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Efficient parallel nonnegative least squares on the GPU. submitted to the
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| Venue: | SIAM Journal on Scientific Computing |
| Citations: | 4 - 0 self |
BibTeX
@ARTICLE{Luo_efficientparallel,
author = {Yuancheng Luo and Ramani Duraiswami},
title = {Efficient parallel nonnegative least squares on the GPU. submitted to the},
journal = {SIAM Journal on Scientific Computing},
year = {}
}
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Abstract
Abstract. We parallelize a version of the active-set iterative algorithm derived from the original works of Lawson and Hanson (1974) on multi-core architectures. This algorithm requires the solution of an unconstrained least squares problem in every step of the iteration for a matrix composed of the passive columns of the original system matrix. To achieve improved performance, we use parallelizable procedures to efficiently update and downdate the QR factorization of the matrix at each iteration, to account for inserted and removed columns. We use a reordering strategy of the columns in the decomposition to reduce computation and memory access costs. We consider graphics processing units (GPUs) as a new mode for efficient parallel computations and compare our implementations to that of multi-core CPUs. Both synthetic and non-synthetic data are used in the experiments. Key words. Non-negative least squares, active-set, QR updating, parallelism, multi-core, GPU, deconvolution
Keyphrases
multi-core architecture active-set iterative algorithm non-negative least square original work original system matrix efficient parallel computation memory access cost multi-core cpu non-synthetic data passive column new mode unconstrained least square problem qr updating key word reordering strategy improved performance qr factorization parallelizable procedure
