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

SVD Factorization for Tall-and-Fat Matrices on Parallel Architectures