and > Scipy by 2 times in all cases, I think this is a solid achievement at this > point. > > I can generalise that our mathematical approach still has a serious > advantage, nevertheless we lose always to Scipy in inverse operation case, > we haven't figured out the reasons yet, we are discussing it at the moment, > but we will fix it. > > It is important to note that at this stage our algorithm shows the above > perfomance on matrices of size powers of two. > This is a specificity of the mathematical butterfly formula. We are > investigating ways to remove this limitation, we already assessed the > effect of element imputation and column dropping, the result is not > accurate enough. Otherwise, we can suggest putting our version to work only > in cases of the mentioned matrices, it'll still be an upgrade for NumPy. > > At this point I can say that we are willing to work and improve the > existing version within our skills, knowledge and available resources. We > still live with the idea of adding our interpretation or idea to the > existing NumPy package, as in theoretical perspective within the memory > usage and efficiency, it can give a serious advantage on other projects > built on NumPy. > > Thank you for your time, we will continue our work and look forward to > your review. > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: george.tro...@gmail.com >
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