Aha, not so simple then. I don't quite get the multiple backend use case, but if it's supported then it's supported.
I didn't quite understand your suggestion. Do you mean to make down_cast() a method on Matrix et al instead of a global method? That sounds nice... Anyhow: I'm away for six weeks, starting tomorrow, and don't know how much I'll be able to communicate when away. Feel free to leave this bug (and others, don't know if I have time now to report more) unresolved. Unless you want to fix them, of course :) -- You received this bug notification because you are a member of DOLFIN Team, which is subscribed to DOLFIN. https://bugs.launchpad.net/bugs/747297 Title: Python: Cache tensor types Status in DOLFIN: New Bug description: In a matrix-multiplication heavy Python workload, I see something like 5-10% of the time being spent in get_tensor_type(). The attached patch memoizes the result, per type. Seems to work fine, but should be sanity checked (is per-type result ok?). _______________________________________________ Mailing list: https://launchpad.net/~dolfin Post to : dolfin@lists.launchpad.net Unsubscribe : https://launchpad.net/~dolfin More help : https://help.launchpad.net/ListHelp
