On 2017-06-15 10:15 GMT+02:00 roy <[email protected]>: > Hello Denis, > > Indeed now OT is faster using ot.Sample(sample). > > Regarding numba, it has to be pure python and not numpy for it to work > efficiently. [...] > 0.0181493670249 > 0.0181493670249 > 0.018149367024149737 > 0.018149367024149737 > Function time: [4.525451728957705, 4.541200206964277, 4.4143504980020225, > 4.56408092204947] > numba time: [4.3976798499934375, 4.876463262015022, 5.385470865992829, > 5.138608552981168] > Fast numba time: [0.6634743280010298, 0.6538278009975329, > 0.7077985780197196, 0.6579875709721819] > OT time: [0.7988348260405473, 0.7220299079781398, 0.7797102630138397, > 0.7526425909600221] > [Finished in 53.8s] > > So using numba is here again faster. Even if I use a large sample (1000) > numba is slightly faster.
Hello, Thanks for your sample code, that is interesting. Your numba implementation is the same as ours, so I guess that differences only come from wrapping (swig vs. numba), and differences should become negligible when increasing sample size. BTW OT is slightly faster on my desktop, but slightly slower on my laptop (both running Linux). Denis _______________________________________________ OpenTURNS users mailing list [email protected] http://openturns.org/mailman/listinfo/users
