[SciPy-user] Numpy/SciPy and performance optimizations
Wed Jan 7 08:23:43 CST 2009
> I have had best luck with boost::ublas. Limited to 2d though.
> blitz is very nice, but 2 problems:
> suffers from lots of old cruft from supporting ancient c++ compilers
> Poorly maintained - future uncertain IMO.
For me the biggest problem with blitz++ is, that it is very unintuitive
and slow to write blas-like statements.
> MTL is moving extremely slowly.
> One very active project is eigen. I haven't used it myself.
Thanks for the hint to eigen (http://eigen.tuxfamily.org/), I did not
know this library - it looks very promising (although there are no
benchmarks for sparse operations, I should try that!).
Do you also know if this library is well maintained ?
I am now using a very nice c++ lib (flens:
http://flens.sourceforge.net/), but with a very unforeseeable future
(and many dependencies, hard to build) ...
However, what I mainly wanted to ask was, how you use boost::ublas or
eigen with numpy/scipy - how both systems are combined ?
For me ATM the optimal way would be to use e.g. eigen in weave like now
blitz++ is used in weave ...
More information about the SciPy-user