A storage-efficient WY representation for products of householder transformations
SIAM Journal on Scientific and Statistical Computing
LAPACK's user's guide
Matrix computations (3rd ed.)
Very large electronic structure calculations using an out-of-core filter-diagonalization method
Journal of Computational Physics
Performance of a new scheme for bidiagonal singular value decomposition of large scale
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Applying recursion to serial and parallel QR factorization leads to better performance
IBM Journal of Research and Development
Gradually reconfiguring virtual network topologies based on estimated traffic matrices
IEEE/ACM Transactions on Networking (TON)
A divide-and-conquer approach for solving singular value decomposition on a heterogeneous system
Proceedings of the ACM International Conference on Computing Frontiers
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We propose an approach to speed up the singular value decomposition (SVD) of very large rectangular matrices using the CSX600 floating point coprocessor. The CSX600-based acceleration board we use offers 50GFLOPS of sustained performance, which is many times greater than that provided by standard microprocessors. However, this performance can be achieved only when a vendor-supplied matrix-matrix multiplication routine is used and the matrix size is sufficiently large. In this paper, we optimize two of the major components of rectangular SVD, namely, QR decomposition of the input matrix and back-transformation of the left singular vectors by matrix Q, so that large-size matrix multiplications can be used efficiently. In addition, we use the Integrable SVD algorithm to compute the SVD of an intermediate bidiagonal matrix. This helps to further speed up the computation and reduce the memory requirements. As a result, we achieved up to 3.5 times speedup over the Intel Math Kernel Library running on an 3.2GHz Xeon processor when computing the SVD of a 100,000 × 4000 matrix.