Consider following mathematical problem. Given general full rank square non symmetric matrix A of 13 000 x 13 000 size I want to find its SVD with all singular values and all right/left eigen vectors. But when I solve it with some driver SVD routine (e.g. LAPACKE_sgesdd) it takes about 2 x slower then I solve two eigen decomposition problems at a time: for A*A' (getting left eigen vectors of A) and A'*A (getting right eigen vectors of A) matrices. Is it normal behavior or I miss something and there is more proper/fast way to find SVD for given matrix type explicitly (via SVD driver routine)?
Considering the same problem, is there any way in MKL to find small subset (let it be 1 left and right eigen vectors for example) of all SVD right/left eigen vectors with the biggest singular values, saving "considerable" amount of time (at least 30%) ?