on Thu Mar 26 2009, Joel Falcou <joel.falcou-AT-u-psud.fr> wrote:
Some exemples of divergence : eigen2 sum of cube of column i is r = m.cols(i).colwise().cube().sum()
NT2 sum of cube of column i is r = sum0( cube( m(_,i) ) );
Compelx indexing is also supported : Matlab : k(1,:, 1:2:10) = cos( m ); NT2 : k( 1, _, colon(1,2,10) ) = cos(m);
Mark-up settings : want an upper-traingular matrix of flaot of maximum static_size of 50x50 with dynamic allocation and want to specify that all loops involving it need to be blocked by a 3x3 pattern ?
matrix<float, settings( upper_triangular, 2D_(ofCapacity<50,50>), cache(tiling<3,3>) )> m;
etc ...
I'm not against teaming-up but not sure which one is better than the other. Moreover, I don't think eigen2 use proto as a base while NT2 performs lots of pre-optimization using proto-transforms and again that's something I don't want to lose.
A small note of encouragement: this sounds really cool, and the numbers are promising! -- Dave Abrahams BoostPro Computing http://www.boostpro.com