  We demonstrate an implementation for an approximate rank-k SVD factorization,
combining well-known randomized projection techniques with previously known
paralel solutions in order to compute steps of the random projection based SVD
procedure. We structure the problem in a way that it reduces to fast
computation around $k \times k$ matrices computed on a single machine, greatly
easing the computability of the problem. The paper is also a tutorial on
paralel linear algebra methods using a plain architecture without burdensome
frameworks.
