<html><head><meta http-equiv="Content-Type" content="text/html; charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><br class=""><div>Hi,</div><div><br class=""></div><div>long time ago I wrote a wrapper to to use optimised and parallelized math functions from Intels vector math library </div><a href="https://github.com/geggo/uvml" class="">geggo/uvml: Provide vectorized math function (MKL) for numpy</a><div class=""><br class=""></div><div class="">I found it useful to inject (some of) the fast methods into numpy via np.set_num_ops(), to gain more performance without changing my programs.</div><div class=""><br class=""></div><div class="">While this original project is outdated, I can imagine that a centralised way to swap the implementation of math functions is useful. Therefor I suggest to keep np.set_num_ops(), but admittedly I do not understand all the technical implications of the proposed change.</div><div class=""><br class=""></div><div class="">best</div><div class="">Gregor</div></body></html>