What are the standard methods of computing the rank-k truncated SVD of large dense matrices? My literature search yields results only for large *sparse* matrices.

I assume for k small that you use a Krylov subspace method (this is what Matlab's svds does). But (empirically) how large can k get before these methods become impractical, and then what should one resort to?