A Monday 28 June 2010 10:22:31 Pauli Virtanen escrigué: > ma, 2010-06-28 kello 09:48 +0200, Francesc Alted kirjoitti: > [clip] > > > But again, the nice thing would be to implement such a special functions > > in terms of numexpr expressions so that the evaluation itself can be > > faster. Admittedly, that would take a bit more time. > > Quite often, you need to evaluate a series or continued fraction > expansion to get the value of a special function at some point, limiting > the number of terms by stopping when a certain convergence criterion is > satisfied. Also, which series to sum typically depends on the point > where things are evaluated. These things don't vectorize very nicely. If > I understand correctly, numexpr bytecode interpreter does not support > conditionals and loops like this at the moment?
No, it does not support loops. And conditionals are supported only via vectorized conditionals (i.e. the `where()` opcode). > The special function implementations are typically written in bare > C/Fortran. Do you think numexpr could give speedups there? As I see it, > speedups (at least with MKL) could come from using faster > implementations of sin/cos/exp etc. basic functions. Using SIMD to > maximum effect would then require an amount of cleverness in re-writing > the evaluation algorithms, which sounds like a major amount of work. Okay. I thought that special functions were more 'vectorizable', or that it could be expressed in terms of more basic functions (sin/cos/exp). But if this is not the case, then it is quite clear that the best solution would be your first suggestion (i.e. implement user-provide ufunc evaluation). And definitely, implementing special functions in terms of SIMD would really be a *major* effort, and only doable by very specialized people ;-) -- Francesc Alted _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
