This is now track #10262 (memory leak in scalar*vector multiplication) Dima
On Nov 13, 5:10 pm, Jason Grout <jason-s...@creativetrax.com> wrote: > On 11/12/10 10:30 PM, Robert Bradshaw wrote: > > > On Fri, Nov 12, 2010 at 7:10 PM, Jason Grout > > It only has to construct an element if it can't figure out what to do > > after consulting the Parents themselves. > > Ah, okay. > > > > > > > > >> And then there's the matter you talk about; why is an element so big? > > > The example above is quite strange. No idea why it should be so big. > > (Note that these are are arbitrary precision integers, so the relative > > overhead for small ones is still quite large). > > > As for the original example, > > > sage: type(vector(RR, range(100))) > > <type 'sage.modules.free_module_element.FreeModuleElement_generic_dense'> > > > Which means that each element is a full Python RealNumber, quite a bit > > more than a double. Still doesn't explain the memory usage. For almost > > any kind of linear algebra, you're better off using RDF, or even numpy > > directly. > > RDF also seems to have a big problem: > > sage: v=vector(RDF, range(10000)) > sage: get_memory_usage() > 206.84765625 > sage: w=v.parent().an_element() > sage: get_memory_usage() > 983.60546875 > > This is very strange, as RDF vectors are literally light wrappers around > numpy arrays. I wonder how much Sage library code has to be loaded to > do this conversion? > > Note that an_element() is cached in the parent. Still, that seems odd > that it goes up by ~780MB. > > Jason -- To post to this group, send an email to sage-devel@googlegroups.com To unsubscribe from this group, send an email to sage-devel+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/sage-devel URL: http://www.sagemath.org
