Given a 2D matrix A as input, how to compute its covariance matrix? For example, in Matlab, I use cov(A) to do the computation.
you can do that by using Summary Statistical Functions. Vslsssnewtask(&task, p, n, xstorage, x, w, indices) where P -- Dimension of the task, number of variables
There are many examples show how to do that. You can find these examples into manual or into <mkl_root>\examples\vsls\source\"
More specifically you should be interested in vslsbasicstats.c/vsldbasicstats.c for C or vslsbasicstats.f/vsldbasicstats.f for Fortran examples.
At the same time those examples show calculation for all basic stats including min/max, moments, skewness, etc.
In order to calculate covariance only you can skip several steps there, the rest will be: vslsSSNewTask() + vslsSSEditCovCor() + vslsSSCompute() + vslSSDeleteTask().
You can also look here: