|[eigen] Matrix multiplication seems to be exceptionally slow in one specific case|
[ Thread Index | Date Index | More lists.tuxfamily.org/eigen Archives ]
I am trying to implement a whitening transform using Eigen3, but I find that one of the matrix operations is extremely slow and I can't understand why.. Below is the code:
MatrixXd M = convertToEigen(data);
MatrixXd mu = M.rowwise() - M.colwise().mean();
MatrixXd S = (mu.adjoint()*mu)/double(M.rows());
EigenSolver<MatrixXd> es(S, true);
V = V.cwiseSqrt().asDiagonal();
MatrixXd L(S.rows(), S.cols());
for(size_t d = 0; d < L.rows(); d++)
L(d, d) = 1./real(L(d, d));
MatrixXd Y(M.rows(), M.cols());
auto T = L*U.real().transpose();
for(size_t n = 0; n < M.rows(); n++)
Y.row(n) = T*(M.row(n) - mu);
Y = Y.transpose();
I clocked some of the operations and find that the T*() inside the for-loop is the bottle neck, which takes about 0.047688 seconds on a 4th generation intel i7 and about the same time as the eigenvalue decomposition, which takes 0.049989 seconds, so that can't be right. M is a 12498 x 225 matrix, so T is 225 x 225 and so is mu.
I am running Kubuntu 14.04, Eigen 3.2.0-8, which is in the repositories and all optimisation flags are turned on. Does anybody know what the problem is?
Het Radboudumc staat geregistreerd bij de Kamer van Koophandel in het handelsregister onder nummer 41055629.
|Mail converted by MHonArc 2.6.19+||http://listengine.tuxfamily.org/|