Standard rank-revealing factorizations such as the singular value decomposition (SVD) and column pivoted QR factorization are challenging to implement efficiently on a gpu. A major difficulty in this regard is the ina...
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Standard rank-revealing factorizations such as the singular value decomposition (SVD) and column pivoted QR factorization are challenging to implement efficiently on a gpu. A major difficulty in this regard is the inability of standard algorithms to cast most operations in terms of the Level-3 BLAS. This article presents two alternative algorithms for computing a rank-revealing factorization of the form A = UTV*, where U and V are orthogonal and T is trapezoidal (or triangular if A is square). Both algorithms use randomized projection techniques to cast most of the flops in terms of matrix-matrix multiplication, which is exceptionally efficient on the gpu. Numerical experiments illustrate that these algorithms achieve significant acceleration over finely tuned gpu implementations of the SVD while providing low rank approximation errors close to that of the SVD.
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