In general, any time you deal with floating point numbers having different magnitudes, you risk pushing some low precision bits out of the result. Simply changing the sequence of calculations such as a literal polynomial evaluation versus Horner's method can obtain different results. Take a course in Numerical Analysis to learn more.
[1] https://en.m.wikipedia.org/wiki/Horner%27s_method [2] https://en.m.wikipedia.org/wiki/Numerical_analysis On May 13, 2020 11:57:09 AM PDT, Rasmus Liland <j...@posteo.no> wrote: >On 2020-05-13 11:44 -0700, Jeff Newmiller wrote: >> Depending on reproducibility in the least >> significant bits of floating point >> calculations is a bad practice. Just >> because you decide based on this one >> example that one implementation of BLAS is >> better than another does not mean that will >> be true for all specific examples. IMO you >> are drawing conclusions on data that is >> effectively random and should change your >> definition of "sufficient to the task". > >Dear Jeff, > >Right, so I really would have wanted OpenBLAS >to be as reproducible as regular BLAS in this >one random example, but my hands remains tied >on this since I do not know anything about >BLAS ... > >More interestingly, could you dream up any >idea as to what might cause this difference? > >Best, >Rasmus -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
