[EMAIL PROTECTED] wrote: > Robert, > the answer could not have been shorter, but I got exactly the > information I was looking for :-) > Thank you so much! > > Just a single followup question: >>> Or do I have to split each problem myself to make use of the >>> parallelism? >> Pretty much. > Does "pretty much" imply that actually *some* parts of NumPy do make > use of the parallel environment without any MPI handling from the > user? I didn't find an indication for this during my short skimming > over the NumPy documentation.
Sorry, that's really just American teenager idiom that's followed me into adulthood; it means, roughly, "Yes, this is entirely, unequivocally true," much like "Literally!" usually means "Figuratively!" No part of numpy proper takes advantage of parallelism. Eric Jones has been toying with using threads to split up some operations on large arrays between cores on an SMP system, but it is not ready to merge into the trunk. The performance numbers haven't been that good. The cost of thread locks is high. But then, only one approach has been tried; if you are interested in trying others, we'd be more than happy to help you along on the numpy-discussion mailing list. http://svn.scipy.org/svn/numpy/branches/multicore/ http://www.scipy.org/Mailing_Lists -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco -- http://mail.python.org/mailman/listinfo/python-list
